New Delhi, June 24 (IANS) Amazon Web Services on Thursday expanded its partnership with Salesforce making it easy for users to use the full set of Salesforce and AWS capabilities together to quickly build and deploy powerful new business applications that accelerate digital transformation.

The partnership will drive customer value through new and enhanced unification between AWS and Salesforce products.

“For more than five years, our customers have benefitted from a tight relationship between AWS and Salesforce,” Andy Jassy, CEO of Amazon Web Services, said in a statement.

“Now, we are taking the partnership to the next level by integrating our offerings so developers using both AWS and Salesforce can build unified applications much faster and simpler than ever before,” Jassy added.

Developers will now be able to build and launch custom applications that extend the power of both platforms by connecting Salesforce data and workflows natively into their solutions running on AWS and make it easy for Salesforce developers to integrate AWS data and workflows into their Salesforce applications.

Salesforce will also embed AWS services for voice, video, artificial intelligence (AI) and machine learning (ML) directly in new applications for sales, service, and industry vertical use cases.

With these new offerings, customers can purchase out-of-the-box solutions directly from Salesforce and then consume the integrated AWS services on a pay-as-you-go basis.

“With this partnership, we are significantly simplifying developers’ lives and empowering them to develop applications however they want, from wherever they want globally, at any scale,” Jassy said.

Previously, significant custom integration code was often required for customers that wanted to combine AWS and Salesforce capabilities for their applications.

These new offerings will enable customers to use AWS and Salesforce services together seamlessly through new, unified platforms.

The companies have also collaborated on new low-code tools to help customers innovate and build applications that connect data in real-time between Salesforce applications and AWS services.

Customers can now innovate faster with pre-built applications that combine AWS and Salesforce Customer 360, making it easier for organisations to seamlessly deploy AWS’s voice, video and AI services natively with Salesforce business applications.

For businesses in the early stage of their ventures, proving that they have the expertise and technical know-how when it comes to cloud technology can be difficult. An acceleration toward digitization due to COVID-19 and an increasingly crowded cloud market, dominated by the big players, means that simply shouting about your skills in cloud engineering and DevOps isn’t enough to get noticed.

Joining forces with expert brands and entering into endorsement partnerships can propel your business by loading it with seasoned expertise. Accreditation, if you can achieve it, can give you a stamp of approval and reassurance to prospects that you have the relevant knowledge to support their digital transformation efforts. Collaborating with industry leaders on skills accreditation programs is a great way to demonstrate your capabilities and gives clear goalposts to work to when it comes to building and learning.

So, how do businesses achieve this status, and what are the long-term benefits of the process?

What does it take to become a trusted AWS Partner?
At AND Digital, we have achieved our accreditation as an AWS DevOps Competency Partner. A lot of hard work and investment has gone into building the technical skills required to reach such a high standard – logging and evidencing every action your team takes is no mean feat. From beginning to end, the course took around 6 months. The process begins with researching and selecting which specific AWS Competency your team is interested in achieving. It’s important to understand which AWS Competency your business is most suited to – so doing your homework is key. From there, there’s a lot of work to evidence, certificates to achieve, and reviews to undergo. Becoming an accredited AWS Partner brings a multitude of benefits to your brand, so is well worth the investment.

Top tips for the accreditation process:
Create detailed accounts of your work: To become a certified AWS DevOps Competency Partner, businesses need to evidence four case study examples, two public and two private. As such, we found it useful to bring in a tech writer who consolidated all our notes and observations throughout. We began with a technical exploration from the team to understand the best case studies to put forward, our work had to be clearly evidenced with strong examples and the documents to back it up which took a considerable amount of time to pull together. Maintaining clear and detailed accounts of previous work can prevent any delays. This information is firstly analyzed by AWS to check that it met the specific requirements before being passed on to be examined by a team of external auditors, so every detail counts when it comes to showcasing your work.

Prepare the marketing team: As part of the process, businesses are required to have certain marketing collateral in place across their website to aid with lead generation For the AWS DevOps Competency designation, we needed to evidence three technical blogs – businesses applying for the certification need to ensure that the content best reflects their expertise in the given AWS Competency. Once the accreditation is finalized, the collaboration should be displayed on the site. Even if a business is tech agnostic, demonstrating your skills in a particular type of cloud engineering is highly valuable to your brand and AWS is a respected cloud platform to use.

Advance technical training in the workplace: Having members of the team qualified in cloud engineering is another requirement of accreditation. For DevOps, we needed eight people in the team to have achieved their associate certifications and four pros. To fulfill this requirement and ensure that we’re on top of our game when it comes to cloud training, we’ve invested in third-party tools, provided access to LinkedIn Learning programs, and give our people additional time off from client-facing work to prepare for their exams. Building and learning in tandem is the fabric of our culture, and a similar approach would benefit any company looking to achieve cert status.

The benefits
Being able to showcase a close association with a major player such as AWS opens up a number of doors for smaller businesses. Operating within this kind of business ecosystem provides the opportunity to demonstrate a business’ DevOps and technical skills to an enormous global audience which can drive new business conversations and drive quality leads. Confirmation that your business is a trusted partner for a major cloud organization will ensure that prospects have confidence in your capabilities and you’re taken seriously as a technical player in the market. Building confidence within your teams and supporting them in their learning is an investment that will pay off in dividends.

At AND, we can automate platforms in as little as one hour, whilst other organizations can take almost eight weeks to complete the process – it’s something we’re really proud of, and working alongside AWS as an accredited AWS DevOps Competency Partner gives us a springboard to talk about our work and engage with other players in the market. The accreditation is a fantastic talking point and definitely gets you noticed in the cloud community – plus, you will be taken more seriously as a partner.

Investment in cloud tech as a key for the future
As businesses everywhere begin to operate in an increasingly digitized environment, technical skills and cloud expertise are a necessary investment for any business. Working towards coveted industry accreditations whilst prioritizing skills training, in the long run, is imperative. It’s a competitive, buoyant market for cloud engineers and DevOps at the moment; opportunities are at their fingertips and if they aren’t being fulfilled by their current work, they’re likely to look elsewhere. Investment in training and nurturing growth in this way breeds loyalty. For a company to thrive technically, it needs to host a wide range of diverse talent and be able to retain it. Learning opportunities should always be the key focus.

The shortage in this talent means it’s critical to advance the business’s cloud expertise by pursuing recognition from major global players, providing advanced training and certification opportunities for people will ensure long-term talent retention whilst making the business a leader in cloud engineering which is pivotal for long term success.

As Amazon’s global imprint continues to expand, the e-commerce giant is celebrating the 15th anniversary of Simple Storage Service, Amazon S3, the first of its cloud service offerings. Launched back in March 2006, Amazon Web Services (AWS) has since become a model for not only other e-commerce entities but a wide range of industries that incorporate cloud services.
“We’ve been the pioneers in this space. The concept of cloud computing didn’t exist before this, and we got a real head start in building these services. Also, the close relationship with our customers — being able to get continuous feedback from our customers — have resulted in the fact that now we have more than 200 services,” Werner Vogels, Amazon’s chief technical officer, told Cheddar.
Vogels said Amazon’s maturation into cloud services was a natural progression for the company because it has been “a tech company since day one.” For client businesses, Amazon’s low-cost cloud services with no long-term commitments were an immediate draw. And, while the pandemic has slowed the production of semiconductors for cloud computing chips, Vogels said Amazon has prepared for the exact scenario, building their own chips and redundancies in their systems.
“Remember, of course, that we learned a lot from building the supply chain for AWS from the tremendous effective supply chain that Amazon.com has, and as such we’re building supply chains to 25 regions around the world that are all replicated and actually can sustain shortages in any of those supply chains,” he noted.
Also, the tech industry isn’t the only one benefitting from the cloud, according to Vogels. Many industries have been able to utilize the technology. Farmers have even found ways to use the services to manage data related to land cultivation and to create effective labor plans.
“If you travel outside of the U.S., what you meet a lot is these companies that are solving truly hard human problems,” he said. “For example, in Indonesia, there’s a company called HARA Token where smallholder rice farmers, they don’t have an identity and as such cannot get loans, and so what this small company does is give them an identity, measure their piece of land, measure their yield, and make the data available to banks.”

Amazon Web Services (AWS), an Amazon.com Inc. company, announced that Edelweiss has selected AWS as its preferred cloud provider, running the vast majority of its workloads on the world’s leading cloud to further drive its digital transformation efforts. To date, Edelweiss has migrated over 210 mission-critical applications from multiple business lines to AWS, including stock recommendations, public websites, and data models, to reduce cost, improve agility, and create innovative new services. Using the breadth and depth of AWS’s portfolio of cloud capabilities, including compute, machine learning, management and governance, serverless, and database, Edelweiss is building a digital ecosystem of applications and systems using application programming interfaces (APIs), which enable Edelweiss’s wealth, asset, credit, and insurance entities to securely exchange information and launch joint customer services. The digital ecosystem’s secure information exchange enables Edelweiss to gain greater customer insights, improve speed to market of new customer services, and help customers make better investment and financial decisions.

Each of Edelweiss’s multiple business groups previously ran their on-premises infrastructure in silos, which meant upgrading infrastructure and building new customer services was challenging and took engineers away from creating innovative new services for the company’s 1.45 million customers located across more than 100 cities in India and internationally. Running the on-premises infrastructure also meant high costs, consuming 80% of Edelweiss’s IT budget. As the company started transforming into an agile, data-driven enterprise by moving to AWS, it focused on dramatically reducing the costs associated with the undifferentiated heavy lifting of managing infrastructure. Edelweiss now spends 50% of its IT budget on driving digital innovation initiatives aimed at providing richer customer experiences at much lower costs, including a data mart which creates a unified view of the customer across Edelweiss’s business groups and an artificial intelligence (AI) stock simulation which provides predictions on stock performance to traders in real time.

With AWS, Edelweiss seamlessly scaled up its infrastructure for five days to process investments of more than US $2B during the launch of the Bharat Bond exchange Traded fund (ETF)—at 10% of the cost of a comparable on-premises system. By leveraging Amazon Elastic Compute Cloud (Amazon EC2), which provides secure, resizable compute capacity in the cloud, Edelweiss eliminated service downtime and minimized response times when customers invested into the ETF, making the Bharat Bond ETF India’s largest first-time bond ETF subscription offer launched by an asset management company. By centralizing and analyzing previously siloed customer data using AWS, Edelweiss can gain new customer insights and enable a more personalized customer experience. Using Amazon SageMaker, AWS’s service that helps data scientists and developers to build, train, and deploy machine learning models quickly, Edelweiss’s life insurance group has trained machine learning models to deliver personalized service and support to over 370,000 customers. For example, the models provide Edelweiss customer service agents with personalized insurance offerings which they can offer when engaging customers by phone or chat. Since adopting Amazon SageMaker, Edelweiss has reduced the time to launch new machine learning models from six months to only three weeks, and increased cross-sell rates by 300% and fraud detection rates by 35%.

