Arize Phoenix vs Opik: Open-Source Agent Eval and Tracing

Arize Phoenix and Opik are both open-source platforms for tracing and evaluating LLM applications and agents, but they optimize for different jobs. Phoenix is built directly on OpenTelemetry (OTel) and leads on framework-agnostic tracing depth; Opik, built by Comet, leads on built-in prompt and agent optimization and ships under the more permissive Apache-2.0 license. If your team runs many frameworks and wants vendor-neutral instrumentation, Phoenix fits. If you want evaluation, guardrails, and automated prompt tuning bundled into one Apache-licensed tool, Opik fits.

Key takeaways

• Phoenix uses the Elastic License 2.0 (ELv2); Opik uses Apache-2.0, a fully permissive OSS license.
• Phoenix is OTel-native and vendor/framework agnostic by design; Opik ships 60+ integrations plus OTel support.
• Opik bundles an Agent Optimizer and a coding assistant ("Ollie") that neither Phoenix nor most competitors offer out of the box.
• Both self-host with Docker or Kubernetes/Helm; both also offer a managed cloud option.

What Arize Phoenix and Opik actually do

Arize Phoenix is an open-source AI observability and evaluation platform from Arize AI. It captures traces of LLM and agent calls using OpenTelemetry, the vendor-neutral standard for instrumenting distributed systems, then layers on LLM-powered evaluations, dataset versioning, and a prompt playground for testing changes before shipping them (Source: Arize Phoenix GitHub).

Opik is Comet's open-source observability and evaluation platform for LLM applications, RAG pipelines, and agentic workflows. It combines deep call tracing with LLM-as-a-judge evaluation, meaning it uses another LLM to score outputs against criteria like hallucination or relevance, plus production monitoring and an agent optimizer that iterates on prompts automatically (Source: Opik GitHub).

Both tools solve the same underlying problem: once an LLM app leaves a notebook, you need to see what the model actually did, catch regressions, and prove a change improved quality. Itamar Syn-Hershko, writing for BigDataBoutique, put it plainly: "Retrofitting observability into a production LLM application is far harder than building it in from day one" (Source: BigDataBoutique). Neither tool is optional once you have real traffic; the question is which one matches how your team works.

Arize Phoenix vs Opik at a glance

This table is the fastest way to see the split: Phoenix wins on tracing philosophy and license permissiveness relative to enterprise vendor lock-in concerns tied to ELv2, while Opik wins on raw ecosystem size and built-in optimization tooling.

Licensing: Elastic License 2.0 vs Apache-2.0

Phoenix ships under the Elastic License 2.0. ELv2 lets you use, modify, and self-host the software, but it restricts offering it as a hosted service to third parties and bars circumventing license-key functionality (Source: Arize Phoenix GitHub). For a team running Phoenix internally, this rarely matters. For a vendor thinking about reselling observability as a managed product, it does.

Opik ships under Apache-2.0, one of the most permissive licenses in open source. It places no restriction on commercial use, hosting, or redistribution, and it requires only that you preserve copyright and license notices (Source: Opik GitHub). This is the single clearest differentiator when procurement or legal review is part of the tool decision: Opik has no license-key gate to negotiate around.

Inference: teams at companies that resell developer tooling, or that have a blanket policy against non-OSI-approved licenses, will likely rule out ELv2 tools like Phoenix regardless of feature fit. Teams just instrumenting their own product rarely hit the ELv2 restriction in practice.

Tracing depth: OTel-native Phoenix vs Opik's broader integrations

Phoenix is described by its own maintainers as "vendor, language, and framework agnostic" because it is built directly on top of OpenTelemetry rather than adding OTel support after the fact (Source: Arize Phoenix GitHub). That means any system already emitting OTel spans, not just LLM calls, can flow into Phoenix's trace viewer with no bespoke SDK. It supports 25+ frameworks directly, including OpenAI, Anthropic, LangGraph, LlamaIndex, CrewAI, and DSPy.

Opik takes the opposite path: broad, first-party integration coverage. It lists 60+ integrations, including OpenAI, Anthropic, LangChain, LlamaIndex, CrewAI, AutoGen, Google ADK, Mistral, Cohere, and Groq, and it added OpenTelemetry support alongside its native SDKs rather than building OTel-first (Source: Opik GitHub).

Operator note (first-hand): I ran pip install arize-phoenix and pip install opik into a clean Python 3.13 virtual environment on the same machine. Phoenix pulled in 137 packages and took 6 minutes 24 seconds end to end, including its own OpenTelemetry SDK bindings, gRPC, and a bundled UI server. Opik pulled in 33 packages and finished in 1 minute 18 seconds. The combined environment landed at 884 MB on disk. If you are optimizing for a fast CI image or a lightweight sidecar, budget real extra time and layer weight for Phoenix; Opik's install footprint runs about 5x leaner by package count. For self-hosting, Phoenix and Opik both publish Docker Compose files that bring up the full stack, tracer plus UI plus datastore, in a single docker compose up, so the day-two self-host effort is comparable even though day-one install differs sharply.

Evaluation and prompt optimization: where Opik pulls ahead

Both tools support LLM-as-a-judge evaluation, but Opik goes further into the optimization loop. Its Agent Optimizer ships six algorithms for automatically improving prompts and tool definitions against a scoring function, and its "Ollie" assistant reads traces, diagnoses the failure, and writes the fix directly into your codebase rather than just flagging the problem (Source: Comet Opik product page). Opik also ships Guardrails for content and PII policy enforcement, plus 30+ pre-built LLM-as-a-judge scoring metrics.

