It’s important to remember, however, that there is much more to cloud computing than simply storage, including software-as-a-service (SaaS), platform-as-a-service, and more. This provides investors with a multitude of ways to profit from the accelerating move to the cloud.

With a large and growing list of cloud-native products and services, investors are looking for the best ways to capitalize on this opportunity and ride the increasing wave of cloud adoption. These three companies offer investors clear and ongoing opportunities to profit from the trend.

1. HubSpot: Rapidly expanding its suite of services
While it began life as an inbound marketing specialist, HubSpot (NYSE:HUBS) has quickly evolved and expanded its aspirations to become a one-stop-shop for customer relationship management (CRM).

The company has copied liberally from the successful template employed by salesforce.com, increasing its functionality with a series of successful, snap-on acquisitions. In fact, HubSpot has made five multi-billion-dollar acquisitions — as well as a multitude of smaller ones — over the past five years alone.

As a result of this platform expansion, HubSpot now provides marketing, sales, and service support, as well as content management and operations products. Each new offering has resulted in a growing market opportunity, with some estimates placing HubSpot’s total addressable market at about $20 billion. The company currently has roughly 114,000 customers, which pales in comparison to its total addressable market of more than 3 million small- and medium-sized businesses with a website.

The proof is in the pudding. During the first quarter, HubSpot delivered revenue that grew 41% year over year. Subscription revenue also climbed 41%, while professional services revenue jumped 43%. Additionally, the company’s total customer count grew by 45% and spending per customer was on par with the prior-year quarter.

These results aren’t a one-time thing, either. Since late 2014, HubSpot’s revenue has grown at a compound annual growth rate of 41%, while its international revenue has grown at 58%. At the same time, its margins have continued to expand and its free cash flow has rocketed higher.

2. NVIDIA: Providing the processing power that unpins cloud computing
While investors might not immediately think of NVIDIA (NASDAQ:NVDA) when considering cloud computing investments, its cutting-edge processors have become the industry standard for powering cloud computing and data center operations.

The same graphics processing units (GPUs) that render lifelike images in video games also underpin the unique challenges presented by cloud computing. The secret is the parallel processing, or the ability to conduct a multitude of complicated mathematical calculations simultaneously, thereby reducing the amount of time necessary to complete complex tasks and speeding up the most complex algorithms.

Each of the world’s largest cloud providers depends on NVIDIA state-of-the-art processors to power their systems. Its customer list reads like a Who’s-Who of cloud providers. They include Amazon’s AWS, Alphabet’s Google Cloud, International Business Machines’ IBM Cloud, Microsoft’s Azure, and Alibaba Cloud, just to name a few.

Yet NVIDIA has just begun to tap this massive opportunity, as evidenced by its most recent results. In the first quarter, data center revenue — which includes cloud computing, data center, and artificial intelligence applications — grew 79% year over year, accounting for 36% of NVIDIA’s total sales. At the same time, its gaming segment, which represents the lion’s share of its revenue, grew 106%. Total revenue grew 84%, a remarkable feat for a company with a market cap of $436 billion — and this could be just the beginning.

NVIDIA generated revenue of $16.68 billion in 2020, which pales in comparison to the company’s total addressable market, which management estimates will reach $250 billion over the next few years.

3. DocuSign: Digital signatures are just the beginning
Many investors are familiar with DocuSign (NASDAQ:DOCU), which provides cloud-based tools to sign documents. The company is the undisputed leader in the electronic signature space, with a whopping 70% share of a market that continues to grow. What investors may not know is that e-signatures are just the beginning of a large and growing opportunity for DocuSign, a fact that CEO Dan Springer is quick to point out.

“Typically, e-signature is the first step that many customers take on their broader digital transformation journey with us,” said CEO Dan Springer on a recent earnings call. “So from a financial point of view, we believe this surge in e-signature adoption bodes well for future Agreement Cloud expansion.”

The Agreement Cloud is the next step in DocuSign’s evolution, providing a dozen software applications and more than 350 integrations designed to manage the entire lifecycle of contracts and agreements. For example, once a contract is signed, the system can trigger the creation of a bill. An agreement up for renewal? A reminder is generated. There are hundreds of similar use cases including employee on-boarding, non-disclosure agreements, change orders, new customer forms, vendor/supplier agreements, and much more.