“AWS is integral to Edelweiss’s strategy as we digitally transform from a traditional financial services organization to an agile, data driven enterprise,” said Nitin Agarwal, President and Group CIO, CTO, and Chief Digital Officer, Edelweiss. “We benefit from AWS technologies such as machine learning to create a digital ecosystem that connects our entities and enables them to co-deliver customer personalization at scale, all while maintaining the highest levels of regulatory compliance.”

“Edelweiss is using AWS to digitally connect its entities and radically change how they develop innovative services that help customers make better investment and financial decisions,” said Puneet Chandok, President, Commercial Business, India and South Asia, Amazon Internet Services Private Limited. “By selecting AWS as its preferred cloud provider, Edelweiss gains access to the broadest and deepest portfolio of cloud services to deliver new financial services faster and interact with customers and partners in a more seamless and secure way.”

Integration with Amazon Macie
Digital Guardian has announced the general availability of their integration with Amazon Macie, a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and provide additional protection for customers’ sensitive data in AWS.

As organizations manage growing volumes of data, identifying and protecting their sensitive data at scale can become increasingly complex, expensive, and time-consuming.

Amazon Macie is designed to automate the discovery of sensitive data at scale and lowers the cost of protecting data. Digital Guardian now leverages Amazon Macie to build an integration that extends enterprise data loss prevention from traditional endpoints to AWS.

For shared Digital Guardian and Amazon Macie customers, this integration provides unified sensitive data discovery and visibility and other compelling benefits, including:

The ability to correlate Amazon Macie findings with Digital Guardian Endpoint events, providing visibility into the file lifecycle.
Automated alarming and incident tracking of Amazon Macie incidents in Digital Guardian Analytics & Reporting Cloud (ARC) to support complex compliance mandates such as ITAR.
On-demand reporting and remediation actions to add an additional layer of protection for sensitive data stored in Amazon Simple Storage Service (Amazon S3) buckets.
In 2020, we announced a series of major enhancements to Amazon Macie, including new features, greater availability worldwide, and substantially reduced pricing,” said Dan Plastina, Vice President for External Security Services, Amazon Web Services, Inc.

“We are pleased Digital Guardian is now integrated with Amazon Macie, as this allows our shared customers to benefit from unified visibility of sensitive data stored in Amazon S3 buckets, on local network shares, and on endpoint devices. This visibility can then inform policies to protect their data no matter where it resides.”

“Providing a seamless integration with Amazon Macie was a top product priority at Digital Guardian,” said Mordecai Rosen, Chief Executive Officer, Digital Guardian.

“We believe federation is critical to enterprise data protection programs providing the ability to see and manage sensitive data no matter where it resides.

Amazon Macie is an effective service for identifying and securing sensitive data in Amazon S3, and with Digital Guardian’s integration we can offer our shared customers unified data visibility and protection from a single pane of glass.”

Support for Amazon WorkSpaces on Windows
Amazon WorkSpaces is a managed, secure Desktop-as-a-Service (DaaS) solution. Customers can use Amazon WorkSpaces to provision Windows desktops in just a few minutes and quickly scale to provide thousands of desktops to workers around the globe.

Digital Guardian customers are increasingly deploying DaaS to secure remote workers and with the release of version 7.6, Digital Guardian is now certified for Amazon WorkSpaces.

Achieves AWS Advanced Technology Partner Status
In October, Digital Guardian achieved AWS Advanced Technology Partner status. As an AWS Advanced Technology Partner, customers can be assured Digital Guardian has met stringent requirements including a thorough assessment of the Digital Guardian Data Protection Platform by AWS experts to ensure conformity with the security and cloud configuration best practices that they demand.

Digital Guardian DLP service is available in AWS Marketplace
With availability in AWS Marketplace, customers can easily subscribe to the cloud-delivered Digital Guardian Data Protection Platform, powered by AWS, to gain instant visibility into their sensitive data as is traverses their enterprise and cloud environment.

They can also benefit from a seamless purchase process offered in AWS Marketplace and reduce the overall burden on their procurement, legal and accounting teams.

“As the global leader in cloud customer experience and contact center solutions, securing our customers’ sensitive data is absolutely paramount,” said Steven Wolford, Chief Information Security Officer, Genesys.

“The Digital Guardian Data Protection Platform provides us with deep visibility and flexible controls, enhancing the protection of our data and IP.

Additionally, the ability to purchase the platform from AWS Marketplace enabled us to avoid lengthy contract negotiations and streamline the procurement process.”

The latest market report Global Database as a Service Market report is a detailed research and statistical analysis given by IMR Research to offer a comprehensive and an in-depth study over the market with the help of regional demand estimation forecast, drivers, challenges, opportunities, threats, opportunities, etc. The report also contains Pre-Commodity pricing volatility where our experts composes a detailed study of the market in order to draw the inference that are in the market and deduce them to estimate the forthcoming market. The primary purpose of the report is to help the client know the Database as a Service market thoroughly with every aspect that our analysts at Introspective Market Research provides such as the niche segment analysis, market potential, DROCs, and challenges.

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Global Database as a Service Market Segmented by Product Type:
On Cloud, On Premises

Global Database as a Service Market Segmented by Application:
Application A, Application B, Application C

Global Database as a Service Market Segmented by Region:
North America (U.S.; Canada; Mexico), Europe (Germany; U.K.; France; Italy; Russia; Spain etc.), Asia-Pacific (China; India; Japan; Southeast Asia etc.), South America (Brazil; Argentina etc.), Middle East & Africa (Saudi Arabia; South Africa etc.)

The Database as a Service market report is a combination of intelligence research exploring almost every component of the insights from the market. Market participants may utilize this report to find out about the competition and the competitive environment in the industry. With the help of utmost accurate data that IMR provides could be utilize to gain the monopoly in the market to build a platform for the unparalleled usage of the commodities. Our team at IMR inspects the competition in the industry for the Database as a Service market which will aid our client to meet the customers need and have a promising growth. The report is in detail with the statistics so that players can prepare themselves to gain market share. This report study provides a complete description of the advancement and the major markets. The user is provided with facts and figures related to the industry and significant factors like consumption, production, earnings CAGR and the fluctuations in market size. The report shares volume growth, market share, attractive index, value and gross margins.

Key Insights covered in This Report:
• Performance of the global Database as a Service Market in recent years and its likely growth rate in the coming years
• Key regional markets in the global Database as a Service industry
• Strategies of merchandise offerings and important players
• Various stages in the value chain of the Database as a Service market
• Key driving factors and challenges in the Database as a Service Market
• Impact of COVID-19 pandemic on the industry
• Niche and Potential segments or areas exhibiting promising expansion
• Existing key players in the industry, along with their SWOT analysis and the market size in the forecast period.

Introspective Market Research (introspectivemarketresearch.com) is a visionary research consulting firm dedicated to assist our clients grow and have a successful impact on the market. Our team at IMR is ready to assist our clients flourish their business by offering strategies to gain success and monopoly in their respective fields. We are a global market research company, specialized in using big data and advanced analytics to show the bigger picture of the market trends. We help our clients to think differently and build better tomorrow for all of us. We are a technology-driven research company, we analyse extremely large sets of data to discover deeper insights and provide conclusive consulting. We not only provide intelligence solutions, but we help our clients in how they can achieve their goals.

New Delhi: Amazon Web Services (AWS) which is the Cloud arm of Amazon has announced that by 2026, it will help 29 million people globally grow their technical skills with free cloud computing skills training.

Teresa Carlson, Vice President of Worldwide Public Sector at Amazon, said that the company will provide training opportunities through existing AWS-designed programmes, as well as develop new courses to meet a wide variety of schedules and learning goals.

“We are investing hundreds of millions of dollars to provide free cloud computing skills training to people from all walks of life and all levels of knowledge, in more than 200 countries and territories,” Carlson said in a statement during the ‘AWS: re:Invent 2020’ annual conference being held in digital mode.

The training ranges from self-paced online courses — designed to help individuals update their technical skills — to intensive upskilling programmes that can lead to new jobs in the technology industry.

“We are building out our library of more than 500 free courses, interactive labs, and virtual day-long training sessions. Individuals looking to learn about cloud technology at their own pace have two robust resources in AWS Training and Certification and AWS Educate,” Carlson informed.

In 2020, AWS Training and Certification has launched 50 new digital courses in addition to the hundreds of free courses already available and will continue adding more content and new ways to learn.

“We will continue to invest in free digital training and exam preparation courses to help people prepare for AWS Certifications, which show technical expertise working with AWS,” she said.

The plan to provide 29 million people around the world with skills training builds on the commitment the company made last year to invest $700 million to train 100,000 Amazon employees.

SEATTLE–(BUSINESS WIRE)–Today, Amazon Web Services, Inc. (AWS), company (NASDAQ: AMZN), announced that Nationwide, a Fortune 100 insurance and financial services provider, is expanding its relationship with AWS and has selected AWS as its preferred cloud provider. As part of its company-wide digital transformation, Nationwide is moving key, business critical workloads to AWS, including more than 850 business and customer-facing applications, such as Claims, Personal and Commercial Insurance Policy Systems, and their flagship website Nationwide.com. Leveraging the breadth and depth of AWS’s advanced services, including analytics, database, machine learning, security, and serverless technologies, Nationwide is delivering deeper insights to its independent agents, financial professionals, and customer service associates to provide more personalized recommendations and customer service to their millions of customers.

Nationwide’s rapid move to the cloud is accelerated by AWS Enterprise Next, a program that pairs AWS experts with customer teams to create a long-term plan and key milestones to drive a company’s ongoing digital transformation. Through this 15-month engagement program, AWS experts and partners are working with Nationwide to create an on-site, cross-functional team to modernize Nationwide’s applications, IT environment, and operating practices. AWS Enterprise Next has already helped Nationwide build more than 60 applications in the cloud, including its Nationwide Small Business Advisory platform, an online experience that uses machine learning services like Amazon Textract (a machine learning service that automatically extracts text and data from scanned documents) to pull data from scanned forms and documents to automate the underwriting process for small business owners. In addition, the platform uses AWS’s machine learning-driven personalization service, Amazon Personalize, to tailor personalized insurance policy recommendations to small business customers in minutes. This is rapidly speeding up the time to get new policies into the hands of customers, providing greater assurance to small businesses, where insurance coverage is vital.

