Arize Phoenix vs. Opik: Open-Source AI Agent Observability and LLM Evaluation in 2026

What are the best alternatives to Arize Phoenix in 2026?

The four platforms teams most often evaluate alongside Arize Phoenix are:

• Opik: An Apache-2.0 platform for agent observability, evaluation, testing, and optimization, with a free full-feature self-hosted deployment.
• Langfuse: An open-source platform focused on LLM tracing, prompt management, evaluation, and application analytics.
• LangSmith: LangChain’s proprietary observability and evaluation platform, with its deepest integration into LangChain and LangGraph.
• Weights & Biases Weave: Evaluation and observability tooling designed to work within the broader W&B ecosystem.

For teams that require a full platform inside their own infrastructure, Opik and Phoenix are the most direct comparison. Both support free self-hosting, broad framework coverage, datasets, experiments, tracing, and evaluations. The bigger differences are licensing, OpenTelemetry architecture, testing workflows, production monitoring, and how much work happens inside the product rather than in external infrastructure or testing tools.

What is the difference between Arize Phoenix and Opik?

The simplest distinction is that Phoenix is standards-first, while Opik is workflow-first.

Phoenix is built around OpenTelemetry and the OpenInference semantic conventions maintained by Arize. It accepts OTLP over HTTP and gRPC and provides instrumentation for popular model providers, frameworks, and programming languages. This makes Phoenix particularly attractive to teams that already operate OpenTelemetry collectors and want AI traces to fit into an existing observability architecture.

Opik also accepts OpenTelemetry data and can receive traces from any language with an OpenTelemetry SDK, although its current direct OpenTelemetry integration supports HTTP transport rather than gRPC. Opik’s emphasis is on what developers do after instrumentation: inspect a trace, turn a failure into a test, run regression suites, monitor the corresponding behavior in production, and optimize the prompts, model parameters, or tools involved.

There is also a meaningful licensing difference. Opik uses Apache License 2.0. Phoenix uses Elastic License 2.0, which allows teams to inspect, modify, distribute, and self-host Phoenix but prohibits offering a substantial set of Phoenix functionality to third parties as a hosted or managed service.

How do Arize Phoenix and Opik compare, feature by feature?

They match on the core observability and evaluation jobs. Both support traces and spans, datasets, experiments, deterministic evaluators, LLM-as-a-judge, human feedback, production trace analysis, session-level evaluation, prompt management, and AI-assisted investigation.

They diverge more clearly in five areas:

1. Opik turns regression testing into a native platform workflow.
2. Phoenix offers the deeper OpenTelemetry and OpenInference architecture.
3. Opik includes production evaluation and alerts in its open-source product, while Phoenix directs continuous alerting and stakeholder dashboards to Arize AX.
4. Opik’s optimization covers prompts, model parameters, tools, MCP workflows, and multimodal agents.
5. Phoenix has stronger first-party embedding visualization and a clearer migration path into Arize’s predictive-ML observability stack.

Can you self-host Arize Phoenix or Opik?

Yes. Both can be deployed on a laptop, private cloud, Kubernetes cluster, on-premises environment, or isolated network.

Opik’s self-hosted product uses Apache License 2.0 and includes the full observability and agent-testing feature set. The license allows teams to use, modify, distribute, and build commercial services around the software under Apache’s standard conditions.

Phoenix is also free to self-host, with no Phoenix features held back for a paid Phoenix edition. However, Arize AX has a different feature set and you are upgrading to a different core product, whereas Opik’s open-source version shares a nearly identical codebase with the cloud and enterprise versions, making upgrades smoother. With Phoenix, your team is responsible for provisioning, scaling, upgrading, securing, and supporting the deployment. Phoenix’s Elastic License 2.0 permits internal use and modification but restricts providing a substantial part of Phoenix to third parties as a hosted or managed service.

For most internal engineering teams, both products satisfy the basic requirement to keep trace and evaluation data inside their own infrastructure. Apache-2.0 becomes more important when permissive redistribution, product embedding, or commercial hosting rights matter.

Is Opik a good open-source alternative to Arize Phoenix?

Yes, particularly for teams that want to move from observing an agent to systematically improving it without assembling separate workflows for testing, production evaluation, optimization, and regression prevention.

Opik covers the core Phoenix use cases: tracing, datasets, experiments, deterministic evaluation, LLM-as-a-judge, human review, production monitoring, prompt management, and self-hosted deployment. It then adds native Test Suites, built-in online-evaluation rules and alerts, disk-backed outage recovery in the Python SDK, broader documented multimodal support, and optimization for prompts, parameters, tools, and MCP workflows.

Phoenix remains a compelling alternative when the architecture matters more than having every workflow consolidated in the product. Teams already operating OpenTelemetry collectors may prefer Phoenix’s native gRPC support and OpenInference conventions. Teams working heavily on search and retrieval may also value its embeddings visualizer.