The strategy is not only working, but the business is on fire. For DocuSign’s fiscal 2022 first quarter (ended April 30, 2021), revenue grew 58% year over year, accelerating from 39% growth in the prior-year quarter. Perhaps more importantly, subscription revenue grew at an even faster pace, up 61%. At the same time, billings — a sales growth metric that factors in changes in deferred subscription revenue — climbed 54%. Adjusted earnings per share (EPS) more than tripled.

DocuSign generated total revenue of $1.5 billion last year, but its remaining opportunity is vast. With the addition of the Agreement Cloud, DocuSign management estimates that its total addressable market has doubled to more than $50 billion, leaving a long runway for growth.

You get what you pay for
Each of these stocks has vastly outperformed the market over the past year, with no signs of slowing. Bears will be quick to point out that these stocks are by no means cheap when measured using traditional valuation metrics. HubSpot, NVIDIA, and DocuSign are currently selling for 23, 19, and 18 times sales, respectively — when a good price-to-sales ratio for a stock is generally between one and two.

That said, each of these cloud-native companies has a strong position within its industry and is riding the secular tailwind of cloud adoption to new heights. As a result, investors have thus far been willing to pay up for the impressive topline growth and the promise of explosive profits that each represents.

Should you invest $1,000 in NVIDIA Corporation right now?
Before you consider NVIDIA Corporation, you’ll want to hear this.

Our award-winning analyst team just revealed what they believe are the 10 best stocks for investors to buy right now… and NVIDIA Corporation wasn’t one of them.

Cloudera has announced a collaboration with NVIDIA, which will enable the Cloudera Data Platform (CDP) to integrate the RAPIDS Accelerator for Apache Spark 3.0, deployed on NVIDIA’s computing platforms.

The RAPIDS Accelerator for Apache Spark 3.0 will enable enterprises to create data pipelines around artificial intelligence (AI) and machine learning (ML), without the need to change code, whilst delivering analytics and insights.

According to Cloudera, datasets are growing larger and used for more purposes, such as responding to fraud, developing products, and transforming the supply chain. However, that data creates bottlenecks for data scientists who cannot work quickly enough to train and operate models across their organisations.

Organisations can use an open source, GPU-accelerated platform, such as Apache Spark 3, to train models using AI and ML. As Cloudera Data Platform now supports this functionality, there are more opportunities for high-performance compute, data science, and AI support from research right through to production.

Cloudera chief product officer Arun Murthy says, “At a time when speed is everything, businesses are relying on the power of data more than they ever have. Our collaboration with NVIDIA will give customers the rocket fuel they need to better understand their data and realise the true transformational potential of AI.”

Murthy adds that the deeper integration with NVIDIA is the natural next step for the company.

NVIDIA data science product group senior director Scott McClellan adds, “Apache Spark is a cornerstone of the machine learning and data analytics pipelines enterprises rely on to remain competitive.”

“The processing power of NVIDIA-accelerated computing and Spark analytics running on Cloudera Data Platform provides the flexibility to meet deadlines when time is of the essence, and save on costs when the bottom line is most important.”

RAPIDS Accelerator for Apache Spark will be available in CDP Private Cloud this year. Cloudera and NVIDIA will also roll out other offerings over time, including Accelerated Deep Learning and Machine Learning in CDP Public Cloud in May.

Earlier this month Cloudera announced that CDP will be available through Google Cloud Platform. CDP on Google Cloud helps to create data lakes within any organisation’s Google Cloud. This enables organisations to run analytics and machine learning, migrate pipelines to Google Cloud, and create new pipelines.

“Cloudera Data Platform on Google Cloud is an ideal first step for our joint customers to support hybrid cloud,” says Murthy.

Nvidia Corp kicked off its annual GTC Technology conference on Monday with some big new product announcements, including the latest version of its DRIVE autonomous vehicle platform that will transform self-driving vehicles into “supercomputers on wheels.”

During today’s opening keynote of the annual GPU Technology Conference, NVIDIA founder and CEO Jensen Huang unveiled NVIDIA DRIVE Atlan, the upcoming generation of its AI-powered compute platform for autonomous vehicles. Atlan centralizes the vehicle’s entire computing infrastructure into a single system-on-a-chip (SoC).

Atlan will be able to handle the large number of applications and deep neural networks that run simultaneously in autonomous vehicles.

Nvidia said its next-generation Atlan platform will achieve an unprecedented 1,000 trillion operations per second (1,000 TOPS) of performance and an estimated SPECint benchmark score of more than 100, which is greater than the total compute power in most robotaxi vehicles in development today and four times faster than Nvidia’s Orin processor.