Leveraging AWS gives Nationwide the ability to introduce new resources like enhanced mobile applications and self-service financial planning, to customers much faster, getting these new ideas into the hands of customers in days, as opposed to the months it took previously. AWS analytics is also helping to reveal new insights related to application usage patterns, business trends, and customer preferences, allowing Nationwide to empower independent agents and financial professionals to make better, more personalized recommendations to customers based on the financial and insurance resources available. Going forward, Nationwide will also use Amazon SageMaker, AWS’s service for easily building, training, and deploying machine learning models quickly at scale, to help its data scientists enhance their performance in areas such as marketing analytics and risk assessment to accelerate the company’s life insurance policy processing.

“Our customers’ needs and expectations of how their insurance partner supports them are evolving rapidly and AWS has given us the agility that we need to quickly innovate and provide new services, while delivering the security, reliability, and performance that customers have come to expect from us for more than 90 years,” said Jim Fowler, CTO at Nationwide. “AWS has helped us move to the cloud quicker than we ever imagined possible, and their unmatched portfolio of services is transforming our business. AWS gives us the ability to reach new digital audiences who can access and consume our services through a variety of channels, and empowers our associates with next-generation tools and insights to enhance our customer service.”

“Combining Nationwide’s financial expertise with AWS’s proven performance and unmatched portfolio of services is enabling them to deliver powerful new financial tools to help them stay ahead competitively and remain a leader in serving customers,” said Greg Pearson, Vice President, Worldwide Commercial Sales at AWS. “Leveraging AWS, Nationwide can innovate and deploy new solutions while they are streamlining their business operations and processes on the backend. By building and running their most strategic platforms on AWS, Nationwide is positioning itself as a leading choice for life insurance and financial services for years to come.”

Amazon Web Services

For 14 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 175 fully featured services for compute, storage, databases, networking, analytics, robotics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 77 Availability Zones (AZs) within 24 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Australia, India, Indonesia, Japan, Spain, and Switzerland. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

Amazon

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Fire tablets, Fire TV, Amazon Echo, and Alexa are some of the products and services pioneered by Amazon. For more information, visit www.amazon.com/about and follow @AmazonNews.

Nationwide

Nationwide, a Fortune 100 company based in Columbus, Ohio, is one of the largest and strongest diversified insurance and financial services organizations in the United States. Nationwide is rated A+ by both A.M. Best and Standard & Poor’s. An industry leader in driving customer-focused innovation, Nationwide provides a full range of insurance and financial services products including auto, business, homeowners, farm and life insurance; public and private sector retirement plans, annuities and mutual funds; excess & surplus, specialty and surety; pet, motorcycle and boat insurance

More than ‘just’ a low-code software company these days, Portugal-founded OutSystems will be 20-years old next year and the company now stands for an application platform designed to provide AI-assisted tools to help developers build and deploy a range of applications.

The latest from OutSystems sees it enter into a new collaboration agreement with Amazon Web Services (AWS) to deliver deeper integrations with AWS services in the OutSystems Cloud.

So in terms of who gets what here, OutSystems developers can now put enhanced AWS DevOps, data and analytics, Artificial Intelligence/Machine Learning (AI/ML) and other AWS services in the OutSystems applications they are building.

As such, OutSystems now calls itself a modern application platform company… with the obvious ‘modernity’ reference there being apps that can exist and flourish in the age of cloud.

What kind of apps could be built here?

OutSystems points to many areas, but makes special note of software that functions to deliver customer experience transformation, workplace innovation, process optimisation and application modernisation.

The joint work here will focus on evolving the OutSystems Cloud offering and work to remove the constraints of legacy software tools, and accelerate the ability to integrate advanced cloud-based services into applications.

Simplified consumption model
OutSystems and AWS will enable a simplified consumption model for OutSystems Cloud by making it available in AWS Marketplace and invest in the OutSystems Forge marketplace to (it hopes)democratise innovation by the developer community.

“Our relationship with AWS is a foundational component for delivering on our mission to give every organisation the power of innovation through software,” said Paulo Rosado, OutSystems CEO. “OutSystems Cloud enables our customers to develop their applications faster and easier, while leveraging the advantages of AWS. We will continue to support our customer and developer communities by further investing and optimising our platform.”

Doug Yeum, head of global partner organisation at Amazon Web Services, Inc.says that he is pleased that OutSystems is augmenting its cloud offering with the breadth of AWS services in areas such as developer tools, data and analytics and machine learning.

More AWS in a growing rapid app platform with low-code functions wrapped up as a part of a tighter integration should sound like good news for OutSystems developers… more of what they already like, but with Amazon-fuelled data power built-in.

They don’t have many rainforests in Portugal (apart from on Madeira), but if they did, this is (arguably) what they would sound like.

Amazon Web Services (AWS) has ‘extended its strategic relationship’ with videoconferencing provider Zoom, casting doubt on the latter’s dealings with Oracle.

Amid the plethora of announcements which have come out of re:Invent, AWS’ major annual event, this week, the news that Zoom is using AWS as its preferred cloud provider flew somewhat under the radar.

AWS made the point that Zoom had been a customer since 2011, with the companies ‘collaborating to develop new solutions for Zoom’s enterprise users, leveraging the breadth and depth of AWS to integrate Zoom services with Amazon devices and capabilities to make it easier for organisations to run hybrid office and remote work models’, in the words of the press materials.

One of the biggest tech stories around the Covid-19 pandemic was how Zoom had seized the zeitgeist and become the de facto conferencing platform. In June, Synergy Research found that the company had caused a spike in the wider conferencing software as a service (SaaS) market virtually on its own.

To accommodate the anticipated uptake as lockdowns hit, the two companies’ engineers added ‘tens of thousands’ of EC2 instances, according to AWS. Alongside this, the company has used Amazon WorkSpaces virtual desktops to help with the increased demand for helpdesk support, as well as integrating Zoom video meetings into Amazon Alexa-enabled smart displays to aim for more seamless integration between home and office working.

“Covid-19 changed everything for Zoom, putting demands on the company to meet the video conferencing needs of hundreds of millions of new participants around the globe, and AWS was there from the beginning to ensure Zoom could scale to meet these new requirements virtually overnight,” said Andy Jassy, AWS CEO in a statement.

This news comes as something of a contrast with an announcement made by Oracle Cloud in April. Seen at the time as a major coup, the company said Zoom had selected it for requiring immediate additional cloud capacity with Oracle’s second-generation infrastructure ‘helping Zoom scale to continue to deliver flawless service to its customer base.’

Continuous software delivery startup Harness Inc. said today it’s integrating its platform with Amazon Web Services Inc.’s Elastic Container Service in a move that will enable users to run applications in Docker containers with less scripting and redundancy.

Amazon ECS is a fully managed container orchestration service that rivals the better known open-source Kubernetes service. They’re both used to manage software containers that host the components of modern apps distributed across many computer servers.

Harness sells a “continuous delivery-as-a-service” platform that uses machine learning algorithms to monitor new software updates for any problems. The service allows users to understand what’s happening with an application’s baseline environment and initiate automatic rollbacks to a previous version if any irregular activity is detected.

Harness said that by integrating its service with Amazon ECS, it enables numerous features that were previously exclusive to Kubernetes users.

That’s important, Harness says, because its 2020 State of Continuous Delivery Insights Report from earlier this year found that 31% of companies use Amazon ECS exclusively for container orchestration, while 67% use Kubernetes.

“Naturally, a vast majority of CI/CD vendors have built their applications for organizations using Kubernetes, but in doing so, they have alienated more than a quarter of the market,” Harness says, referring the providers of continuous integration and continuous deployment of applications.

So today’s integration is all about filling that gap, Harness said. By combining its platform with Amazon ECS, users will be able to automate app deployments without needing to manage scripts and handle various failure conditions during deployment.

Moreover, there will be less redundancy, Harness said. Finally, Amazon ECS users will be able to take advantage of several “out-of-the-box deployment strategies” that Harness provides, such as Canary, Blue-Green and Basic Deployments, as well as its failure strategy and automated rollback capabilities.

“With their single minded focus on Kubernetes, most DevOps SaaS solutions have forgotten Amazon ECS customers,” said Harness co-founder and Chief Executive Jyoti Bansal (pictured). “We understand that many of our customers are happy with their ECS environments and don’t plan to switch, which is why we offer the same level of functionality for ECS and Kubernetes.”

What Is GAIA-X?

GAIA-X is a joint Franco-German initiative to create a federated cloud computing ecosystem for Europe. Announced formally on 4 June 2020, the project aims to establish a common set of technical standards, regulatory requirements, and data governance rules that any cloud provider — European or otherwise — must meet to participate in the platform.

At launch, the initiative was governed by 22 founding cloud providers and users from Germany and France, including large industrial names such as ATOS, Bosch, and Siemens. A non-profit organisation was planned for establishment in Brussels to manage the platform’s rules, bylaws, and engineering requirements before opening membership to other European and international companies.

The Core Technical Goals

GAIA-X is not designed to build a single monolithic cloud platform. Instead, it aims to create a federated architecture with interoperability at its centre. The practical goals as stated at launch include:

• Portability: Users should be able to move data and workloads between any provider within the ecosystem without vendor lock-in or data loss.
• Transparency: Full visibility into who has access to data at any given time, addressing one of the persistent criticisms of hyperscaler cloud arrangements.
• Interoperability: Common engineering standards that allow services from different providers to work together seamlessly.
• Data sovereignty: Alignment with European data protection rules, particularly GDPR, so that European users retain meaningful control over their data.

Boris Otto, executive director of the Fraunhofer Institute for Software and Systems Technology and one of the architects behind GAIA-X, described the ambition this way: “What we want to create is a federated data infrastructure that makes sure that European values — like openness, interoperability, transparency about the use of the data, and trust — are ensured at all times.”

Why Now? The Political and Market Context

The idea originated in July 2019 in a conversation between German Economy Minister Peter Altmaier and Chancellor Angela Merkel. France formally joined the initiative in August 2019. By the time of the June 2020 launch, the COVID-19 pandemic had added urgency to the project: disrupted global supply chains had reinforced European concerns about strategic dependence on technology infrastructure outside their regulatory jurisdiction.

French Economy Minister Bruno Le Maire framed it in terms of geopolitical identity at the launch event: “We are not China, we are not the United States, we are European countries with our own values and with our own economic interests that we want to defend.”

German Economy Minister Altmaier described GAIA-X as “nothing less than a European moonshot in digital policy” and referenced the Greek goddess Gaia — worshipped as the mother of life — as the inspiration for the project’s name.

The Competitive Landscape: US Hyperscalers and the Lock-in Problem

The three dominant US cloud providers — Amazon Web Services, Microsoft Azure, and Google Cloud — collectively account for a substantial majority of the European cloud market. The difficulty of migrating between these platforms, due to proprietary APIs, data egress costs, and tightly integrated service dependencies, has long been identified as a structural barrier to competition.

