Opik vs Langfuse vs Phoenix (2026): Open-Source LLM Observability Compared

If you want LLM observability and evaluation without a proprietary platform, the 2026 shortlist is Opik vs Langfuse vs Arize Phoenix. The short answer: pick Langfuse for the most mature, widely adopted open-core platform, Opik for a fully Apache 2.0 feature set with guardrails and an agent optimizer, and Phoenix for OpenTelemetry-native, notebook-friendly tracing with the lightest self-host footprint. This post compares all three on tracing, evals, self-hosting, and cloud pricing. For the proprietary side of the landscape, see Braintrust vs LangSmith.

The short answer

• Langfuse - the maturity and ecosystem pick. MIT-licensed core, tracing plus prompt management plus datasets plus evals in one platform, adopted at Fortune 500 scale, and backed by ClickHouse since its January 2026 acquisition.
• Opik - the license-purity and optimization pick. Everything is Apache 2.0 with no open-core split, and it adds real-time guardrails and an automated agent optimizer the other two lack.
• Phoenix - the phoenix llm tracing pick for OpenTelemetry-first architectures. Built around OTel and OpenInference conventions, local-first and notebook-friendly, with eval primitives inherited from Arize’s ML observability lineage.

What each tool is

• Opik is Comet’s open-source LLM observability and evaluation platform: comprehensive tracing with multimodal logging and agent graph visualization, 30+ LLM-as-judge metrics, online evaluation rules on live traffic, guardrails that block policy violations and PII exposure in real time, and an agent optimizer that automatically tests prompt variants against your metrics.
• Langfuse is the most widely adopted open-source LLM engineering platform: end-to-end tracing, prompt management with versioning, datasets, evaluations, and dashboards on an MIT core. ClickHouse acquired Langfuse in January 2026 alongside a $400M Series D and committed to keeping it open source and self-hostable, with Langfuse Cloud continuing unchanged.
• Arize Phoenix is the open, self-hostable layer of Arize’s observability stack. It grew out of pre-LLM ML observability, which shows in its eval primitives and embeddings analysis, and it is built tightly around OpenTelemetry and the OpenInference semantic conventions rather than a proprietary SDK.

Opik vs Langfuse vs Phoenix: head-to-head

Tracing

All three capture the essentials: nested spans for chains and agents, token counts, latency, and cost per trace.

• Opik stands out for multimodal logging (images, audio, video attached to traces), agent graph visualization for frameworks like LangGraph, and OQL, a query language for filtering trace data at scale.
• Langfuse has the broadest integration surface - its SDKs and framework integrations are the most battle-tested, and sessions, users, and metadata modeling are mature. This is where its adoption lead pays off in documentation and community answers.
• Phoenix treats tracing as OpenTelemetry data first. Instrument once with OpenInference conventions and your telemetry is portable to any OTel backend - the strongest anti-lock-in story of the three, and the reason Phoenix keeps showing up in agent testing stacks like the ones in our AI agent trajectory testing guide.

Evaluation

This is where the platforms diverge most.

• Opik ships 30+ judge metrics covering hallucination, answer relevance, context recall, moderation, and factual correctness, plus online evaluation rules that score a sample of live traffic and fire webhook alerts. The agent optimizer goes a step further than evaluation: it runs meta-prompting and Bayesian search to improve prompts against your metric automatically.
• Langfuse pairs managed evaluators with custom LLM-as-judge evals and closes the loop with datasets: production traces become test cases, experiments run against them, and scores land back on traces. It is the cleanest dataset-to-experiment workflow of the three.
• Phoenix brings eval primitives with an ML-observability pedigree - retrieval relevance, hallucination, and QA correctness evals, plus embeddings analysis and drift detection the others approximate with weaker statistics.

None of the three replaces a dedicated metric library. The common production pattern is still: platform captures traces, a scheduled job runs DeepEval or RAGAS on a sample, and scores are written back to the platform.

Self-hosting

Self-hosted llm evaluation is the reason most teams shortlist these three over LangSmith or Braintrust in the first place - data residency, vendor neutrality, and cost control at scale.

• Phoenix is the lightest: a single container, or even a process inside a notebook. Ideal for local-first development and small production footprints.
• Opik ships Docker Compose and Kubernetes deployments and, critically, keeps its entire feature set - guardrails and online evaluation included - in the open-source build. Nothing sits behind an enterprise key.
• Langfuse is production-grade but the heaviest to operate: the v3 architecture needs Postgres plus ClickHouse plus supporting services. In exchange you get the most scalable open-source option, and the ClickHouse acquisition makes that data layer a first-party concern.

If procurement requires an OSI-approved license, note the split: Opik is Apache 2.0 throughout, Langfuse’s core is MIT, and Phoenix is free to self-host but not OSI open source.

Cloud pricing

Self-hosting is free; the managed clouds are cheap to start and usage-based after that (late-2026 list prices):

Two practical notes. First, “units” and “spans” are not identical - a single request can produce multiple spans, so model your real trace shape before comparing bills. Second, at production volume the platform fee is usually smaller than the LLM judge tokens your evaluations burn; sampling 1-5% of traffic for online evaluation is the biggest cost lever on any of the three.

Verdict by use case

Common pitfalls

• Choosing by star count alone. Adoption matters for community support, but a license mismatch or a missing guardrail feature will hurt more than a smaller community.
• Underestimating Langfuse self-host ops. The Postgres plus ClickHouse architecture is robust, but it is not a weekend deployment. Budget real DevOps time or use the cloud.
• Assuming platform evals replace eval frameworks. Built-in judges cover the generic metrics; application-specific quality still needs DeepEval, RAGAS, or custom judges wired in - see our Promptfoo alternatives guide for how the eval-framework layer is shifting in 2026.
• Scoring every trace. On any platform, unsampled online evaluation multiplies judge-token cost for little added signal. Sample, then re-baseline thresholds after two weeks in production.

Related reading

• Langfuse vs LangSmith vs Braintrust vs Helicone vs Portkey - the wider observability landscape including proprietary platforms
• LangSmith vs Langfuse - managed LangChain-native vs open-source self-hosted
• Langfuse vs Helicone - full platform vs lightweight proxy logging
• Promptfoo alternatives - where the eval-tool layer moved after the OpenAI acquisition
• DeepEval vs RAGAS - the metric libraries that plug into all three platforms

Getting help

We deploy Opik, Langfuse, and Phoenix stacks for Series A-C AI startups shipping production LLM and agent applications. A genai.qa Readiness Assessment delivers a working tracing and evaluation pipeline, calibrated thresholds, and an audit-grade report in 2-3 weeks. Engagements from AED 15k.

Book a free scope call.