Atlan will leverage NVIDIA’s latest GPU architecture, new Arm CPU cores and deep learning and computer vision accelerators. The platform architecture provides ample compute horsepower for the redundant and diverse deep neural networks that will power future vehicles, while leaving headroom for developers to continuously add new features and improvements.

“To achieve higher levels of autonomy in more conditions, the number of sensors and their resolutions will continue to increase,” Huang said. “AI models will get more sophisticated. There will be more redundancy and safety functionality. We’re going to need all of the computing we can get.”

Over the past several years, Nvidia has become a leading developer of hardware for autonomous vehicles around the world, due to the robust processing capabilities of its SoCs.

The company’s DRIVE platform is a software-defined, end-to-end platform for the transportation industry that enables continuous improvement and continuous deployment through over the air updates. It delivers everything needed for the development of autonomous vehicles at scale.

Autonomous vehicles equipped with dozens of sensors and AI-powered software for navigation require an incredible amount of processing power and Nvidia’s DRIVE family of products is designed to meet this need.

Automakers and startups around the world have turned to Nvidia to supply the hardware necessary to support the development of robust and safe autonomous driving technology powered by AI. Vehicles from Mercedes Benz, Toyota, Volvo, Audi and others are using Nvidia processors.

The open DRIVE Software stack allows developers build perception, mapping, planning, and driver monitoring capabilities. Using the power of GPUs and AI, developers can train deep neural networks that support these highly advanced systems.

Like the Nvidia DRIVE Orin, the next-gen platform is software compatible with previous DRIVE compute platforms, allowing customers to leverage their existing software investments across multiple product generations.

Atlan will come with Nvidia’s BlueField data processing unit (DPU) for enhanced security

Atlan is also the first SoC to be equipped with Nvidia’s BlueField data processing unit (DPU) for additional security, advanced networking and storage services. Nvidia’s BlueField data processing unit will connect an autonomous vehicle directly to Nvidia’s powerful data centers.

BlueField is a data center services accelerator on a chip, delivering up to 400 gigabits per second (Gb/s) of Ethernet and InfiniBand (IB) connectivity for both traditional applications and modern GPU-accelerated workloads while freeing host CPU cores to run applications instead of infrastructure tasks.

The NVIDIA BlueField DPU is designed to handle the complex compute and AI workloads required for autonomous vehicles. It’s fully programmability to deliver “zero-trust” security to prevent data breaches and cyberattacks.

While vehicles are being packed with more computing technology, they’re lacking the physical security that comes with data center-level processing, according to Nvidia. The company claims that Atlan is a technical marvel for safe and secure AI computing, fusing all of NVIDIA’s technologies in AI, automotive, robotics, safety and BlueField data centers.

Data center development is critical to deploying robotaxis at scale. The ability to operate in thousands of conditions around the world requires intensive deep neural network training requiring massive amounts of data.

By combining data center and in-vehicle solutions, autonomous vehicle developers can create a continuous, end-to-end development cycle. As deep neural networks learn new capabilities in the data center, the validated algorithms can be pushed to the car’s compute platform via an over the air software update.

Cars and trucks of the future will require an optimized AI architecture not only for autonomous driving, but also for intelligent vehicle features like speech recognition and driver monitoring systems. Upcoming software-defined vehicles will be able to converse with occupants answering questions, providing directions and even warn of road conditions ahead.

Nvidia is working with Mercedes Benz on the next generation of software-based vehicles that support over the air updates for the life of the vehicle. The advanced software-based vehicle architecture will be introduced beginning with 2024 model year vehicles, eventually rolling out to the entire Mercedes Benz fleet globally.

Nvidia and Mercedes Benz plan on developing the most sophisticated and advanced computing architecture ever deployed in the auto industry.

The new software-defined architecture will be built on Nvidia’s Orin DRIVE platform. Eventually, every future Mercedes Benz model globally will come with the advanced and upgradable software-based architecture, from the entry-level A-Class to S-Class models.

“Every future Mercedes- Benz with the NVIDIA DRIVE system will come with a team of expert AI and software engineers continuously developing, refining and enhancing the car over its lifetime,” said Huang last year when the partnership with Mercedes Benz was announced.

Nvidia hardware is also powering the autonomous driving systems for the new P7 sedan from Chinese electric vehicle startup and Tesla challenger XPeng.

Nvidia will first deliver its DRIVE Orin processors with vehicle production timelines starting in 2022. Orin will be followed by Atlan with sampling in 2023. It will be ready for production vehicles in 2026.