GAIA-X directly targets this lock-in problem. By mandating interoperability and portability as baseline requirements for any provider in the ecosystem, the initiative aims to lower switching costs and create space for smaller European cloud providers to compete on merit rather than incumbency.

However, analysts cautioned against reading the initiative as a straightforward exclusion of US providers. Candice Tran Dai, an expert at the Global Foundation for Cyber Studies & Research, noted: “If you look at the cloud market from a business point of view, it would be very difficult to exclude non-EU cloud actors.”

Are US Hyperscalers Involved?

Despite the sovereignty language, GAIA-X has not positioned itself as a fortress against US providers. At the time of the launch:

• Amazon and Google had both participated in GAIA-X technical working groups and indicated willingness to continue.
• Microsoft indicated it was in active discussions about participation, stating it believed it could “ensure the appropriate technological architecture and meet the necessary operational principles.”

This participation reflects the pragmatic reality: excluding the dominant providers entirely would make GAIA-X less relevant to the European enterprises it is trying to serve. The practical goal, as Tran Dai framed it, is “standardisation and harmonisation” within a rules-based framework — not market exclusion.

Governance and Expansion Plans

The 22 founding members took responsibility for establishing the non-profit entity, its bylaws, and initial governance policies. Gerd Hoppe, member of the executive board at Beckhoff Automation, speaking on behalf of the founding group, described this phase as “a service for the future community of members.”

The roadmap at launch envisaged the platform becoming operational in early 2021, after which other EU member states would be invited to join, expanding GAIA-X from a Franco-German bilateral project into a genuinely pan-European data ecosystem. The longer-term ambition, according to Boris Otto of the Fraunhofer Institute, is that GAIA-X could become an exportable standard — much as GDPR extended European data protection norms globally — making it attractive to companies and countries beyond Europe.

DevOps and Engineering Implications

For DevOps engineers and cloud architects working in Europe, GAIA-X is worth tracking for several practical reasons:

• Portability standards: If GAIA-X mandates common APIs or data portability formats, it could reduce the cost and complexity of multi-cloud strategies for European workloads.
• Compliance alignment: Engineering requirements tied to GDPR and European data governance could simplify compliance architecture for organisations operating across EU jurisdictions.
• Vendor competition: Lowered switching costs and clearer interoperability requirements could make it easier to negotiate with providers and architect hybrid or multi-cloud environments.
• New provider opportunities: Analysts noted that a common framework could give smaller European cloud providers a stronger footing, potentially expanding the competitive options available to engineering teams.

Summary

GAIA-X represents Europe’s most concrete attempt to date to establish a rules-based, federated cloud ecosystem grounded in European data protection values. It is not an attempt to build a rival hyperscaler from scratch, nor is it a straightforward exclusion of US providers. Rather, it is an effort to define the terms on which any provider — domestic or foreign — can operate in a European cloud market, with portability, transparency, and interoperability as the baseline requirements. Whether those ambitions translate into meaningful market change depends on the standards that ultimately emerge and the rate of adoption across EU member states.

Why Cloud Monitoring Has Become a Priority

Traditional on-premises monitoring tools were designed for static infrastructure with predictable topologies. Cloud environments introduce ephemeral compute, auto-scaling groups, serverless functions, managed databases, and multi-region deployments — all of which can change state within seconds. Legacy monitoring approaches break down when the target infrastructure is this dynamic.

Cloud monitoring tools address this gap by providing:

• Real-time metrics collection across compute, network, storage, and application layers.
• Distributed tracing to follow a request across microservices and identify latency bottlenecks.
• Log aggregation and correlation to connect events across disparate services.
• Alerting and on-call workflows that route incidents to the right team at the right time.
• Cost and resource utilization visibility to prevent cloud spend from spiraling undetected.

Major Vendors in the Cloud Monitoring Space

The market includes a mix of cloud-native SaaS platforms, established enterprise software vendors, and open-source projects. The following vendors are frequently cited as significant players:

SaaS-First Observability Platforms

• Datadog — A full-stack observability platform covering infrastructure metrics, APM, log management, synthetics, and security. Widely adopted in cloud-native engineering teams.
• New Relic — Offers application performance monitoring (APM), infrastructure monitoring, browser and mobile monitoring, and a unified telemetry data platform.
• AppDynamics (Cisco) — Enterprise-focused APM with deep application topology mapping and business transaction tracing. Commonly deployed in large enterprises running Java, .NET, and Node.js applications.
• PagerDuty — Primarily an incident management and alerting platform that integrates with monitoring tools to route, escalate, and resolve incidents.

Cloud Provider Native Tools

• Amazon CloudWatch (AWS) — AWS’s native monitoring service for metrics, logs, alarms, and dashboards across all AWS services. Often the default starting point for teams on AWS.
• Microsoft Azure Monitor — Azure’s integrated monitoring solution covering metrics, logs via Log Analytics, and Application Insights for APM.
• Google Cloud Operations Suite (formerly Stackdriver) — Monitoring and logging for workloads running on Google Cloud.

Enterprise and Legacy Vendors Extending to Cloud

• Broadcom (formerly CA Technologies) — Offers enterprise monitoring and AIOps capabilities, primarily targeting large IT organizations with complex hybrid environments.
• BMC Software — Provides IT operations management tools, including TrueSight, which covers infrastructure and application monitoring for hybrid environments.
• VMware Aria Operations (formerly vRealize Operations) — Focused on virtualized and multi-cloud environments, particularly for VMware-heavy infrastructure teams.

Open-Source and Lightweight Options

• Netdata — An open-source, lightweight agent for real-time metrics collection with a low resource footprint. Popular for monitoring Linux hosts and containers without heavy infrastructure overhead.
• Paraleap Technologies (AzureWatch) — A cloud monitoring and auto-scaling tool focused on Microsoft Azure environments.

Key Drivers of Market Growth

Several structural trends continue to expand the addressable market for cloud monitoring tools:

Accelerated Cloud Migration

Organizations across industries have accelerated the migration of workloads from on-premises data centers to public and hybrid cloud environments. Each migrated workload requires monitoring coverage, expanding the market for both cloud-native and hybrid-capable tools.

Microservices and Containerization

Kubernetes, Docker, and service mesh architectures have fragmented monolithic applications into many independently deployable services. This increases the number of instrumentation points and the complexity of correlating signals across them, driving demand for purpose-built distributed tracing and observability tools.

Site Reliability Engineering (SRE) Adoption

As SRE practices spread beyond large technology companies, teams are formalizing service level objectives (SLOs) and error budgets. Implementing and tracking these requires sophisticated monitoring tooling that can report on availability and latency targets continuously.

Compliance and Security Observability

Regulatory requirements and security best practices increasingly require continuous log retention, audit trails, and anomaly detection. Many monitoring platforms have extended into security observability (SIEM integration, runtime threat detection), broadening their relevance to security teams alongside DevOps.

Market Segmentation DevOps Teams Should Understand

When evaluating cloud monitoring tools, it helps to think in terms of the following dimensions:

By Organization Size

• Large enterprises typically require tools with strong RBAC, SSO integration, audit logging, SLA-backed support, and the ability to monitor complex multi-cloud or hybrid topologies. Vendors like AppDynamics, Broadcom, and BMC Software are historically strong here.
• SMEs and startups often prioritize quick time to value, transparent pricing, and self-service onboarding. Datadog, New Relic, and Netdata are commonly found in this segment.

By Primary Cloud Platform

• Teams heavily invested in AWS often start with CloudWatch and supplement it with a third-party platform for deeper APM or cross-cloud visibility.
• Azure-first organizations frequently use Azure Monitor and Application Insights, sometimes alongside Datadog or Dynatrace.
• Multi-cloud environments favor vendor-neutral platforms with native integrations for all major cloud providers.

By Monitoring Depth

• Infrastructure monitoring: CPU, memory, disk, network at the host or container level.
• Application Performance Monitoring (APM): Request tracing, error rates, latency histograms, and dependency maps at the application layer.
• Log management: Centralized storage, search, and alerting on structured and unstructured log data.
• Synthetic monitoring: Scripted tests that simulate user journeys to detect availability and performance issues proactively.
• Real User Monitoring (RUM): Capturing performance data from actual end-user browsers and mobile clients.

Evaluating Cloud Monitoring Tools: Practical Criteria

When selecting a cloud monitoring platform, DevOps and platform engineering teams should assess the following:

• Integration breadth: Does the tool natively support your cloud providers, container orchestrator, CI/CD pipeline, and alerting workflows?
• Data retention and cardinality: Can it handle high-cardinality metrics (e.g., per-pod metrics for thousands of containers) without prohibitive cost?
• Query language and dashboarding: Is the query interface expressive enough to answer your operational questions quickly?
• Alert quality: Does the alerting system support anomaly detection, multi-condition rules, and notification routing without generating excessive noise?
• Total cost of ownership: SaaS monitoring tools can become expensive at scale. Understand how pricing scales with data ingestion volume, host count, or active users.
• Vendor lock-in risk: Consider whether instrumentation is based on open standards (OpenTelemetry, Prometheus) or proprietary agents and SDKs.

The Role of OpenTelemetry

A significant development shaping the competitive landscape is the widespread adoption of OpenTelemetry (OTel), a CNCF project that provides vendor-neutral APIs, SDKs, and a collector for traces, metrics, and logs. Most major monitoring vendors now support OTel as an ingestion path, which reduces the switching cost between platforms and mitigates instrumentation lock-in. DevOps teams building new services should strongly consider instrumenting with OpenTelemetry from the outset.

Summary

The cloud monitoring tools market encompasses a wide spectrum of solutions — from lightweight open-source agents like Netdata to full-stack enterprise platforms like AppDynamics and Datadog. Market growth is driven by cloud migration, microservices complexity, SRE adoption, and security requirements. For DevOps practitioners, the most important decisions are not which vendor analysts rank highest, but which platform integrates cleanly with your stack, scales economically with your data volume, and supports open instrumentation standards that protect your future flexibility.

Evaluating tools against your actual infrastructure topology, team size, and alerting maturity will produce better outcomes than following market share rankings alone.

A Crisis Lands on Cloud Bills

By mid-March 2020, John Lyotier’s travel software company, Left Technologies Inc, was in freefall. The COVID-19 pandemic had gutted the travel industry almost overnight. Like many small business owners, Lyotier reached out to his office landlord — and got rent relief. Then he contacted Amazon.com Inc, asking to “explore creative financing opportunities” for his monthly cloud-computing bill.

The response from Amazon was blunt: “Nope, that’s the way it is.”

