Langfuse vs Opik: Self-Hosted LLM Observability Compared

The Langfuse vs Opik decision comes down to two different bets on where the value sits, since both are fully open source LLM observability platforms you can self-host for free. Langfuse (MIT license) is framework-agnostic, ships with a ClickHouse-backed columnar storage layer, and just got a capital and engineering boost when the database company ClickHouse acquired it in January 2026. Opik (Apache-2.0), built by Comet, bets on automated evaluation and prompt optimization baked into the core product. If your team runs a mixed stack of frameworks and wants the broadest integration surface plus infrastructure backed by a database vendor, Langfuse is the safer default. If your team wants a tool that automatically tunes prompts and agent tool schemas instead of just logging them, Opik's built-in optimizer is the differentiator. Here is the full self-hosting comparison.

Key takeaways:

• Both tools are genuinely open source: Langfuse is MIT licensed, Opik is Apache-2.0, and both can be self-hosted for free with no artificial feature gate on the core product.
• ClickHouse acquired Langfuse on January 16, 2026, alongside a $400 million Series D, and committed to keeping the MIT license and self-hosting path unchanged.
• Opik ships a built-in "Agent Optimizer" with seven optimization algorithms; Langfuse focuses on manual prompt management, versioning, and dashboards instead of automated tuning.
• Langfuse currently counts roughly 30,000 GitHub stars against Opik's roughly 20,000 (checked live in July 2026), reflecting Langfuse's longer head start in the space.

What changed: ClickHouse acquired Langfuse

Langfuse is an open source platform for tracing, evaluating, and managing prompts for LLM applications, originally built by three cofounders who launched through Y Combinator in 2023. On January 16, 2026, ClickHouse, the company behind the open source columnar database of the same name, announced it had acquired Langfuse the same day it closed a $400 million Series D round (Source: ClickHouse Blog).

The acquisition matters for self-hosters specifically because Langfuse already ran on ClickHouse's database as its production data layer before the deal closed, so the acquisition formalized an existing technical dependency into shared ownership. At the time of the deal, Langfuse reported 20,000-plus GitHub stars, over 23 million monthly SDK installs, and customers including 19 of the Fortune 50 (Source: ClickHouse Blog).

Langfuse's cofounders were explicit about what would not change: "Langfuse stays open source and self-hostable. There are no planned changes to licensing." They added that Langfuse Cloud "keeps running as-is. Same product, same endpoints, same experience" (Source: Langfuse Blog). For a self-hoster evaluating Opik instead, that commitment removes the biggest risk of picking the acquired product: a rug pull on the license.

Merck's Chief Data and AI Officer, Walid Mehanna, framed the underlying demand this acquisition responds to: "Generative AI will only earn enterprise trust when we can see what's happening under the hood" (Source: ClickHouse Blog). That is the same demand Opik is built to serve, just with a different feature emphasis, which is why the comparison below matters more than the acquisition headline alone.

At-a-glance: Langfuse vs Opik

The table below is the decision surface most self-hosting teams actually weigh.

(Source: langfuse/langfuse GitHub, comet-ml/opik GitHub, BigDataBoutique)

License and self-hosting footprint

Both projects clear the bar that matters most for a self-hoster: the core product is free to run in your own infrastructure with no artificial cap. Langfuse's MIT license covers tracing, prompt management, evaluation, and datasets. Some enterprise features sit in a separate /ee directory under different terms, so check that folder first if you need SSO or audit logs at the free tier (Source: langfuse/langfuse GitHub). Opik is Apache-2.0 across the board, including the Agent Optimizer SDK, which the vendor states is "fully available under an Opik open-source license" (Source: Comet Agent Optimizer page).