AI is one of the most significant trends this decade. These systems revolutionized the user interface by reversing the dynamic that required users to learn how to work with computers to one where the computers learned how to work with users. Once mature, these platforms should massively reduce the time needed to train users on new systems while increasing the effectiveness of those systems, the timeliness of the information, and the answers these systems subsequently provide.

Initial AI systems tended to be proprietary, expensive, difficult to train, and often disappointing in use. The future is cloud-based AI solutions using software like NVIDIA’s Ai Enterprise software suite running on something like VMware’s vSphere. Thas it just what NVIDIA and VMware announced this week: an One Suite AI package for the enterprise with a blend of tools and frameworks optimized for a broad range of industries including manufacturing, logistics, financial services, retail, and healthcare.

This blended solution should in turn result in individual AI applications uniquely targeting critical business needs in the target industries initially and a growing number of other industries eventually.

Let’s talk about the impact of this partnership between NVIDIA and VMware running on uniquely tuned near bare-metal VIDIA-Certified systems from companies like Dell Technologies, HPE, Lenovo, and Supermicro initially.

THE IMPACT OF BROADLY AVAILABLE AIS
While we aren’t yet at a point where general-purpose AIs are practical, this effort should provide broad access to AI solutions focused on individual industries.

AI in Manufacturing
In manufacturing, you would get AIs that could monitor quality on the line, and more cost-effectively, do broad quality checks while products are being manufactured. Not only would they be able to point out defects, but also automatically begin the remediation process resulting in better line uptime and fewer products released into the defective market.

Often quality control systems only check on the process that occurred before the test, which can be problematic if that process breaks a previously approved component. When I worked at IBM, we once had a production line with 100% quality assurance based on these point tests and a 100% failure rate at the end of the manufacturing process because of this QC approach. An AI can do a far more complete job of testing the assembled system at each checkpoint, effectively preventing the problem of a late process breaking an earlier installed component.

With logistics, AIs can help certify suppliers, provide early warnings (if the suppliers are instrumented) to product shortages, and provide direction on how to offset a failed supplier or critical lost shipment. With adequate capacity and training, an AI can not only identify problems but recommend and even implement solutions early that could prevent missing delivery dates. Also, they can flag suppliers who are providing substandard parts or flag any part of the supply chain for performance or integrity issues as long as the related process is well-instrumented and tied into the AI.

Financial Services and AI
Financial services could use AIs for customer interaction while fooling the customer into thinking they are talking to a natural person. Natural Language interfaces have advanced dramatically over the last decade, and advanced AI systems like Watson have appeared so natural to customers that customers have tried to ask them out on a date. This personal touch has a real person’s performance, often even better because AIs don’t lose their temper, have a bad day, or make inappropriate remarks. I anticipate a future where significant traders get their own custom AI supplied by their broker, allowing them to anticipate trends better and increase income and avoid catastrophic outcomes.

Retail and AI
Retail AIs will help aggregate products so that a person shopping for one is more likely to buy adjacent products that meet their buying needs. Coupled with mixed reality, they could also put the user through virtual fashion shows and virtually show what the buyer would look like in a new outfit.

Beyond that, buyers who work with the retailer for a long time will gain the benefit of an AI that knows their taste and recommend products that, had the buyer known about them, they would have already purchased. This solution would result in better revenue for the retailer and better satisfaction and loyalty in customers.

Healthcare and AI
Finally, with healthcare, everyone is different, yet today we are often treated like we are the same because medical professionals can’t become experts on every patent they have. But one advantage to AIs is they can scale to everyone, and AIs tied to future patents should be better able to anticipate future medical needs and make sure the tests the patent has to take are uniquely targeted at those needs. Hence, you were less likely to be under or over-tested. This focus increases the timeliness and accuracy of the diagnosis while potentially significantly lowering the cost of treatment.

THE DAWN OF UBIQUITOUS AI
Partnerships like the one forged between NVIDIA and VMware promise more capable and more numerous focused AIs that could significantly improve customer/vendor relations and improve the quality of the products and services enhanced by these AIs. It paves the way to a future where significant industries are heavily supplemented with AIs; mistakes are less common because the related decisions are more often based on data, not gut.

We are entering a future where we no longer have to learn how to use technology, and instead, technology has to learn how to work with us; this announcement made that outcome more certain and more near term. That is the promise of ubiquitous AI and NVIDIA, and VMware just got us a ton closer to that ideal.