That exchange encapsulates the friction between the commercial reality of cloud computing contracts and the economic devastation experienced by startups and small businesses during the pandemic’s early months.

Cloud Giants: Essential Infrastructure, Rigid Contracts

Amazon Web Services (AWS), Microsoft Azure, and Google Cloud had transformed the internet economy over the prior decade by making enterprise-grade computing accessible on a credit card. The on-demand, pay-as-you-go model was a core part of their pitch to startups: scale up when you need it, scale down when you don’t.

But the actual commercial reality for many established cloud customers looked very different. To attract larger customers and lock in revenue, all three providers had shifted their sales strategy toward long-term committed-spend contracts — agreements where customers receive discounted pricing in exchange for guaranteed minimum annual spend over one or more years.

As of December 2019, those commitments were substantial:

• AWS reported US$29.8 billion in remaining performance obligations from contracts longer than one year.
• Google Cloud disclosed US$11.4 billion in future cloud revenue it planned to book from committed contracts.
• Microsoft sells both cloud services and much of its on-premises software via multiyear agreements, though it did not break out a comparable figure at that time.

For companies whose revenues had cratered, these committed minimums meant their cloud costs were essentially fixed — even as their businesses had stopped generating income.

The Gap Between Flexibility Promised and Flexibility Delivered

“Their revenues have sunk 50 percent or 60 percent, but their costs have remained static,” said Jason Kim, co-founder of Taloflow, a firm that advises businesses on managing AWS expenditure. Kim described the major cloud providers as having been “pretty radio silent” in response to customer appeals for relief.

Half a dozen startup executives interviewed for the original Bloomberg report said that outreach to cloud providers had gone unanswered. None of the three major providers — AWS, Microsoft, or Google — made public announcements about deferring or reducing bills for clients during this period.

According to a person familiar with the decisions cited in the original reporting, AWS was restructuring some large enterprise contracts on a case-by-case basis. Smaller companies, however, were not receiving the same treatment.

One example from the reporting illustrates Google’s position directly: when a California company asked Google Cloud for payment assistance, it was told the only available option was to suspend the account and reopen it 30 days later — essentially losing access to their infrastructure rather than receiving any payment flexibility. This was described in an April 1 email viewed by Bloomberg News.

A Google Cloud spokesperson declined to confirm details, stating: “We treat payment terms with our customers or partners as confidential and we’re not able to disclose details.”

An AWS spokesman acknowledged the difficulty but stopped short of announcing any specific relief program: “This is a very difficult time for some of our customers, and we are actively working across a number of dimensions to help them manage costs.” He also noted that customers can shut off cloud services more easily than they could decommission corporate-owned data centers.

According to a separate report by The Information, both Microsoft and Google did extend payment deadlines for at least some customers.

Contrast With Other Tech Vendors and Regulated Utilities

The restraint from major cloud providers stood in contrast to the actions of older technology companies. Cisco Systems, for example, publicly offered credits to customers facing financial hardship during the same period.

Regulated utilities offered a starker comparison. California state regulators moved to prohibit utility providers from cutting off power to customers who missed payments — treating electricity and water as essential services that could not be interrupted during a public health emergency.

Some smaller cloud and hosting providers took a more pragmatic approach. Pierre-Luc Quimper, CEO of GloboTech Communications — a Montreal-based company offering server and data management services — said that approximately 7 percent of clients had reported payment difficulties that month, and he expected that figure to rise. GloboTech had accommodated the vast majority of affected customers with deferred or staggered payment plans.

“The Internet is an infrastructure that’s become essential to our lives,” Quimper said. “We’re not interested in putting companies out of business just because they’re not paying for a month.”

What This Reveals About Cloud Contract Risk

The COVID-19 period exposed a structural risk that many cloud customers had not fully accounted for: the gap between the flexibility of on-demand cloud usage and the rigidity of committed-spend contracts. The very discounts that made long-term agreements attractive also created exposure when business conditions changed suddenly.

For DevOps and infrastructure teams advising their organizations on cloud strategy, the episode highlights several practical considerations:

• Understand your contractual commitments. Committed Use Discounts (CUDs) on Google Cloud, Savings Plans and Reserved Instances on AWS, and Azure Reserved VM Instances all carry minimum spend obligations. Know what you are locked into and for how long.
• Model downside scenarios. Before signing a multi-year committed contract, stress-test the minimum spend against scenarios where your product usage drops significantly.
• Negotiate exit clauses and flexibility upfront. It is far easier to negotiate force majeure provisions, ramp schedules, or early termination options before signing than after a crisis has begun.
• Diversify where possible. Dependence on a single cloud provider with a large committed contract creates concentration risk. Multi-cloud or hybrid approaches can reduce this exposure, though they carry their own operational complexity.
• Monitor FinOps continuously. Tools and practices around cloud financial operations (FinOps) help ensure your actual usage is aligned with your committed spend, reducing waste and over-commitment.

Cloud as Critical Infrastructure

The pandemic accelerated a debate that had been building for years: whether cloud computing has become critical infrastructure in the same sense as electricity, water, or telecommunications — and whether that status should come with corresponding regulatory obligations or protections for customers.

In 2020, that question went largely unanswered by governments and cloud providers alike. But for the startups caught between collapsing revenue and fixed cloud bills, it was not an abstract policy question. It was a matter of survival.

Original reporting by Bloomberg News, as published by Taipei Times, April 2020.

The Three Service Categories: IaaS, PaaS, and SaaS

Cloud services are broadly divided into three delivery models:

• Infrastructure as a Service (IaaS): Provides virtualized compute, storage, and networking resources on demand. The customer manages the operating system and above; the provider manages the physical hardware.
• Platform as a Service (PaaS): Adds a managed runtime environment, middleware, and development tools on top of the infrastructure. Targeted primarily at application developers.
• Software as a Service (SaaS): Delivers fully managed applications over the internet. The customer manages only data and user configuration.

In practice, most enterprise IT operations teams gravitate toward IaaS and SaaS. IaaS offers the control and familiarity that infrastructure teams are comfortable with, while SaaS eliminates the overhead of running common business applications. PaaS occupies a middle ground that is popular with development teams but less widely adopted across the enterprise as a whole.

How the Cloud Market Has Evolved

The Rise of the Big Three

The modern public cloud market is dominated by three hyperscale providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Each offers hundreds of services spanning compute, storage, databases, networking, AI/ML, security, and developer tooling.

AWS launched with a small set of foundational services — Amazon EC2 for virtual compute, Amazon S3 for object storage, and Amazon SQS for message queuing — that remain core to its portfolio today. Over subsequent years, AWS expanded into container orchestration, serverless functions, block and file storage, relational and NoSQL databases, content delivery, and a broad suite of monitoring and security tools.

Google entered the IaaS market in 2008 with App Engine, leveraging the same internal infrastructure it had built to power Search, advertising, and consumer applications. Microsoft launched Azure in 2010 and later added Azure Stack to address hybrid on-premises and cloud deployments.

Market Share and Regional Variations

AWS has historically held the largest share of the global public cloud infrastructure market, followed by Azure and Google Cloud. In China, domestic providers such as Alibaba Cloud and Tencent Cloud dominate due to regulatory and market conditions. IBM and Oracle maintain meaningful positions in the enterprise segment, particularly among organizations with existing vendor relationships and legacy workloads.

The Shift from Infrastructure to Higher-Level Services

The evolution of cloud computing has moved steadily up the software stack. Early cloud services required users to provision and configure virtual machines much as they would physical servers. Over time, providers introduced managed services that abstract away infrastructure concerns entirely.

This progression has produced several categories of higher-level offerings:

• Managed security and identity services: Centralized policy enforcement, role-based access control, and threat detection without requiring dedicated security infrastructure teams.
• DevOps tooling: Managed code repositories, CI/CD pipeline automation, artifact registries, and testing environments directly integrated with cloud infrastructure.
• Serverless compute: Functions-as-a-Service (FaaS) offerings that automatically provision and decommission compute capacity per invocation, eliminating server management entirely.
• Managed data and AI platforms: Fully managed data warehouses, streaming analytics, and machine learning training and inference pipelines targeted at data engineers and analysts.

Advantages of Cloud Computing

Organizations adopt cloud services for a wide range of operational and strategic reasons. The following are among the most commonly cited benefits:

Financial Flexibility

Cloud computing shifts IT spending from large upfront capital expenditures (hardware purchases, data center build-out) to ongoing operational expenses billed monthly or annually. This model suits organizations that need to scale resources quickly or unpredictably, or that prefer to preserve capital for core business investment.

That said, cloud cost management is genuinely complex. For workloads with stable, predictable resource consumption and large datasets, on-premises infrastructure with dedicated servers can be more cost-effective over a multi-year horizon. Accurate total cost of ownership (TCO) comparisons require careful modelling.

Reduced Operational Overhead

Cloud providers take responsibility for the physical infrastructure layer, including hardware maintenance, facility management, power, cooling, and physical security. For managed services, they also handle OS patching, software updates, and availability. This frees internal IT staff to focus on application development and business-facing initiatives rather than infrastructure maintenance.

Access to Current Technology

Hyperscale providers invest heavily in cutting-edge hardware — including the latest CPU and GPU generations, high-speed network interface cards, and custom AI accelerators — and make these available to customers without requiring long procurement cycles. Organizations can access capabilities that would be difficult or uneconomical to deploy in a private data center.

Elastic Scalability

Cloud infrastructure is engineered for massive scale. Individual services can be expanded rapidly to handle peak workloads and scaled back when demand subsides, avoiding the over-provisioning that is common with on-premises infrastructure purchased to meet peak capacity requirements.

Resilience and Geographic Redundancy

Hyperscale providers operate multiple physical regions and availability zones, giving organizations a straightforward path to redundant, geographically distributed deployments. Some managed services include built-in multi-site redundancy that would require significant engineering effort to replicate on premises.

Security Engineering at Scale

Large cloud providers maintain dedicated security teams, automated threat detection, and compliance certifications (such as ISO 27001, SOC 2, PCI DSS, and FedRAMP) that most organizations cannot match internally. The underlying physical and network infrastructure benefits from security investment at a scale few enterprises can justify independently.

Common Cloud Workloads and Use Cases

The following categories represent workloads most frequently migrated to or built on public cloud infrastructure:

• Small to mid-size databases and complex backend systems supporting enterprise applications such as ERP and financial processing platforms
• Public-facing websites, web applications, and content distribution networks
• Data analytics pipelines, including IoT data ingestion, consolidation, and real-time analysis
• Legacy applications previously running on internal virtualization stacks
• Software development, testing, staging environments, and CI/CD workflows
• Containerized microservices applications
• AI and machine learning model training and inference

Disadvantages of Cloud Computing

Cloud services solve many IT operational problems but introduce a distinct set of challenges that organizations must plan for deliberately.