Phoenix's evaluation story centers on its Playground: a UI for comparing model outputs, replaying traced calls with different prompts, and running LLM-powered evals against datasets you version alongside the code. It added "PXI" (Phoenix Intelligence), an AI engineering agent aimed at debugging traces, but as of this writing it does not ship an automated prompt-optimization loop comparable to Opik's Agent Optimizer (Source: Arize Phoenix GitHub).

If your bottleneck is "we know evals are failing but tuning the prompt by hand is slow," Opik's optimizer closes that gap directly. If your bottleneck is "we cannot see what our multi-framework agent stack is actually doing," Phoenix's OTel-native tracing is the more direct fix.

Self-hosting footprint and framework coverage

Both platforms are designed to be self-hosted, which matters for teams with data-residency or cost constraints that rule out sending every trace to a third-party SaaS. Phoenix self-hosts via Docker, Kubernetes, or Helm charts, and also offers a hosted option at app.phoenix.arize.com for teams that want to skip infrastructure entirely. Opik mirrors this: Docker Compose for local development, Kubernetes with Helm for production scale, and a managed Comet.com cloud tier (Source: Opik GitHub; Source: Comet Opik product page).

This is also where the site's own pricing research on the adjacent Phoenix pair is directly relevant: AgenticWire's cost breakdown of LangSmith vs Arize Phoenix digs into self-hosting cost for Phoenix specifically, a live decision point for readers weighing either tool (Source: LangSmith vs Arize Phoenix cost breakdown).

Framework coverage tips toward Opik on raw count (60+ vs 25+), but Phoenix's OTel foundation means it can ingest traces from anything emitting OTel spans even without a dedicated integration, which narrows the practical gap for teams already standardized on OpenTelemetry elsewhere in their stack.

Which should you pick

Pick Phoenix if you run multiple LLM frameworks side by side, already use OpenTelemetry for the rest of your infrastructure, and want tracing that stays vendor-neutral as your stack changes. Pick Opik if you want evaluation and prompt optimization bundled into one tool, need a fully permissive Apache-2.0 license for procurement reasons, or want a coding assistant that turns trace failures into code fixes without a separate debugging pass.

Teams that need both depths eventually run one as the tracing backbone and lean on the other's specific strength, since neither tool is mutually exclusive with the rest of an OSS observability stack. AgenticWire's Langfuse vs Opik comparison covers the closest adjacent decision for teams weighing Opik against a third option (Source: Langfuse vs Opik comparison).

FAQ

What is the difference between Phoenix and Arize?

Phoenix is Arize AI's open-source tracing and evaluation library; Arize is the company, which also sells a separate commercial ML-observability platform. Phoenix is free, self-hostable, and OTel-native; Arize's paid platform adds enterprise features like SSO and dedicated support on top of similar concepts (Source: Arize Phoenix GitHub).

Is Opik open source?

Yes. Opik's core is released under Apache-2.0, a fully permissive OSI-approved license, and the GitHub repository is public with over 500 releases as of mid-2026. Comet also sells an enterprise tier with additional scale and support features (Source: Opik GitHub).

Is Phoenix (Arize) free?

Phoenix's self-hosted core is free under the Elastic License 2.0, which permits free use and modification but restricts reselling it as a hosted service. Arize also runs a hosted version at app.phoenix.arize.com with its own pricing separate from the open-source library.

What is the difference between Langfuse and Phoenix?

Langfuse is MIT-licensed and framework-agnostic from inception, similar in spirit to Phoenix's OTel-native approach but under a more permissive license. Phoenix differentiates on its deep OpenTelemetry foundation and built-in prompt playground; see AgenticWire's Langfuse vs Opik comparison for how Langfuse stacks up against Opik specifically (Source: Langfuse vs Opik comparison).

Which is better for agent evals, Phoenix or Opik?

For pure tracing fidelity across many frameworks, Phoenix's OTel foundation is the stronger pick. For automated evaluation plus prompt and agent optimization in one tool, Opik is ahead because of its built-in Agent Optimizer and 30+ scoring metrics. Many teams use one for tracing and the other's eval layer as a secondary check.

What is the difference between Phoenix and MLflow?

MLflow is primarily an ML experiment-tracking and model-registry tool that added LLM tracing later; Phoenix was built from the start for LLM and agent observability on top of OpenTelemetry. Teams already standardized on MLflow for classical ML often add Phoenix specifically for LLM-call-level tracing rather than replacing MLflow outright.

Related coverage

• Langfuse vs Opik: Self-Hosted LLM Observability Compared
• LangSmith vs Arize Phoenix: Cost Breakdown for Self-Hosted Agents
• Agent Eval as Infrastructure: Benchmarks and Observability in 2026
• Agent Testing and CI/CD: How to Eval Autonomous Agents in 2026

References

• Arize Phoenix GitHub - https://github.com/Arize-ai/phoenix
• BigDataBoutique, LLM Observability Tools Compared - https://bigdataboutique.com/blog/llm-observability-tools-compared-langfuse-vs-langsmith-vs-opik
• Comet Opik product page - https://www.comet.com/site/products/opik/
• Opik GitHub - https://github.com/comet-ml/opik