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Elastic Completes Acquisition of Deductive AI

Acquisition advances Elastic Observability with AI-powered root cause analysis that learns and improves with every incident

Elastic has completed the acquisition of Deductive AI, an AI-powered investigation platform that helps engineering teams identify and resolve production issues faster. 

The acquisition advances Elastic's goal to set the standard for production incident investigation by bringing more AI-powered investigation and automation to Elastic Observability.

As modern applications grow more distributed, interconnected and complex, engineers have more telemetry than ever, but spend too much time manually piecing together information across tools to understand what happened, why it happened, and what to do next. Deductive AI moves beyond simply summarizing alerts or suggesting where to look. Its technology is designed to take on more of the investigation itself while keeping engineers in control.

"Engineering teams today are drowning in telemetry but starved for answers," said Ash Kulkarni, chief executive officer, Elastic. "We have been building AI into Elastic Observability to move teams from detection to resolution faster. Deductive AI brings a sophisticated reinforcement learning harness for root cause analysis that accelerates this work. Together with the depth of telemetry and context in Elastic, it brings us closer to fundamentally changing how production incidents are investigated."

Deductive AI adds an AI SRE agent that gathers evidence, forms and tests hypotheses, and reasons across code, telemetry, and organizational knowledge to determine root cause. Its investigation engine learns which paths lead to useful evidence and successful outcomes, so every investigation can improve the next. Combined with Elastic’s ability to infer meaningful entities, relationships, and significant operational events from telemetry, these capabilities will help engineering teams investigate and resolve production issues faster.

"We've always believed that engineering teams deserve better than hours spent manually tracing the root cause of every incident," said Rakesh Kothari, former chief executive officer and cofounder, Deductive AI. "Joining Elastic lets us bring that conviction to a much larger audience and dramatically accelerate the development of AI-powered investigation capabilities. The opportunity ahead is unlike anything we could have built on our own."

Existing Deductive AI customers will continue to receive support while integration plans are developed. Additional product roadmap details will be shared in the coming months.

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Elastic Completes Acquisition of Deductive AI

Acquisition advances Elastic Observability with AI-powered root cause analysis that learns and improves with every incident

Elastic has completed the acquisition of Deductive AI, an AI-powered investigation platform that helps engineering teams identify and resolve production issues faster. 

The acquisition advances Elastic's goal to set the standard for production incident investigation by bringing more AI-powered investigation and automation to Elastic Observability.

As modern applications grow more distributed, interconnected and complex, engineers have more telemetry than ever, but spend too much time manually piecing together information across tools to understand what happened, why it happened, and what to do next. Deductive AI moves beyond simply summarizing alerts or suggesting where to look. Its technology is designed to take on more of the investigation itself while keeping engineers in control.

"Engineering teams today are drowning in telemetry but starved for answers," said Ash Kulkarni, chief executive officer, Elastic. "We have been building AI into Elastic Observability to move teams from detection to resolution faster. Deductive AI brings a sophisticated reinforcement learning harness for root cause analysis that accelerates this work. Together with the depth of telemetry and context in Elastic, it brings us closer to fundamentally changing how production incidents are investigated."

Deductive AI adds an AI SRE agent that gathers evidence, forms and tests hypotheses, and reasons across code, telemetry, and organizational knowledge to determine root cause. Its investigation engine learns which paths lead to useful evidence and successful outcomes, so every investigation can improve the next. Combined with Elastic’s ability to infer meaningful entities, relationships, and significant operational events from telemetry, these capabilities will help engineering teams investigate and resolve production issues faster.

"We've always believed that engineering teams deserve better than hours spent manually tracing the root cause of every incident," said Rakesh Kothari, former chief executive officer and cofounder, Deductive AI. "Joining Elastic lets us bring that conviction to a much larger audience and dramatically accelerate the development of AI-powered investigation capabilities. The opportunity ahead is unlike anything we could have built on our own."

Existing Deductive AI customers will continue to receive support while integration plans are developed. Additional product roadmap details will be shared in the coming months.

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Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

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Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...