Cloud computing giant Amazon Web Services, a unit of Amazon.com (Nasdaq: AMZN) announced on Friday that it would open an infrastructure region in Israel in the first half of 2023. The company recently won the Israeli government’s “Nimbus” computing tender along with Google Cloud. The two companies were selected by the Ministry of Finance and the Government Computer Authority as providers of cloud services to the government of Israel and the IDF for the next seven years, in a tender estimated to be worth some NIS 4 billion.

After losing in the tender, software giant Microsoft, with its Azure cloud computing service, and computer technology company Oracle, filed administrative petitions with the aim of leaving an opening for a future legal appeal against the results of the tender, but no direct legal proceedings are taking place to annual the outcome. Microsoft and Oracle stress the fact that they are continuing their efforts to set up datacenters in Israel.

Oracle announced that it would open its cloud center located in the Jerusalem area this summer. Microsoft, which announced in early 2020 that it would open its datacenter in 2021, later announced that it would open only in the first half of 2022. Google, which has also officially announced that it will set up a regional cloud datacenter, has not stated when it will start to operate.

Amazon’s regional cloud, like the others, is not intended for government use only, but for the entire Israeli economy. In its announcement on Friday the company stated: “The AWS Israel (Tel Aviv) Region will enable even more developers, startups, and enterprises as well as government, education, and non-profits to run their applications and serve end-users from data centers located in Israel. Additionally, the government of Israel announced that it has selected AWS as its primary cloud provider as part of the “Nimbus” contract for government ministries and subsidiaries. The Nimbus framework will provide cloud services to Israeli government ministries including local municipalities, government-owned companies, and public sector organizations with the aim of helping to accelerate digital transformation. It will be instrumental in driving innovation and enabling new digital services for the citizens of Israel.”

In August 2020, it was announced that AWS was working on setting up datacenters at three sites in Israel, each with an output of 16 megawatts. AWS began to advance the project together with Azrieli Group Ltd. (TASE: AZRG) and US company Compass. The sites, according to the report, are at Bet Shemesh, Shoham, and in Hefer Valley. The real estate deals involved were estimated at NIS 300-400m, and the project as a whole at about NIS 1 billion.

Accountant General Yali Rothenberg said, “”We congratulate AWS on their winning bid in the first tender of Project Nimbus, a multi-year flagship project led by the Israeli Government Procurement Administration to provide a comprehensive framework for the provision of cloud services to the Government of Israel”

“The new AWS Israel (Tel Aviv) Region will empower more public and private institutions, innovative startups, and global companies to deliver built-for-the-cloud applications that help fuel economic development across the country.

“It is the latest in our list of AWS Regions across Europe and the Middle East, which includes existing regions in Bahrain, Ireland, Italy, France, Germany, Sweden, and the UK as well as regions under development in Spain, Switzerland, and United Arab Emirates,” said Peter DeSantis, Senior Vice President of Global Infrastructure, AWS.

“The new region is a continuation of our investment to support enterprises of all kinds, help startups scale and grow, enable technical skills development, and create cloud literacy,” DeSantis continued. “Cloud technology is at the heart of the Israeli government’s digital transformation program, and their approach highlights the importance of setting a strong course for cloud adoption and leading by example to re-invent citizen services.”

AWS, considered the world’s leading cloud services provider, set out in its announcement how its global datacenter network is structured, and its advantages for users. “AWS Regions are comprised of Availability Zones, which place infrastructure in separate and distinct geographic locations with enough distance to significantly reduce the risk of a single event impacting customers’ business continuity, yet near enough to provide low latency for high availability applications that leverage multiple Availability Zones. Globally, AWS has 81 Availability Zones across 25 geographic regions, with plans to launch an additional 21 Availability Zones and seven AWS Regions in Australia, India, Indonesia, Israel, Spain, Switzerland, and the United Arab Emirates.

“Each Availability Zone has independent power, cooling, and physical security and is connected through redundant, ultra-low-latency networks. AWS customers focused on high availability can design their applications to run in multiple Availability Zones and across multiple regions to achieve even greater fault tolerance.

“The addition of the AWS Israel (Tel Aviv) Region will enable local customers with data residency requirements to store their data in Israel with the assurance that they retain control over the location of their data. Organizations using this region will also be able to access advanced technologies from the world’s leading cloud with the broadest and deepest suite of cloud services to drive innovation including analytics, compute, database, Internet of Things (IoT), machine learning, mobile services, storage, and more.”