Operator note (first-hand): I checked both projects' current READMEs on GitHub in July 2026 to confirm the actual bootstrap commands rather than trust marketing copy. Langfuse's quick start is git clone --depth=1 https://github.com/langfuse/langfuse.git && cd langfuse && docker compose up, which brings up Langfuse, Postgres, and ClickHouse as containers and is genuinely usable in under five minutes. Opik's quick start runs ./opik.sh, a wrapper script that supports service profiles such as --infra, --backend, and --guardrails. That lets you bring up only the pieces you need, a cleaner story for teams that want to skip the guardrails module entirely (Source: comet-ml/opik GitHub).

For production self-hosting, both projects document Kubernetes and Helm paths; Langfuse additionally ships Terraform modules for AWS, Azure, and GCP, which is a meaningful head start if your infrastructure team already manages state in Terraform.

Framework and integration coverage

Langfuse was built framework-agnostic from the start, and it shows: LangChain, LlamaIndex, the OpenAI SDK, LiteLLM, and the Vercel AI SDK are all first-class. Langfuse also functions as a generic OpenTelemetry (OTel) backend, the open standard for collecting traces and metrics, so any framework or hand-rolled agent that emits OTel spans gets traced without a dedicated SDK (Source: BigDataBoutique).

Opik's integration count is larger in raw numbers: more than 60 integrations including OpenAI, LangChain, LlamaIndex, Anthropic, CrewAI, AutoGen, AG2, and Google ADK, plus a native OpenTelemetry path (Source: comet-ml/opik GitHub). The practical difference shows up less in breadth and more in depth of ecosystem maturity: Langfuse's dashboards support drag-and-drop custom widgets and public trace sharing via URL, features Opik's comparison page acknowledges Langfuse does well (Source: BigDataBoutique).

If your stack is a single well-supported framework like LangChain, either tool covers you. If your stack mixes frameworks, hand-rolled agents, and multiple LLM providers, Langfuse's framework-agnostic design and OTel-first posture reduce the number of custom integrations you have to maintain.

Evaluation and prompt optimization

This is where the two tools diverge on philosophy, not just feature count. Langfuse treats evaluation as a scoring and dataset problem: LLM-as-judge evaluators, code-based evaluators, user feedback capture, and dataset management for regression testing, all surfaced through dashboards a human reviews before acting.

Opik goes further with the Opik Agent Optimizer, a dedicated SDK that automatically tunes prompts, tool schemas, and multi-step agent workflows using the traces and metrics already logged to Opik. It ships seven algorithms. They include a Few-Shot Bayesian optimizer, a Hierarchical Reflective Prompt Optimizer that runs root-cause analysis on failed outputs, an evolutionary optimizer using genetic mutation and crossover, and a tool-optimization mode. That last one tunes MCP (Model Context Protocol) tool schemas and function-calling definitions directly, not just the system prompt around them (Source: Comet Agent Optimizer page).

Dmitrii Krasnov, an engineering manager at Zencoder, described the payoff in observability terms first: "We needed a solution that allowed us to see how our models behaved, and Opik gives us the ability to understand what went wrong" (Source: Comet Agent Optimizer page). The optimizer is the layer built on top of that visibility. For a team that has already accepted the operational cost of self-hosting and wants the platform to close the loop from "here is what broke" to "here is a better prompt," Opik's optimizer is not available in Langfuse without wiring a separate tool.

Pricing and lock-in

Self-hosted core features are free under both licenses, so the real cost is infrastructure plus any optional cloud tier you layer on top. Langfuse's cloud has a free Hobby tier around 50,000 units per month with 30-day retention, plus paid cloud plans starting near $29 per month for higher volume. Enterprise cloud contracts run considerably higher for teams that want managed scale instead of self-hosting (Source: BigDataBoutique). Opik's cloud free tier covers roughly 25,000 spans per month with unlimited team members, and its Pro tier starts around $19 to $39 per month depending on the source consulted, with additional volume billed per span (Source: BigDataBoutique).

Lock-in risk is low on both sides if you stay self-hosted: MIT and Apache-2.0 both let you fork, modify, and export your data without a vendor's permission. The practical lock-in to watch is subtler. Langfuse's enterprise features live behind a separate EE license, so a team that grows into needing SSO or fine-grained access control should budget for that before committing. Opik's Agent Optimizer is open source, but its most useful workflows assume you are also comfortable running experiments against Comet's broader ML platform conventions, which is a soft pull toward the Comet ecosystem even though nothing is technically locked.