Shared Security Responsibility

Public cloud providers operate on a shared responsibility model: the provider secures the infrastructure, but the customer is responsible for correctly configuring access controls, encryption, network policies, and data handling. Misconfigurations in this model have been the root cause of some of the largest data exposure incidents in recent years. Security in the cloud requires specific expertise and ongoing attention that goes well beyond traditional perimeter-focused security approaches.

Complex and Opaque Pricing

Cloud pricing is multidimensional. Compute instances alone may have on-demand, reserved, spot, and savings plan pricing tiers. Additional charges apply for data storage, API calls, data transfer, support tiers, and premium features. Calculating accurate TCO typically requires dedicated tooling — either the provider’s native cost management service or a third-party cloud cost optimization platform.

Egress Costs and Vendor Lock-In

Most cloud providers charge for outbound data transfer (egress) at rates that can become significant for data-intensive workloads. This creates a financial disincentive to migrate large datasets between providers or retrieve them to on-premises systems, effectively creating vendor lock-in reinforced by economics rather than just technical dependency.

Reduced Configuration Flexibility

Managed cloud services abstract infrastructure details, which reduces management burden but also limits configuration options. Organizations with highly specific performance tuning, compliance, or operational requirements may find that cloud service defaults or constraints do not accommodate their needs, requiring workarounds or custom solutions that partially undermine the managed service value proposition.

Support Quality Variability

Enterprise support from hyperscale cloud providers can be inconsistent in responsiveness and depth, particularly at lower support tiers. Organizations with mission-critical cloud deployments often supplement provider support with third-party managed service partners who can provide faster response times and more tailored expertise.

Network Dependency

Cloud-hosted workloads require reliable, low-latency connectivity. For latency-sensitive or high-throughput use cases, a dedicated private circuit to the cloud provider (such as AWS Direct Connect or Azure ExpressRoute) is often necessary, adding cost and lead time to cloud deployments.

Skills Gap

Operating cloud infrastructure effectively requires skills that differ from traditional data center operations: cloud-native architecture patterns, infrastructure-as-code tooling, cloud security posture management, and provider-specific service knowledge. Many organizations underestimate the training and hiring investment required to develop genuine cloud operations competency.

Regulatory and Data Residency Compliance

Storing and processing data in cloud regions outside a home country or industry regulatory boundary can create compliance obligations under frameworks such as GDPR, HIPAA, or local data sovereignty laws. Understanding where data physically resides, how it is replicated, and who can access it requires deliberate architectural decisions and ongoing governance.

Finding the Right Balance

For most organizations, the question is not whether to use cloud computing, but which workloads belong in the cloud and which are better served by on-premises or colocation infrastructure. The sustained revenue growth of all three major cloud providers reflects growing enterprise adoption, but the pattern that emerges for large organizations is typically a hybrid model: cloud for variable, scalable, and new workloads; on-premises for stable, data-intensive, or latency-sensitive applications where dedicated hardware delivers a better cost profile.

Smaller organizations and startups, which lack existing infrastructure investments and need to scale quickly, often find an all-cloud approach straightforward to justify. Large enterprises with established data centers and complex regulatory requirements generally converge on a mixed architecture that optimizes cost, performance, and compliance across the full application portfolio.

Regardless of scale, a clear workload assessment — evaluating data volume, latency requirements, compliance obligations, team skills, and total cost — should precede any significant cloud commitment.

As container adoption has grown, so has the ecosystem of platforms built to orchestrate, scale, and secure them. Whether you are a solo developer experimenting locally or an enterprise running thousands of microservices across hybrid cloud infrastructure, choosing the right container management platform matters. Below is a practical overview of the most widely used options, both open-source and commercial.

What Is a Container Management Platform?

A container management platform handles the operational concerns that arise when you run containers at scale: scheduling workloads across hosts, allocating CPU and memory, restarting failed containers, exposing services over the network, and rolling out updates without downtime. Without an orchestration layer, teams must manage these tasks manually—which becomes impractical beyond a handful of containers.

Kubernetes

Kubernetes is the dominant open-source container orchestration system. Originally designed at Google and released in 2014, it is now stewarded by the Cloud Native Computing Foundation (CNCF). Kubernetes automates deployment, horizontal scaling, self-healing (restarting crashed containers), and rolling updates across clusters of nodes.

Key concepts in Kubernetes include:

• Pods – the smallest deployable unit, containing one or more containers that share networking and storage.
• Deployments – declarative specifications for how many pod replicas should run and how updates are rolled out.
• Services – stable network endpoints that abstract the underlying pods.
• Namespaces – logical partitions that allow multiple teams or projects to share a cluster safely.

Because Kubernetes is open-source and cloud-agnostic, it runs on-premises, in public clouds, and at the edge. Most major cloud providers offer a managed Kubernetes service built on top of it. Many vendors also publish their own Kubernetes distributions with additional tooling and support contracts.

Best for: Teams that need maximum flexibility and portability, and that are willing to invest in operational expertise.

Docker

Docker, introduced by Docker Inc. in 2013, popularised the container format that most of the industry now uses. The Docker platform covers the full developer workflow: building images with a Dockerfile, storing them in a registry, and running them as containers locally or in CI/CD pipelines.

Docker’s core strengths include:

• Developer experience – a simple CLI and docker compose for defining multi-container applications locally.
• Isolation – containers run in isolated namespaces, keeping dependencies separate from the host system.
• Security – built-in image scanning (via Docker Scout), content trust for image signing, and rootless container support.
• Large ecosystem – Docker Hub hosts a vast library of official and community images.

While Docker itself is not a full cluster orchestrator, Docker Swarm provides basic multi-node orchestration. For production-scale orchestration, most teams pair Docker images and registries with Kubernetes or a managed cloud service.

Best for: Local development, CI/CD image builds, and teams getting started with containers.

Amazon Elastic Container Service (ECS)

Amazon Elastic Container Service (ECS) is AWS’s proprietary container orchestration service. It schedules Docker containers across a managed cluster of Amazon EC2 instances—or, when combined with AWS Fargate, removes the need to manage EC2 instances entirely.

ECS is deeply integrated with the broader AWS ecosystem, making it a natural choice for teams already using AWS services:

• Amazon ECR – private container image registry, tightly integrated with ECS task definitions.
• IAM – fine-grained per-task identity and access management.
• CloudWatch – logs, metrics, and alarms for running tasks.
• AWS Secrets Manager and Parameter Store – secure injection of credentials at runtime.
• Elastic Load Balancing – automatic load balancing for ECS services.

According to AWS, ECS powers internal AWS services such as Amazon SageMaker, AWS Batch, and Amazon Lex, which gives confidence in its reliability and security posture at scale.

Best for: Teams heavily invested in the AWS ecosystem who want a managed orchestration service without operating a Kubernetes control plane.

Google Kubernetes Engine (GKE)

Google Kubernetes Engine (GKE) is Google Cloud’s fully managed Kubernetes service. Because Kubernetes originated at Google, GKE typically tracks upstream Kubernetes releases quickly and offers features that reflect deep familiarity with the project.

Notable GKE capabilities include:

• Autopilot mode – Google manages node provisioning, scaling, and security hardening automatically; you pay only for pod resource requests.
• Standard mode – gives operators control over node configuration while Google manages the control plane.
• Integrated observability – native integration with Google Cloud Monitoring and Logging.
• Release channels – choose Rapid, Regular, or Stable to control how quickly clusters receive Kubernetes version upgrades.
• Workload Identity – maps Kubernetes service accounts to Google Cloud IAM roles without storing credentials in pods.

GKE runs on Google Compute Engine instances grouped into node pools. Cluster autoscaler adds or removes nodes based on pending workloads, and Horizontal Pod Autoscaler adjusts replica counts based on CPU or custom metrics.

Best for: Teams on Google Cloud, or those who want a managed Kubernetes service with close alignment to upstream Kubernetes features.

AWS Fargate

AWS Fargate is a serverless compute engine for containers. Launched in 2017, Fargate initially supported Amazon ECS and was later extended to support Amazon EKS (Elastic Kubernetes Service), allowing Kubernetes users to run pods without managing EC2 nodes.

With Fargate, you define your container’s CPU and memory requirements, and AWS provisions and manages the underlying infrastructure. Key advantages:

• No node management – you do not patch, scale, or right-size EC2 instances.
• Per-task isolation – each Fargate task runs in its own kernel-isolated environment, reducing the blast radius of a container escape.
• Consumption-based pricing – you pay for the vCPU and memory allocated to your tasks while they run, avoiding the cost of idle cluster capacity.
• Native AWS integration – works with IAM task roles, CloudWatch Logs, Secrets Manager, and VPC networking.

The trade-off is less control: you cannot choose the underlying instance type or install node-level software. Fargate is best suited for workloads that fit within its resource limits and do not require privileged containers or custom kernel tuning.

Best for: Teams that want to run containers on AWS without any infrastructure management overhead, or that need strong workload isolation by default.

Red Hat OpenShift

Red Hat OpenShift is an enterprise Kubernetes platform that adds an opinionated set of developer and operations tooling on top of upstream Kubernetes. Its flagship product, OpenShift Container Platform, runs on Red Hat Enterprise Linux CoreOS and is available on-premises, in public clouds, and as a managed service.

OpenShift differentiates from bare Kubernetes in several ways:

• Built-in image registry and build pipelines – Source-to-Image (S2I) builds container images directly from source code without a separate CI system.
• Stricter security defaults – pods run as non-root by default; Security Context Constraints (SCCs) replace Kubernetes PodSecurityPolicies.
• Developer console – a web UI for browsing workloads, viewing logs, monitoring resource usage, and managing networking.
• Operator Framework – Red Hat is a primary contributor to the Operator pattern for packaging complex, stateful applications.
• Support and compliance – backed by Red Hat subscriptions with enterprise SLAs, which matters for regulated industries.

OpenShift supports hybrid and multi-cloud deployments, letting operations teams manage clusters across on-premises data centers and multiple cloud providers from a single control plane.

Best for: Enterprises that need a hardened, supported Kubernetes distribution with strong developer tooling and compliance features, particularly in regulated industries.

Choosing the Right Platform

No single platform is the best choice for every situation. Consider the following factors when evaluating options:

For most teams starting out, Docker handles local development and CI image builds, while a managed Kubernetes service (GKE, EKS, or AKS) handles production orchestration. Teams deeply committed to a single cloud may find ECS or Fargate simpler to operate. Enterprises with strict compliance needs or a preference for a supported distribution should evaluate Red Hat OpenShift.