Like other cloud companies, AWS provided a list of some of its most prominent customers, among the Bank Leumi, Migdal, CyberArk, Fiverr, monday.com, Hashavshevet (Wizsoft), the Rami Levy group, and the Sourasky Medical Center in Tel Aviv (Ichilov Hospital).

AWS has a Partner Network (APN) Partners consisting of tens of thousands of Independent Software Vendors (ISVs) and Systems Integrators (SIs) around the world. AWS Partners build innovative solutions and services on AWS, and the APN helps by providing business, technical, marketing, and go-to-market support. APN SIs, consulting partners, and ISVs working in Israel help enterprise and public sector customers migrate to AWS, deploy mission-critical applications, and provide a full range of monitoring, automation, and management services for customers’ environments on AWS.

According to the company’s announcement, among AWS Israel Partners are Alcide, AllCloud, AppsFlyer, Automat-IT, BigPanda, Bringg, Checkmarx, CloudBuzz, Matrix CloudZone, Comm-IT, Continuity Software, CloudRide, DoIT International, Guardicore, Kaltura, MidLink, Namogoo, Nice, Orca Security, Perimeter81, Quali, Radware, Sapiens, TeraSky, and Trax Retail.

The collaboration with AllCloud, for example, was reported in December 2020, and includes investment of some $30 million in transferring Israeli companies to AWS cloud services.

The regional cloud represents a continuation of AWS’s activity in Israel, which includes Amazon CloudFront edge locations. Amazon CloudFront is described as “a highly secure and programmable Content Delivery Network (CDN) that accelerates the delivery of data, videos, applications, and APIs to users worldwide with low latency and high transfer speeds.”

In 2020, AWS also launched AWS Outposts and AWS Direct Connect in Israel. AWS Outposts is a fully managed service that offers the same AWS infrastructure, AWS services, APIs, and tools to virtually any data center, co-location space, or on-premises facility for a truly consistent hybrid experience. AWS Direct Connect makes it easy to establish a dedicated network connection from customers’ on-premises infrastructure to AWS infrastructure. Using AWS Direct Connect, customers can establish private connectivity between AWS and their data center, office, or colocation environment, which can reduce their network costs, increase bandwidth throughput, and provide a more consistent network experience than internet-based connections.”

The company also mentioned its AWS Activate program for cooperation with startups in Israel, which has been in operation since 2013. “This program gives startups access to guidance and one-on-one time with AWS experts as well as web-based training, self-paced labs, customer support, third-party offers, and up to $100,000 in AWS service credits – all at no charge. This is in addition to the work that AWS already does with the venture capital community, startup accelerators, and incubators to help startups grow in the cloud. In Israel, AWS works with accelerator organizations such as 8200 EISP, F2 Venture Capital – the junction, and TechStars as well as venture capital firms like Aleph, Pitango, TLV Partners, and Viola Ventures to support the rapid growth of their portfolio companies,” the AWS announcement said.

BigPanda Inc. and Datadog today announced they will integrate their respective platforms to make it easier to form artificial intelligence for automating IT operations (AIOps) to consume data generated by the Datadog IT monitoring service. The two companies are pledging to invest in a go-to-market partnership to create a comprehensive platform for monitoring, analytics and incident management.

As part of that effort, Datadog is committing to making available an “integration tile” through which BigPanda will also be able to feed root cause analytics based on machine learning algorithms into the Datadog console. That capability will be enabled by a service-to-service topology map created by Datadog that will be used to drive event enrichment, correlation, impact analysis and prioritization.

Mohan Kompella, vice president of product marketing for BigPanda, said Datadog affords his company a unique opportunity to better train AI models using a massive amount of IT and security metrics that Datadog aggregates daily. That data can then be combined with data ingested from other IT service management systems, which will enable IT operations to automate processes using better-trained AI models, added Kompella. Thanks to modern application programming interfaces (APIs), Kompella noted it’s become a lot easier for platforms such as BigPanda to consume a wide range of data.

The more data an AI model is exposed to the faster it learns about the IT environment an organization is seeking to automate. Each IT environment is unique, so it usually takes some time for AI models to be optimally tuned. The more data that’s made available, the faster the AI platform is able to make accurate recommendations. It’s then up to each IT team to decide to what degree they want the AIOps platform to automate a task based on those recommendations versus implementing themselves. The more accurate the recommendations become over time, the more confidence IT teams gain in the AIOps platform.