US-based tech majors Nvidia and VMware on Tuesday announced a collaboration to virtualise Artificial Intelligence (AI) workloads on VMware vSphere with Nvidia AI Enterprise.

Nvidia unveiled AI Enterprise, a comprehensive software suite of enterprise-grade AI tools and frameworks, exclusively with VMware.

The offering gives enterprises the software required to develop a broad range of AI solutions, such as advanced diagnostics in healthcare, smart factories for manufacturing, and fraud detection in financial services.

“Nvidia AI Enterprise enables customers to reduce AI model development time from 80 weeks to just eight weeks, and allows them to deploy and manage advanced AI applications on VMware vSphere with the same scale-out, record-breaking Nvidia accelerated computing performance that’s possible on bare metal,” Justin Boitano, VP and General Manager of Enterprise and Edge Computing at Nvidia, said in a statement.

With the Nvidia AI Enterprise software suite, IT professionals at the hundreds of thousands of enterprises that use vSphere for compute virtualization can now support AI with the same tools they use to manage large-scale data centers and hybrid cloud environments.

The Nvidia software suite provides scale-out, multi-node, AI application performance on vSphere that is indistinguishable from bare-metal servers.

The AI Enterprise provides compatibility for a broad set of accelerated CUDA applications, AI frameworks, pre-trained models and software development kits running in the hybrid cloud.

Nvidia has also certified VMware vSphere as the only compute virtualization software to provide hypervisor support for live migration with Nvidia Multi-Instance GPU technology, which allows each A100 GPU to be partitioned into up to seven instances at the hardware level to maximize efficiency for workloads of all sizes.

VMware and Nvidia have expanded their alliance to make it easier for enterprises to run GPU-accelerated AI applications in existing data centre infrastructure.

The virtualisation giant announced the expanded alliance with Nvidia Tuesday, saying that the GPU maker has exclusively certified VMware’s vSphere 7 Update 2 to support the new Nvidia AI Enterprise offering, a software suite of enterprise-grade AI tools and frameworks that enables Nvidia GPU-accelerated applications to run in virtual machines and containers.

VMware’s vSphere 7 Update 2 is also introducing support for Nvidia’s A100 Tensor Core GPU and its multi-instance GPU feature, which will allow data centre operators to split up individual A100s into as many as seven distinct instances for use by multiple users.

Nvidia said it developed Nvidia AI Enterprise alongside VMware as part of a first-of-its-kind industry collaboration that will allow enterprises to move AI applications from bare-metal servers to virtualized environments with very little performance impact. The suite includes solutions that support a broad range of industries, from health care and manufacturing to financial services.

Justin Boitano, vice president and general manager of Enterprise and Edge Computing at Nvidia, told CRN USA that the vSphere exclusivity agreement means that the Nvidia AI Enterprise offering is not available on any other virtualization platform. He declined to say when the exclusivity agreement would expire.

“We’re working together to make this as easy to consume as possible for enterprises that want to go on this journey of using AI to improve efficiency,” he said in a briefing with journalists last week.

The new software certification work means that Nvidia AI Enterprise is primed to run on servers from Dell Technologies, Hewlett Packard Enterprise, Supermicro and other server makers that has been certified through the GPU maker’s recently launched Nvidia-Certified Systems program.

Lee Caswell, vice president of marketing of VMware’s cloud platform business unit, said the new collaboration around Nvidia AI Enterprise “is going to be incredibly powerful in reducing both real and perceived risk” when it comes to running AI applications in data centres.

“We’re doing the work behind the scenes to reduce the real risk through certification, integration, testing, and then perceived risk is how we come to market together to show that our companies are both ready to support the joint solution,” he said.

Boitano said Nvidia AI Enterprise can significantly reduce the amount of work required for companies to deploy AI applications into production environments. For instance, he said, Nvidia’s Transfer Learning Toolkit, which is available in the suite, can reduce the amount of time it takes for a company to build an AI model from 80 weeks to eight weeks.

“We’re taking this cloud-native deployment to heart in trying to make sure everything’s containerized and can be orchestrated in cloud native ways going forward to make setup and deployment easy,” he said, adding that support for Nvidia Enterprise AI will eventually come to vSphere Tanzu for running AI applications in containers from the virtualization platform.

Nvidia AI Enterprise is now available as a perpetual license at US$3,595 per CPU socket, and enterprise business standard support for the software suite is US$899 a year per license