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Dynatrace to Acquire AI Observability Leader Arize

Acquisition will provide the industry's most comprehensive AI development lifecycle solution

Dynatrace signed a definitive agreement to acquire Arize in a cash and stock transaction valued at $915 million. 

Together, Dynatrace and Arize will enable customers to evaluate, operate, and continuously improve AI applications from development through production at scale.

AI Observability spans the full lifecycle of an AI application, from experimentation and evaluation before release, to tracing how LLMs, agents, and orchestration layers behave in production. It connects that behavior with application performance, GPU utilization, infrastructure health, and business processes. AI Observability delivers the insights AI engineers, developers, SREs, and platform teams need to debug and optimize AI behavior and keep AI-powered services accurate, reliable, and cost-efficient. It is also one of the fastest-growing categories in observability, projected to exceed $10 billion by 2030 and is central to Dynatrace’s growth strategy.

“AI is now moving into production at incredible speed, and the resulting AI Observability market opportunity is enormous. Dynatrace anticipates customers’ needs at critical inflection points, and this is one of the most significant in our history,” said Rick McConnell, CEO, Dynatrace. “Acquiring Arize advances our AI observability leadership, accelerates our roadmap, enhances our long-term growth profile, expands our reach with the developer community, and adds an incredible AI-first team to Dynatrace.”

Today, AI software delivery is fragmented. AI engineering teams evaluate model and agent behavior in one set of tools, while the teams running the applications and infrastructure beneath them work in another. There is often no shared system connecting how an AI application is evaluated to how it behaves in production, so when output quality slips or a customer transaction fails, the cause can sit anywhere from the prompt to the infrastructure, and there is little feedback to developers. Dynatrace’s acquisition of Arize will eliminate that fragmentation and provide end-to-end observability from development to production.

Arize is the category leader in AI Observability, purpose-built for AI and agents and trusted by Fortune 500 enterprises and AI-native builders alike. Arize combines a strong developer brand and thriving open source community with the enterprise-grade frameworks that teams need to detect hallucinations, measure output quality, and continuously validate AI behavior. It is the only platform that is simultaneously OSS-native and stack-agnostic across every major AI framework and model provider. This makes Arize a clear choice for developers across the AI lifecycle.

“We founded Arize because AI teams needed a way to know their agents were actually working correctly, not just running,” said Jason Lopatecki, CEO, Arize. “Joining Dynatrace will enable us to take that mission much further. Together, we can bring AI evaluation and software observability into an end-to-end system, enabling teams to build more ambitious AI applications faster. This is the kind of innovation that only happens when two companies with highly complementary solutions and go-to-market models come together, setting a new bar for what AI Observability can deliver for the entire industry.”

Following the closing of the proposed acquisition, Dynatrace customers will gain:

  • Continuous coverage across the AI lifecycle, from experimentation and deployment readiness through runtime evaluation and observability, with automated feedback loops that support ongoing improvement.
  • Unified context for understanding AI behavior and business impact, connecting model and agent evaluation with application performance, infrastructure health, and business outcomes.
  • An enterprise data foundation for AI workloads, powered by exabyte-scale analysis and AI lakehouse capabilities.

For developers, the combination will deliver a direct path from experimentation to enterprise-grade deployment, backed by Arize’s trusted standing in the open-source AI community. As AI tooling decisions increasingly start with developers, that trust opens a direct path to the observability and reliability capabilities that Dynatrace already brings to enterprise customers.

The transaction is expected to close later this quarter or early in Dynatrace’s third quarter, subject to regulatory reviews and other customary closing conditions. Under the terms of the agreement, Dynatrace will acquire Arize for $915 million, subject to customary adjustments, consisting of approximately $815 million in cash as well as replacement equity awards for Arize employees joining Dynatrace. Dynatrace plans to fund the transaction through cash on hand and/or its existing credit facility.

Arize’s two founders, Jason Lopatecki and Aparna Dhinakaran, will both join Dynatrace at closing. Jason will continue to lead the Arize team and will report directly to Rick McConnell.

The Latest

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 ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

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 ...