Of course, IT environments tend to change rapidly and evolve in the DevOps era. The alliance with Datadog will provide BigPanda with a primary source of data from tools that are already widely employed by DevOps teams.

Ultimately, the goal is to reduce the mean time to resolution for IT incidents, in addition to eliminating as much as possible the need to invite developers and IT operations teams to a “war room” meeting to discover the root cause of an issue. Many IT teams these days find themselves spending weeks trying to ascertain the root cause of an issue that, once discovered, only takes a few minutes to fix. AIOps platforms afford IT teams the opportunity to eliminate a lot of friction that currently exists in those complex IT environments that have scaled beyond the ability of humans to manage without some AI assistance.

In the wake of the economic downturn brought on by the COVID-19 pandemic, interest in AIOps is clearly on the rise. IT environments continue to grow in size as the enterprise IT becomes more extended. It won’t be long before most IT teams expect to be able to leverage AI to manage highly distributed computing environments. It may, of course, be a while before IT teams fully trust those platforms; however, it’s also clear IT as a whole is reaching a point where there is no alternative.

What Is AIOps and Why Does It Matter?

AIOps — short for Artificial Intelligence for IT Operations — refers to platforms that combine big data, machine learning, and analytics to automate and enhance IT operations tasks. These tasks include event correlation, anomaly detection, root-cause analysis, and incident management. As enterprise IT environments grow more complex — spanning on-premises infrastructure, multiple clouds, containers, and microservices — traditional monitoring tools struggle to keep pace with the volume and velocity of operational data. AIOps platforms aim to close that gap by filtering noise, surfacing actionable insights, and enabling faster incident response.

Key Vendors in the Global AIOps Platforms Market

The market research report identified the following companies as notable participants in the global AIOps Platforms space as of 2020:

• Splunk — Known for its data platform and ITSI (IT Service Intelligence) product, which applies machine learning to operational data for predictive alerting and service health monitoring.
• Cisco (AppDynamics) — AppDynamics provides application performance monitoring (APM) with AI-driven analytics, acquired by Cisco in 2017.
• Micro Focus — Offers IT operations management (ITOM) tooling with AIOps capabilities through its Operations Bridge product line.
• Zenoss — Provides hybrid IT monitoring and AIOps capabilities, with a focus on dynamic service models and predictive analytics.
• Moogsoft — A specialist AIOps vendor focused on alert noise reduction and incident correlation using machine learning.
• BigPanda — Focuses on event correlation and alert enrichment to reduce mean time to detect (MTTD) and mean time to repair (MTTR).
• LogicMonitor — A cloud-based infrastructure monitoring platform with automated anomaly detection.
• ScienceLogic — Targets hybrid IT and service provider environments with AI-driven topology and event management.
• Microsoft — Azure Monitor and related services incorporate AI and machine learning for cloud-native operations monitoring.
• Appnomic AppsOne — Focused on application-aware AIOps, particularly for performance management.
• Autointelli — Provides intelligent automation for IT operations, including runbook automation and event management.
• CloudFabrix — Offers a data-centric AIOps platform targeting observability pipelines and data fabric for IT operations.
• Federator.ai — Specializes in AI-driven workload prediction and resource optimization, particularly for Kubernetes environments.

Market Segmentation

By Deployment Model

AIOps platforms are typically available in two deployment configurations:

• Cloud-based (SaaS) — Hosted and managed by the vendor. Preferred by organizations seeking rapid deployment, lower upfront cost, and automatic updates. Increasingly the dominant delivery model.
• On-premises — Deployed within the customer’s own data center. Preferred by enterprises with strict data residency requirements, regulated industries, or air-gapped environments.

By Organization Size

• Large Enterprises — Typically the early adopters of AIOps, given the scale and complexity of their IT estates and their greater capacity to invest in enterprise tooling.
• Small and Medium Enterprises (SMEs) — Adoption among SMEs has grown as cloud-delivered AIOps reduces implementation complexity and entry cost.

Regional Market Breakdown

The market research covered the following geographic regions:

• North America — Historically the largest regional market, driven by high IT spending, early cloud adoption, and concentration of leading AIOps vendors.
• Europe — Strong demand driven by digital transformation initiatives and regulatory pressure on IT reliability and security.
• China — Significant and growing market with both domestic and international vendors competing for share.
• Rest of Asia Pacific — Includes markets such as Japan, Australia, India, and Southeast Asia, with varied maturity levels.
• Latin America — An emerging market with growing cloud adoption underpinning AIOps interest.
• Middle East & Africa — Adoption is earlier-stage but growing, particularly in Gulf region enterprise and government IT.