Which has better evaluation?

Both platforms have credible evaluation systems, and reducing the comparison to a checklist would miss the more important difference.

Phoenix supports deterministic code evaluators, custom heuristics, LLM-as-a-judge, client-side evaluation in Python and TypeScript, server-side evaluators configured through the UI, evaluations over production traces, and evaluation runs linked back to their own OpenTelemetry traces. Its pytest integration is especially useful for teams that already treat evaluation code as part of the application’s normal test suite.

Opik supports the same broad categories of evaluation, including offline experiments, online production evaluation, human review, conversation-level scoring, trajectory evaluation, and built-in or custom metrics. Its clearest advantage is the Test Suite abstraction: developers can express expected behavior as pass/fail assertions, apply assertions globally or to individual scenarios, specify how many times nondeterministic cases must run, and define how many runs must pass. Test Suites can be created in the UI, called through the SDK, or populated from failed production traces by Ollie.

For a team that wants evaluation code to live primarily in pytest, Phoenix is a strong choice, though Opik’s pytest integration is worth comparing too. For a broader group of developers, product managers, and domain experts who need to build and inspect regression suites inside the observability platform, Opik provides the more integrated workflow.

Opik also goes further once the evaluation identifies a problem. Its optimizer can search multiple prompt strategies, model parameters, few-shot examples, tool definitions, and MCP workflows against the same datasets and evaluation metrics. Phoenix offers automatic prompt learning, but its documented optimization scope is narrower.

Do Arize Phoenix and Opik offer free trials or proof of concept?

Neither platform requires a sales conversation to begin a self-hosted proof of concept.

Opik’s full open-source deployment is free, and Opik Cloud has a $0 tier with 25,000 spans per month. Phoenix is also free to self-host with no Phoenix feature gates. Teams that prefer Arize’s managed platform can start on the separate AX Free plan.

A useful proof of concept should test more than whether both products can display the same trace. Instrument one representative production agent and compare four complete workflows:

1. Identify the root cause of a failed or low-quality output.
2. Turn that failure into a repeatable regression test.
3. Evaluate the behavior continuously on production traffic.
4. Improve the agent and verify that the change did not create a new regression.

Also simulate a temporary network failure. Opik’s Python SDK should persist failed tracing messages locally and replay them after recovery. With Phoenix, review how your chosen OpenTelemetry exporter, processor, collector, and queue configuration behave under the same conditions.

The platform that makes the entire loop easiest for your team—not merely the one with the best trace screenshot—is usually the right choice.

Where does Arize Phoenix beat Opik?

There are three places where Phoenix has a genuine advantage.

First, Phoenix has the deeper OpenTelemetry and OpenInference architecture. It supports OTLP over both HTTP and gRPC, uses OpenInference semantic conventions throughout the product, and is a natural fit for teams that already route telemetry through collectors and shared observability infrastructure. Opik accepts native OpenTelemetry traces from many languages, but its documented direct integration currently supports HTTP transport only.

Second, Phoenix has especially strong search and retrieval tooling. Its embeddings visualizer helps teams examine how data is clustered and represented, which can inform chunking, indexing, similarity, and retrieval decisions. Teams building retrieval-intensive applications may value that visual workflow more than Opik’s broader optimization surface.

Third, Phoenix fits naturally into existing software-testing conventions. Its pytest plugin lets developers write evaluation cases as ordinary tests, use fixtures and parameterization, preserve Git metadata, run the same suite locally or in CI, and fail the build through standard assertions. Opik’s native Test Suites require fewer moving parts inside the product, but teams already committed to test-as-code may prefer Phoenix’s approach.

PXI is also a meaningful Phoenix strength. It can investigate failures, navigate observability data, propose prompt revisions, run experiments, and author evaluators from inside Phoenix. Ollie goes further into the application code, but PXI is not merely a chat interface layered over trace search.

Which should you choose?

Choose Opik when your priorities are:

• A permissive Apache-2.0 license
• Native Test Suites that can be managed in the UI and SDK
• Production evaluation and alerts in the open-source product
• A unified workflow from failed trace to regression test
• Prompt, parameter, tool, MCP, and multimodal optimization
• Built-in SDK protection against temporary ingestion outages
• Broader collaboration across developers and non-developer evaluators

Choose Arize Phoenix when your priorities are:

• OpenTelemetry and OpenInference as the foundation of your architecture
• Native OTLP over gRPC
• Tight integration with an existing pytest-based test workflow
• Embedding visualization and retrieval analysis
• A self-hosted platform your infrastructure team is prepared to operate
• A future path into Arize AX for managed GenAI and predictive-ML observability

Both are capable observability and evaluation platforms. The decision is less about whether either product can capture a trace or run an LLM judge. It is about whether your team wants a standards-oriented toolkit that fits into existing telemetry and testing infrastructure, or a more integrated product for testing, monitoring, optimizing, and fixing agents.