In August Dynatrace announced plans to acquire Arize, an AI observability company, for $915m. The deal makes strategic sense – AI workloads are increasingly production workloads, and will need to be managed as such. We’re going to see convergence and consolidation in the markets for Observability, AI Observability, and evals tools. Arize has out of the box integrations for more than 30 AI platforms, providing auto-instrumentation for them.
Acquiring Arize will give Dynatrace a foothold in the market for evals – automated checks to evaluate the quality, accuracy and safety of AI systems. Evals are kind of like unit tests, but for non-deterministic LLM-based systems – this idea is pretty much an industry truism now, but it’s worth reading Hamel Husain’s seminal essay Your AI Product Needs Evals for further context.
Like software engineering, success with AI hinges on how fast you can iterate. You must have processes and tools for:
- Evaluating quality (ex: tests).
- Debugging issues (ex: logging & inspecting data).
- Changing the behavior or the system (prompt eng, fine-tuning, writing code)
The first two points map to observability and evals.
Enterprises are going to need a full lifecycle tool enabling developers and agents to collaborate to build, experiment, monitor and manage applications using, and iterating across, different models. Evals was originally a term used by Frontier model companies – most notably OpenAI, but has now crossed over into more general industry parlance.
We increasingly see companies adopting multimodel strategies – for example using OpenAI models for coding and Anthropic models for verification, or vice-versa. Open weights models are coming into play, too, to reduce costs, or at least to provide a hedge against vendor lock in. Startups are adopting open weights models like MIT-licensed DeepSeek V4-Flash, primarily for cost, independence and control reasons. In a recent Y-Combinator podcast Ollama co-founder and CEO Jeffrey Morgan said that the majority of enterprise token volume, roughly 80 to 90%, will flow through open models. According to a recent New York Times article 40% of the tokens AT&T burns are now “open models”. Rising token costs are encouraging dev shops to adopt tiered model usage strategies – with the most expensive models used for more complex jobs, requiring for example, greater reasoning capabilities. Some of this for development, and some for building outward facing apps.
Adopting the best model for the job is a strategy that requires tools to manage evals, observability and automation. The feedback loops for monitoring systems and applications, and apps and models, will need to converge, which is what Dynatrace is betting on with this acquisition.
For more signal note that:
- ClickHouse acquired Langfuse January 2026, an open source evals and AI observability platform already built on Clickhouse, making the integration that much easier.
- Cisco acquired Galileo May 2026, to bring AI agent observability and evaluation tools into its Splunk Observability portfolio.
Evals are critical because models are non-deterministic, and because different models have their own distinct personalities – changing models can change agent behaviour, which could negatively affect customer experience. Questions of safety and overall system reliability are increasingly intertwined. Your back ends need to keep running and your agents need to provide reasonably consistent behaviours in order to support customer trust. AI Engineering and IT Ops teams need to collaborate closely, so these management platforms need to be closely integrated.
Arize already has a solid customer base, which skews to modern ecommerce and web companies including booking.com, Reddit, Doordash, Priceline, Wayfair, Uber and Duolingo. Dynatrace meanwhile has a solid foothold in the enterprise, built with more than 20 years of direct sales. The company has 4000+ subscription customers, and operates in over 100 countries worldwide. Its more traditional customers will increasingly be looking for the kind of tools Arize provides. The two companies already share a number of joint customers, including Air Canada. The trick will be to infuse Dynatrace with the AI experience and skills from Arize, while bringing to Arize its own breadth of capability and customer base. And of course keeping the Arize people in the fold, and allowing them to keep building.
According to the press release about the deal:
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.
This is the right way to think about the problem. LLM usage is not going to be a separate silo, but is central to how modern software development teams work. As organizations build harnesses, the infrastructure and instructions that shape and manage models into functional agents, they need feedback loops to understand what these agents are actually doing. Traces show what actually happened (observability). Evals turn these traces into repeatable tests: is the harness working as expected? Thus evals are becoming central to harness engineering, and harness engineering is where agentic software delivery is going.
Making Progress in Progressive Delivery
Before considering Arize further, it’s worth examining the overall state of play with Dynatrace in 2026 and its push forward with Progressive Delivery.