Which should you pick

Pick Langfuse if your team runs multiple frameworks or providers, wants an OpenTelemetry-native backend that does not assume any single stack, and values that the project is now backed by a database company with a direct financial interest in the storage layer underneath it. The Docker Compose bootstrap and Terraform modules make it the lower-friction self-host for teams that already run infrastructure-as-code.

Pick Opik if your priority is closing the loop from evaluation to improvement without hand-building an optimization pipeline, especially for agents with tool calls and MCP integrations you want tuned automatically rather than edited by hand. The tradeoff is a younger ecosystem: independent comparisons of the two tools are still thin, and the most detailed side-by-side available today is Comet's own product page rather than a neutral third party (Source: BigDataBoutique).

Teams running both should not feel like they made a mistake. Nothing prevents self-hosting Langfuse for tracing and dashboards while running Opik's optimizer against the same underlying prompts, though most teams standardize on one to avoid maintaining two trace pipelines.

FAQ

Is Opik open source?

Yes. Opik is released under the Apache-2.0 license, and the core platform, including the Opik Agent Optimizer SDK, is free to self-host with no paywalled core features (Source: comet-ml/opik GitHub). Comet also sells a hosted cloud tier for teams that prefer not to self-host.

Is Opik free?

Opik's self-hosted deployment is free under the Apache-2.0 license, with no cap on core tracing or evaluation features. Comet's hosted cloud alternative also has a free tier, covering roughly 25,000 spans per month with unlimited team members, plus paid tiers for higher trace volume or managed infrastructure (Source: BigDataBoutique).

Can you self-host Langfuse for free?

Yes. Langfuse's core platform is MIT licensed and self-hosting is free with no artificial usage cap; the quick start is a single docker compose up command from the official repository. Some enterprise-only features sit under a separate EE license (Source: langfuse/langfuse GitHub).

Does Opik do automated prompt optimization?

Yes, through the Opik Agent Optimizer, a dedicated SDK with seven optimization algorithms that can tune system prompts, few-shot examples, LLM call parameters, and tool or MCP schemas automatically using traces already logged to Opik (Source: Comet Agent Optimizer page). Langfuse does not ship an equivalent automated optimizer; it focuses on manual prompt versioning and scoring.

What is the difference between Langfuse, Opik, and Comet?

Langfuse and Opik are the two open source observability platforms being compared here. Comet ML is the company that builds Opik; Comet also runs a separate, older ML experiment-tracking platform. Langfuse is now owned by ClickHouse, the database company, following the January 2026 acquisition (Source: ClickHouse Blog).

Which is better for production agents?

Neither wins outright. Langfuse fits teams running mixed frameworks that want OpenTelemetry-native tracing and ClickHouse-backed scale. Opik fits teams that want automated prompt and tool optimization built into the same platform that captures the traces, particularly for agents with MCP tool calls.

Related coverage

• Langfuse vs LangSmith Self-Hosted: Which to Pick in 2026
• LangSmith vs Arize Phoenix: Cost Breakdown for Self-Hosted Agents
• Agent Eval as Infrastructure: Benchmarks and Observability in 2026

References

• BigDataBoutique - https://bigdataboutique.com/blog/llm-observability-tools-compared-langfuse-vs-langsmith-vs-opik
• ClickHouse Blog - https://clickhouse.com/blog/clickhouse-acquires-langfuse-open-source-llm-observability
• Comet Agent Optimizer page - https://www.comet.com/site/products/opik/features/automatic-prompt-optimization/
• comet-ml/opik GitHub - https://github.com/comet-ml/opik
• Langfuse Blog - https://langfuse.com/blog/joining-clickhouse
• langfuse/langfuse GitHub - https://github.com/langfuse/langfuse