Dynatrace to Acquire AI Observability Leader Arize

Acquisition will provide the industry's most comprehensive AI development lifecycle solution

Dynatrace signed a definitive agreement to acquire Arize in a cash and stock transaction valued at $915 million. 

Together, Dynatrace and Arize will enable customers to evaluate, operate, and continuously improve AI applications from development through production at scale.

AI Observability spans the full lifecycle of an AI application, from experimentation and evaluation before release, to tracing how LLMs, agents, and orchestration layers behave in production. It connects that behavior with application performance, GPU utilization, infrastructure health, and business processes. AI Observability delivers the insights AI engineers, developers, SREs, and platform teams need to debug and optimize AI behavior and keep AI-powered services accurate, reliable, and cost-efficient. It is also one of the fastest-growing categories in observability, projected to exceed $10 billion by 2030 and is central to Dynatrace’s growth strategy.

“AI is now moving into production at incredible speed, and the resulting AI Observability market opportunity is enormous. Dynatrace anticipates customers’ needs at critical inflection points, and this is one of the most significant in our history,” said Rick McConnell, CEO, Dynatrace. “Acquiring Arize advances our AI observability leadership, accelerates our roadmap, enhances our long-term growth profile, expands our reach with the developer community, and adds an incredible AI-first team to Dynatrace.”

Today, AI software delivery is fragmented. AI engineering teams evaluate model and agent behavior in one set of tools, while the teams running the applications and infrastructure beneath them work in another. There is often no shared system connecting how an AI application is evaluated to how it behaves in production, so when output quality slips or a customer transaction fails, the cause can sit anywhere from the prompt to the infrastructure, and there is little feedback to developers. Dynatrace’s acquisition of Arize will eliminate that fragmentation and provide end-to-end observability from development to production.

Arize is the category leader in AI Observability, purpose-built for AI and agents and trusted by Fortune 500 enterprises and AI-native builders alike. Arize combines a strong developer brand and thriving open source community with the enterprise-grade frameworks that teams need to detect hallucinations, measure output quality, and continuously validate AI behavior. It is the only platform that is simultaneously OSS-native and stack-agnostic across every major AI framework and model provider. This makes Arize a clear choice for developers across the AI lifecycle.

“We founded Arize because AI teams needed a way to know their agents were actually working correctly, not just running,” said Jason Lopatecki, CEO, Arize. “Joining Dynatrace will enable us to take that mission much further. Together, we can bring AI evaluation and software observability into an end-to-end system, enabling teams to build more ambitious AI applications faster. This is the kind of innovation that only happens when two companies with highly complementary solutions and go-to-market models come together, setting a new bar for what AI Observability can deliver for the entire industry.”

Following the closing of the proposed acquisition, Dynatrace customers will gain:

  • Continuous coverage across the AI lifecycle, from experimentation and deployment readiness through runtime evaluation and observability, with automated feedback loops that support ongoing improvement.
  • Unified context for understanding AI behavior and business impact, connecting model and agent evaluation with application performance, infrastructure health, and business outcomes.
  • An enterprise data foundation for AI workloads, powered by exabyte-scale analysis and AI lakehouse capabilities.

For developers, the combination will deliver a direct path from experimentation to enterprise-grade deployment, backed by Arize’s trusted standing in the open-source AI community. As AI tooling decisions increasingly start with developers, that trust opens a direct path to the observability and reliability capabilities that Dynatrace already brings to enterprise customers.

The transaction is expected to close later this quarter or early in Dynatrace’s third quarter, subject to regulatory reviews and other customary closing conditions. Under the terms of the agreement, Dynatrace will acquire Arize for $915 million, subject to customary adjustments, consisting of approximately $815 million in cash as well as replacement equity awards for Arize employees joining Dynatrace. Dynatrace plans to fund the transaction through cash on hand and/or its existing credit facility.

Arize’s two founders, Jason Lopatecki and Aparna Dhinakaran, will both join Dynatrace at closing. Jason will continue to lead the Arize team and will report directly to Rick McConnell.

The Latest

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 ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

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 ...