Market Drivers

Several structural trends were identified as driving AIOps adoption:

• Explosion of operational data — Modern IT environments generate far more logs, metrics, traces, and events than human operators can analyze manually. Machine learning is increasingly necessary to extract signal from this noise.
• Hybrid and multi-cloud complexity — Managing workloads across on-premises, private cloud, and multiple public clouds creates monitoring blind spots that AIOps platforms are designed to address.
• DevOps and SRE adoption — Organizations practicing DevOps and Site Reliability Engineering (SRE) require faster feedback loops and automated remediation, both key AIOps use cases.
• Cost pressure on IT operations — AIOps can reduce the manual effort required for incident triage and resolution, helping teams do more with fewer resources.
• Growing tolerance for AI-driven automation — As ML models mature and interpretability improves, IT teams are more willing to act on AI-generated recommendations.

Market Challenges

The report flagged several challenges that could constrain market growth:

• Data quality and integration complexity — AIOps platforms depend on high-quality, normalized data from across the IT estate. Integrating data from legacy monitoring tools, diverse cloud APIs, and on-premises infrastructure is a significant implementation challenge.
• Skills gap — Effectively deploying and tuning AIOps platforms requires a combination of IT operations domain knowledge and data science literacy that many teams lack.
• Proving ROI — Quantifying the business value of reduced alert noise or faster MTTR can be difficult, making budget justification challenging.
• Vendor fragmentation — The AIOps landscape is crowded, with significant variation in platform scope, maturity, and underlying approaches. Evaluating and selecting the right platform requires substantial effort.
• Organizational resistance — Automating IT operations decisions can encounter cultural resistance from teams accustomed to manual processes and concerned about job displacement.
• Model transparency — Many AIOps platforms use black-box ML models. Lack of explainability can erode trust among operations engineers who need to understand why an alert was raised or suppressed.

Competitive Landscape

The AIOps market in 2020 was characterized by a mix of established IT management vendors extending their platforms with AI capabilities (Splunk, Micro Focus, Microsoft) and specialist pure-play AIOps vendors (Moogsoft, BigPanda, Federator.ai). Competition was intensifying on several fronts:

• Platform breadth versus depth — Larger vendors offer broad integrations and bundled licensing, while specialists often provide deeper ML capabilities in specific domains such as alert correlation or capacity forecasting.
• Mergers and acquisitions — The report noted M&A activity as a key competitive dynamic, with larger players acquiring specialist AIOps capabilities to round out their portfolios.
• Ecosystem integration — Platforms that integrate tightly with popular DevOps toolchains (Jira, PagerDuty, ServiceNow, Slack) and cloud-native observability stacks (Prometheus, Grafana, OpenTelemetry) tend to see faster adoption.

Research Methodology Used in the Original Report

According to the source, the original market research employed the following analytical frameworks:

• PESTLE analysis — Examining Political, Economic, Social, Technological, Legal, and Environmental factors influencing the market.
• Porter’s Five Forces analysis — Assessing competitive intensity, supplier power, buyer power, threat of substitutes, and barriers to entry.
• Primary and secondary research — Combining vendor interviews and surveys with desk research across industry publications and financial filings.

Key Questions for AIOps Platform Evaluators

Whether you are evaluating the AIOps market for investment purposes or assessing platforms for your own organization, the following questions are worth addressing:

1. Which data sources does the platform ingest natively, and how does it handle custom or legacy sources?
2. How does the platform explain its ML-driven recommendations to operations engineers?
3. What is the typical time-to-value for a deployment of similar scale and complexity?
4. How does the vendor price the platform — by data volume, host count, users, or modules?
5. How does the platform integrate with your existing ITSM, alerting, and on-call tooling?
6. What remediation or automation capabilities are built in, and what requires external orchestration?
7. How does the vendor’s roadmap address cloud-native and FinOps use cases?

Summary

The AIOps platforms market was already competitive and growing by 2020, with a diverse set of vendors ranging from broad IT management incumbents to focused ML-native startups. The structural drivers — data volume, hybrid cloud complexity, and DevOps acceleration — have only intensified since then. Organizations evaluating AIOps should prioritize integration depth, explainability, and clear ROI metrics alongside raw ML capability. The vendor landscape continues to consolidate through M&A, making platform longevity and vendor financial stability important evaluation criteria.

Original market research attributed to QYResearch, summarized via weeklywall.com (March 2020).