Dynatrace kicked off 2026 by acquiring DevCycle, a feature management platform based on the OpenFeature standard, in January. Dynatrace actually led creation of the OpenFeature standard in 2022, but acquired DevCycle to accelerate its own growth, especially with a product led growth motion (PLG). OpenFeature is now a CNCF project.
Feature flags are a core technology for Progressive Delivery, enabling canary deployments, blue-green releases, and experimentation. Putting functionality behind a flag also allows for fast production changes and rollbacks if something breaks. In the Progressive Delivery book we argued that feature management and Observability tooling were both fundamental pillars to modern software delivery, and should be part of an integrated platform. We’ve been proven right by vendor consolidation as vendors in both categories have made acquisitions.
- Harness acquired Split.io May 2024, expanding the scope of its software delivery platform into feature management and progressive delivery. Split also supports OpenFeature natively.
- LaunchDarkly acquired Highlight April 2025, filling out its story for guarded releases and runtime management, with a modern Observability platform, offering tracing, session replay etc.
- DataDog acquired Eppo May 2025, to broaden out its footprint, moving into experimentation and feature management and a progressive delivery platform play. The deal was partly an evals play.
- OpenAI acquired Statsig September 2025, a Frontier model company acquiring one of the hottest companies in the experimentation and feature management space. Progressive Delivery is evidently going to be relevant to the AI era – with faster dev cycles than ever, and model changes requiring testing with named cohorts and rollbacks.
But back to how Dynatrace’s acquisition story offers a coherent look into Progressive Delivery in the agentic era: in April 2026 it also acquired Bindplane, a telemetry data pipeline company, to improve its story around fast-growing telemetry data volumes. System telemetry can be collected, enriched, processed and routed before it reaches observability platform datastores. Overall, this kind of approach improves cost management and efficiency. I have written about this idea in a post about log data management before. If telemetry data was growing fast before, with AI agents being used in production it’s exploding, so managing telemetry costs, and having flexibility across multiple data sources, will be more important than ever.
Arize, AI Native
Arize was built for monitoring and managing AI workflows – tracing behaviour, evaluating that behaviour, and learning whether changes will have the desired effect or not. The company’s AI engineering agent Alyx is squarely targeted at agentic ops for agentic development. Arize has been around for six years – it is properly AI native. This is a major cultural and technical infusion for Dynatrace, providing it can keep the talent in place.
Supporting its PLG motion Arize has a source available (not open source according to an OSI definition) project called Phoenix for instrumentation and evals. Phoenix provides tracing allowing you to see what happened during a run of your AI application – model calls, tool use, and custom logic. It accepts traces using OpenTelemetry with auto-instrumentation for popular frameworks (such as LlamaIndex, LangChain, DSPy, Vercel AI SDK), providers (OpenAI, Bedrock, Anthropic), and languages (Python, TypeScript, Java).
While Phoenix is based on Open Telemetry, the project is not open source, instead licensed under the Elastic License 2.0. There is a solid open source and open standards throughline here: OpenFeature for DevCycle, OpenTelemetry for Bindplane, and OpenTelemetry again for Phoenix. It will be interesting to see whether Dynatrace fully open sources Phoenix. Of course being RedMonk we encourage using OSI-compliant licenses. We’re pretty consistent on that.
Arize also created OpenInference, an open source set of extensions for OpenTelemetry designed specifically for tracing LLM and agent context. This Apache 2.0 licensed tool has been adopted by third parties including envoy, Microsoft, and Tetrate. It’s also been adopted by frameworks including Crew AI, IBM Bee AI, and Langchain. Open source always makes third party adoption easier.
Dynatrace was already pushing ahead with some interesting agentic tooling. It recently launched Bluebox, an observability platform built to provide context to agents rather than humans – topology, traces and so on to support autonomous troubleshooting and debugging.
But the Arize acquisition will accelerate a push into agent-first workflows.
The Observability market was already undergoing consolidation as vendors pursued platform strategies to consolidate adjacent markets (metrics, logs, traces, feature management, progressive delivery) and now AI observability is part of that mix. The reality of multi model usage, and the desire of companies to drive down token costs, are going to make it critical to have integrated observability and evals platforms with full visibility into agent and developer workflows. Observability and evals will also be central to harness engineering.
Dynatrace and LaunchDarkly are clients.This is not a sponsored piece of research.
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