As container tooling continues to mature, the operational gap between these platforms is narrowing—making the choice increasingly about ecosystem fit, team expertise, and cost rather than raw capability differences.

As the COVID-19 pandemic forced brick-and-mortar retail closures, accelerated remote work, and changed everyday consumer habits overnight, Amazon found itself at the center of an extraordinary demand surge. Across e-commerce, grocery delivery, cloud infrastructure, and streaming media, the company’s existing investments aligned almost perfectly with where the world suddenly needed to go. Here are six dimensions along which Amazon’s competitive position strengthened during the crisis.

1. A Greater Reliance on E-Commerce

With brick-and-mortar retailers across the United States and Europe forced to close their doors, and with many consumers unwilling or unable to leave their homes, Amazon became an essential utility for millions of households. Demand surged across product categories from household staples to home office equipment.

Much of this shift toward e-commerce was expected to be temporary—consumers would return to physical stores once restrictions lifted. However, behavioral economics and historical precedents from other digital adoption waves suggest that a meaningful portion of newly acquired online shoppers tend to stick with the habit. Convenience, familiarity, and the benefits of Amazon Prime all create switching costs that favor long-term retention.

2. An Inflection Point for Online Grocery

Online grocery was already growing before the pandemic, but COVID-19 created a step-change in adoption. Amazon’s Whole Foods delivery and Amazon Fresh services—available to Prime members with free delivery on qualifying orders within a selected two-hour window—saw massive order volumes in the metro areas where they operated.

Demand grew so rapidly that Amazon temporarily suspended its Prime Pantry service (which offered free shipping on grocery orders from Amazon warehouses) due to capacity constraints. For many consumers, this period represented their first experience with grocery delivery or pickup. First-time users who build the habit during a crisis often become long-term customers, making this moment a significant inflection point for Amazon’s grocery ambitions.

3. Expanded Fulfillment and Delivery Scale

In response to the demand surge, Amazon announced plans to hire 100,000 additional warehouse and delivery workers and temporarily raised hourly wages for those roles by $2 through April 2020. This built on an already aggressive expansion: in 2019, Amazon’s headcount grew by approximately 23% to around 798,000 employees, driven in part by its push to make one-day shipping the standard for Prime members.

Amazon’s financial position gave it the flexibility to absorb these costs. The company reported approximately $32 billion in net cash at the end of 2019, along with a strong credit rating. The practical effect of this hiring wave was to deepen the scale advantage Amazon holds over rivals in last-mile delivery and fulfillment—an advantage that compounds over time as fixed infrastructure costs are spread across higher volumes.

4. A Spike in Video Streaming Activity

Stay-at-home orders drove a notable surge in streaming consumption globally. In Europe, regulators and telecom operators asked major streaming providers to reduce video bitrates to ease pressure on network infrastructure—a measure of just how sharp the increase in usage was.

Amazon Prime Video, widely regarded as one of the key drivers of Prime membership acquisition and retention, almost certainly benefited from this environment. Similarly, time spent on devices running Amazon’s Fire TV platform was likely elevated. For Amazon, video content is not purely an entertainment business—it is an engagement mechanism that makes Prime memberships stickier, which in turn supports the entire Amazon ecosystem.

5. Accelerated Migration to Public Cloud

Amazon Web Services (AWS) and its cloud competitors faced real short-term friction during the early pandemic period. Enterprise sales cycles typically involve in-person meetings, on-site assessments, and collaborative implementation work—all of which became difficult or impossible under social distancing measures.

However, the broader economic pressure of the crisis cut in cloud’s favor over the medium term. Businesses facing cash constraints and uncertain revenue outlooks became increasingly reluctant to commit to large capital expenditures on on-premise IT hardware and software. Pay-as-you-go public cloud services, with their variable cost model and minimal upfront investment, became more attractive by comparison. The structural shift from on-premise to cloud infrastructure, already well underway before COVID-19, appeared poised to accelerate as a result.

6. An Improved Public Image

In the period before the pandemic, Amazon faced significant reputational headwinds: antitrust scrutiny from the FTC, criticism over working conditions at fulfillment centers, and broader political and media pressure on large technology companies. The crisis created an opportunity for reframing.

By acting as a critical supply chain for essential goods during the pandemic, expanding hiring at a time of rising unemployment, and raising wages—even temporarily—Amazon positioned itself as a stabilizing force rather than a disruptive one. Public sentiment toward organizations that visibly contribute during a crisis tends to carry forward, providing a degree of reputational capital that can soften future regulatory or media pressure.

Looking Back: What Actually Happened

This analysis was written in March 2020, near the beginning of the pandemic. In retrospect, most of these trends did materialize as anticipated: e-commerce adoption accelerated broadly, online grocery gained lasting new users, AWS continued its strong growth trajectory, and Amazon expanded its workforce significantly. The longer-term reputational and regulatory picture proved more complex, with renewed antitrust scrutiny and ongoing labor debates continuing through subsequent years. But as a snapshot of where structural momentum was pointing in early 2020, the framework held up well.

Original analysis sourced from TheStreet, March 2020.

Overview

In March 2020, the White House announced the formation of a new public/private consortium aimed at directing America’s high-performance computing (HPC) infrastructure toward COVID-19 research. The consortium was co-led by IBM, the White House Office of Science and Technology Policy (OSTP), and the U.S. Department of Energy. Major cloud and technology companies — Amazon Web Services, Google Cloud, and Microsoft Azure — joined alongside academic institutions including MIT and Rensselaer Polytechnic Institute, as well as federal agencies such as the National Science Foundation and NASA.

Why Supercomputing Matters for Pandemic Research

High-performance computing enables researchers to run calculations at a scale and speed impossible on conventional hardware. Dario Gil, IBM’s Director of Research, explained the value directly:

“How can supercomputers help us fight this virus? These high-performance computing systems allow researchers to run very large numbers of calculations in epidemiology, bioinformatics, and molecular modeling. These experiments would take years to complete if worked by hand, or months if handled on slower, traditional computing platforms.”

Dario Gil, Director of Research, IBM

The three primary research domains targeted by the consortium were:

• Epidemiology – modeling how the virus spreads through populations to inform public health decisions.
• Bioinformatics – analyzing genomic and proteomic data to understand the virus’s structure and behavior.
• Molecular modeling – simulating interactions between viral proteins and potential drug candidates to accelerate treatment discovery.

Consortium Members and Compute Capacity

IBM coordinated with national laboratories and partner institutions to make a combined total of 330 petaflops of computing power available to qualifying research projects. The full list of contributing organizations included:

• IBM (consortium lead)
• Amazon Web Services (AWS)
• Google Cloud
• Microsoft Azure
• MIT
• Rensselaer Polytechnic Institute
• U.S. Department of Energy national laboratories
• National Science Foundation
• NASA
• National Institutes of Health (NIH)

IBM and its partners committed to evaluating research proposals and granting compute access to projects most likely to produce near-term, actionable results against the pandemic.

Individual Company Commitments

Amazon Web Services

AWS had already committed $20 million to support COVID-19 research before the consortium announcement, making cloud infrastructure and data services available to researchers and public health organizations.

Google Cloud

Mike Daniels, Vice President of Global Public Sector at Google Cloud, described the role cloud HPC would play:

“We know that high performance computing can reduce the time it takes to process massive data sets and perform complex simulations from days to hours. We look forward to participating in this initiative alongside leaders in technology, academia, and the public sector to make more resources available to COVID-19 researchers and to apply Google Cloud computing capabilities toward the development of potential treatments and vaccines.”

Mike Daniels, VP Global Public Sector, Google Cloud

Separately, Google’s parent company Alphabet — through its life-sciences subsidiary Verily — launched a tool to help Bay Area residents locate COVID-19 testing sites.

Microsoft Azure

John Kahan, Microsoft’s global head for the AI for Health program, outlined the company’s approach: expanding access to Azure cloud resources for COVID-19 researchers and creating structured opportunities for those researchers to collaborate directly with Microsoft’s data scientists.

“[We want to] make sure researchers working to combat COVID-19 have access to the tools they need.”

John Kahan, Global Head, AI for Health, Microsoft

Significance for the DevOps and Cloud Community

The consortium demonstrated how cloud-native infrastructure — elastic compute, managed data pipelines, and collaborative research platforms — could be rapidly repurposed for large-scale scientific work. Key takeaways for practitioners include:

• On-demand HPC at cloud scale can compress research timelines that would otherwise span months or years on conventional hardware.
• Cross-organization data governance becomes critical when multiple cloud providers, federal agencies, and academic institutions share infrastructure and data sets.
• Proposal-based access models — rather than first-come, first-served — help direct scarce compute toward the highest-impact work.
• Public-sector cloud partnerships (e.g., AWS GovCloud, Azure Government, Google Public Sector) are well-positioned to respond rapidly in crisis scenarios because security and compliance frameworks are already in place.

Summary

The COVID-19 High Performance Computing Consortium represented one of the largest coordinated deployments of shared supercomputing resources in response to a public health emergency. By pooling 330 petaflops across national labs and commercial cloud providers, and establishing a structured process for evaluating research proposals, the initiative offered a practical model for how government, industry, and academia can rapidly align infrastructure to address a crisis. The involvement of IBM, AWS, Google Cloud, and Microsoft underscored that modern pandemic response increasingly depends on the same distributed, scalable computing platforms that underpin everyday enterprise software delivery.

What Is Cloud Computing and Why Does It Matter?

Cloud computing has quietly become one of the most consequential infrastructure shifts in the history of technology. While most users never see it, the cloud underpins an enormous share of everyday digital activity — from streaming music and video to posting on social media, requesting a ride, and collaborating on documents across continents.

Rather than storing data or running software on a local device, cloud computing moves those workloads to large, professionally managed data centers. Users access computing power, storage, and software over the network — typically the internet — on demand and at scale.

Core Use Cases

The growth of cloud adoption has run in parallel with the expansion of 4G broadband and smartphone penetration. Together, fast networks and remote servers make the following possible for individuals and organizations:

• Media streaming: Music and video services offload encoding, storage, and delivery to the cloud, letting users stream on demand from any device.
• Remote and hybrid work: Productivity suites, communication tools, and file sharing platforms run in the cloud, enabling teams distributed across time zones to collaborate in real time.
• Social media and ride-hailing: Real-time map updates, user-generated content at scale, and instant notifications all depend on cloud back-ends.
• Cloud storage: Individuals and businesses alike store documents, photos, and backups without maintaining local hardware.
• Cloud gaming: Games with demanding graphics and physics engines can run on remote servers, streaming rendered frames to players. This removes the need for expensive, rapidly-outdated consumer hardware.
• Enterprise software (SaaS): CRM, ERP, HR, and analytics platforms are increasingly delivered as cloud services rather than on-premises installations.

