Mirrors vs Arize

What Arize is good at

• Drift and embedding analysis, and classic ML monitoring, inherited from the years they spent doing exactly that before LLMs. If one team owns both a fraud model and an agent, this is the only platform in this comparison that covers both, and that is a genuine reason to buy it.
• Enterprise scale and the operational maturity that comes with having run at it.
• Standards work. They maintain OpenInference alongside OpenTelemetry, which means the instrumentation you write for Arize is portable, and some of the reason this category has any interoperability at all is their doing.
• Phoenix as a local-first open source tracer with 10,852 stars: it runs on your machine, no account, and it is a good first thing to install when you have nothing.
• A full eval surface: evals and LLM-as-judge, datasets, a playground, annotation, and guardrails, with Agent Experiments generally available since June 2026.

Side by side

Prices and limits checked against arize.com on 2 August 2026. The Phoenix license split matters if you plan to build on it: read it as two licenses, not one.

The actual difference

Arize is a measurement company, and it shows in the best way. AX is built to tell you what a system in production is doing, whether that system is a ranking model or an agent, and Phoenix is the local end of the same idea. Agent Experiments extends it to running a dataset through your agent and scoring the results.

The thing being run, though, is yours, in your infrastructure, wired to your dependencies. Agent Experiments posts rows to an endpoint you stand up. Whatever that endpoint touches when it handles a row, it touches for real. Nothing in AX or Phoenix pretends to be your order database.

Mirrors is that missing half. It reads your traces, your code, or your API docs and builds the systems the agent calls: a running service per tool, each with a schema, fabricated seed records, and state that changes as the agent acts and resets between runs. The endpoint Agent Experiments posts to can then be an agent wired to that environment rather than to production.

The honest framing is that these are adjacent problems, not competing answers. Arize measures behavior. Mirrors provides somewhere for the behavior to happen. Neither is a substitute for the other, and Arize covers a surface (classic ML monitoring, drift, embeddings) that we do not touch at all.

One word worth disambiguating, because this category overloads it. When these products say sandbox they usually mean a microVM where an agent runs code, and the proxy in front of it injects real credentials so that the code reaches real services. That is the opposite of a fake backend, and it is useful for what it is built for. Nothing on this page claims a competitor lacks one, and the environment Mirrors builds is not one of them.

When to use Arize instead

If you run classic ML models as well as agents, use Arize. Nothing else in this comparison covers both, and running two observability platforms so that one of them can be marginally better at agents is a bad trade.

Use it also if you are standardizing on OpenTelemetry and OpenInference, if you need production monitoring with drift detection at enterprise volume, or if you just want a local tracer today: Phoenix on your own machine costs nothing and answers the "what is my agent actually doing" question in an afternoon.

Most teams run both

The composition is clean because the two products meet at the span. Mirrors runs the agent against a rebuilt environment; those runs emit OpenTelemetry spans; AX or Phoenix ingests them alongside production traffic and applies the same evaluators to both.

That also gives you the comparison that matters most: the same evaluator, over a production run and over a replay of the change, in one place.

What this page compares, and what it does not

These five are the observability and evaluation platforms a team already has open when it asks how to test an agent, which is why they are the pages we wrote. They are not the whole field. Tools that do stand something in for an agent's counterpart, LangWatch's Scenario and Laminar's agent rollouts among them, are outside this comparison, and nothing here is a claim about them.

Standing in for a live dependency is also not a new idea. Record-and-replay HTTP mocking, VCR-style cassettes, predates LLM agents by about fifteen years and is free. What Mirrors adds is the construction and the state: the environment is built from your traces, code, or docs instead of hand-written, and it is a seeded, stateful backend you can reset between runs instead of a fixed transcript of responses.