Deployment Models: Public, Private, and Hybrid Cloud

Organizations choose from several cloud deployment models depending on their cost, compliance, and flexibility requirements:

• Public cloud: Infrastructure is owned and operated by a third-party provider (such as AWS, Azure, or Google Cloud) and shared across many customers. It offers elastic scaling, broad service catalogs, and a pay-as-you-go pricing model.
• Private cloud: Infrastructure is dedicated to a single organization, either hosted on-premises or in a colocation facility. It provides greater control and can simplify compliance with data-residency regulations.
• Hybrid cloud: A combination of public and private environments, connected to allow workloads to move between them. This is the model most large enterprises have settled on, balancing the cost and agility of public cloud with the security and control of private infrastructure.

According to analyst Bob O’Donnell, president and chief analyst at TECHnalysis Research, companies on average use approximately three different public-cloud providers — a pattern often called multi-cloud. O’Donnell also noted that while cloud-native companies are well established, many traditional businesses were still in the early stages of migration as of early 2020.

Market Size and Growth

As of early 2020, the public cloud market was expanding rapidly:

• Gartner projected the global public cloud market would reach approximately $266 billion in 2020, representing roughly 17 percent year-over-year growth.
• IDC forecast the market would nearly double to around $500 billion by 2023.

Note: These are projections made in early 2020. Actual figures will have evolved since publication.

Major Cloud Providers and Market Share

Amazon Web Services (AWS)

AWS launched in 2006 and established a commanding early lead in cloud infrastructure. As of early 2020, analysts estimated its share of the global public-cloud market at between 30 and 50 percent. AWS reported 2019 revenues of $35 billion and claims millions of active customers worldwide.

Microsoft Azure

Microsoft is the second-largest cloud provider, with Azure holding an estimated 15 percent market share as of early 2020. Microsoft does not break out Azure revenue as a standalone figure, making precise comparisons difficult, but the cloud division has consistently been Microsoft’s fastest-growing and highest-performing business unit.

A notable development at the time: the US Department of Defense awarded Microsoft its Joint Enterprise Defense Infrastructure (JEDI) contract — worth up to $10 billion over ten years — in October 2019. Amazon disputed the award and sought a court injunction; a federal court temporarily halted work on the contract while the matter was under review.

Google Cloud

Google Cloud held approximately five to six percent of the public cloud market as of early 2020. The company reported $9 billion in Google Cloud revenues for 2019, representing roughly 53 percent year-over-year growth. Google’s cloud strategy emphasizes large-scale data analytics, machine learning capabilities, and tools that facilitate workload portability across multiple cloud environments (multi-cloud and hybrid-cloud interoperability).

Alibaba Cloud

China’s Alibaba Cloud also held around five to six percent of the global market at the time, making it the fourth-largest provider and the dominant player in the Asia-Pacific region.

Key Technology Trends Shaping Cloud’s Future

Artificial Intelligence and Machine Learning

All major cloud providers have invested heavily in managed AI and machine learning services. The scale of cloud infrastructure makes it practical to train large models and run inference workloads that would be prohibitively expensive on-premises. The JEDI contract, for instance, explicitly included AI-driven modernization of US military computing systems as a core objective.

Cybersecurity

Cloud providers’ reputations depend on their ability to protect customer data. All major providers invest substantially in physical security, encryption, identity management, and compliance certifications. For many organizations, delegating security to a specialized cloud provider results in a stronger security posture than they could achieve independently.

Edge Computing

As 5G networks roll out and connected devices proliferate — in vehicles, factories, homes, and cities — there is growing demand to process data close to where it is generated rather than routing it to a distant data center. This approach, known as edge computing, reduces latency and bandwidth consumption and is considered essential for applications such as autonomous vehicles and industrial automation.

Cloud providers have been partnering with telecommunications companies to bring compute capacity closer to users. AWS partnered with Verizon, and Microsoft with AT&T, to support edge deployments that extend cloud services to the network edge.

Environmental Considerations

Large-scale data centers consume significant amounts of electricity, primarily for cooling and powering servers. As cloud adoption grows, so does scrutiny of its environmental impact. Major providers have announced goals around renewable energy sourcing and improving power usage effectiveness (PUE). Consolidating workloads in efficient hyperscale data centers can also reduce per-workload energy consumption compared with running equivalent workloads across many smaller, less efficient on-premises environments.

Summary

Cloud computing is no longer an emerging technology — it is foundational infrastructure. For DevOps and platform engineering teams, the practical implications are significant: the cloud enables faster provisioning, infrastructure-as-code, global distribution, managed services that reduce operational overhead, and the ability to scale capacity to match demand. Understanding the competitive landscape, deployment models, and emerging trends such as edge computing and AI integration is essential for making informed architectural decisions.

Application performance management is different than container management in myriad ways. Don’t confuse the two.

It’s scary how quickly widespread adoption was achieved for container technology in the world’s mission-critical business applications. Consider that it took about 10 years after the introduction of Java for the programming language to become a near-ubiquitous standard (followed shortly by C#/.Net). In stark contrast, enterprise-ready Docker just turned 5 years old, and it’s been pretty much everywhere for a while.

Why so relatively quick? Are containers that much easier to deploy, control and manage than traditional Java app servers were? Uh, no! In fact, containers create a whole mess of control and management issues (especially performance management) that even the well-established application performance management (APM) companies struggled to create any visibility from inside containerized applications.

But when DevOps teams considered how useful and powerful containers were for deploying and operating microservice applications, collectively they figured it was worth the pain. To achieve the efficiencies of deployment, they were willing to put up with the management headaches associated with operating them, from over-committing infrastructure to application hangs and a lack of visibility, even with (or especially with) APM solutions.

Enter Kubernetes
And then came orchestration; more specifically, along came Kubernetes. While not the first or only system designed to manage containerized systems, Kubernetes simply rolled over the alternatives. How quickly did that occur? Quicker than you can spell Kubernetes—and I mean spelling it “K – 8 – S.” In what seemed to be one announcement on top of another, every cloud service provider announced its own enterprise version of Kubernetes. These enterprise distributions were announced by IBM, Microsoft, Red Hat, Amazon, Google and others; VMware announced it even before its cloud was available. Other orchestration tools simply didn’t know what hit them, and really didn’t stand a chance.

Kubernetes Becomes King of the Hill
Having a standard, especially when it comes to managing infrastructure (and especially application infrastructure), makes everybody happy. Thus, Kubernetes continued to win over the community—or to be more specific, communities—of developers and operations. K8S provided some semblance of control in the wild, wild west of containers and microservices. As a bonus, the multitude of distribution choices satisfied another requirement of modern application delivery teams—the desire to avoid vendor lock-in—pretty much for any part of their environment.

But even with the enterprise versions of K8s (which now includes newer distributions Pivotal PKS and Rancher in its ranks), container “management” is missing two major aspects of delivering applications that meet service requirements. The first gap is performance management; specifically, performance monitoring of the services running in Kubernetes. The second is a lack of understanding how a specific set of services, hosts, deployments and pods contribute toward the overall composition of a specific application. There’s a little chicken-and-egg problem there, but the salient point is that the orchestration system manages resources across containers—not applications and not application performance.

Orchestration certainly makes containers more malleable and enterprise-ready, but without visibility into application performance, service owners can’t prove (or even know) how well the application is actually servicing customer needs. To answer that question, the operations, DevOps, and/or development teams must have a way to tie application performance directly to Kubernetes (and Kubernetes orchestrated applications).

Ignoring Application Performance is Asking for Trouble
One could choose to simply ignore questions of how the application is performing: Is the app meeting its promised service levels (SLAs)? Does it scale appropriately? Is the response time fast enough? After all, if your applications are running properly, wouldn’t you be able to tell through the overall infrastructure usage and container status? (Hint: That’s a trick question—the answer is, “Not on your life!”)

Think of the last time you were in a meeting with the CIO (or maybe you’re the CIO meeting with the application owner). When she asked how the apps were doing, I’m guessing the answer, “Well, our resource usage is normal,” somehow would not be a satisfactory answer.

The onus for application performance management has shifted over the years. When the first APM tools appeared, it was simply a matter of survival to keep the apps running, getting visibility into production code running on Java application servers. Only the largest of organizations could afford (or even consider) to purchase APM solutions.

Today, even the tiniest startups rely on their business applications to conduct their business. Application performance and availability, troubleshooting and optimization are not nice-to-have luxuries of the Fortune 50—every ops team and development team has the need to see the performance of services and applications.

A Lesson to Learn From Capacity Management (and Capacity Planning)
One of the interesting anecdotes I like to use when discussing the evolution of IT operations is to talk about the difference between the “old school” practice of capacity planning and the ITIL/ITSM practice of capacity management.

Capacity planning was the exercise of figuring out the configuration, number and size of servers (physical servers, no less) needed to execute the application being delivered to operations. The answer was static—CPU/core counts, I/O bandwidth, storage size, memory size—and a count. The planner was (mayyyyybe) thinking about the concept of providing resources for the application to run properly, but all they had to go on was an estimate of compute resources needed by the programmer.

Then along came the concept of capacity management. First of all, capacity management was not an exercise with a hard beginning and end. Capacity management was fluid, always in play. Why? Sure, the capacity manager determines the exact same resource configuration—CPUs, memory, hard drive, network. The difference is the phrase that follows the answer, “In order to meet our scalability targets (1,000 concurrent users) and performance targets (all transactions less than half a second) …”

Capacity management is an always-active, always-questioning and always-reporting discipline. It requires adjustments to “the answer” depending on what’s happening with the applications.

Kubernetes and Application Performance Management
To ensure that orchestrated microservice applications from single mobile apps to complex financial suites are running at optimal performance levels, it’s critical to include application performance to the Kubernetes mix. Analogous to capacity management, K8s with APM provide resource allocation and management to meet your application scalability and performance targets. You can’t meet them if you’re not measuring them.

The final piece of the puzzle is picking an APM tool that can handle the three most difficult aspects of orchestrated container applications:

Visibility into containers, their stack and the performance characteristics of the services running on them (throughput, error rate and latency).
Observability in Kubernetes, understanding how the orchestration system is related to the services running on it.
Dealing with the complexity and constant change in microservice environments, being able to filter out the noise and show stakeholders everything they need to see about components and services they’re responsible for.
Conclusion
If you’re going to run microservices, you’re probably running them in containers. And if your application is critical to your business, you should be orchestrating your containers. Kubernetes is a good choice since enterprise versions are included with almost any cloud provider. Always keep an eye on your application’s performance with a modern application monitoring solution that supports the observability of your Kubernetes-orchestrated environments.