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Composo vs Langfuse
Langfuse is an open-source tracing and observability platform - it captures what your AI did. Composo is a quality layer that sits on top of tracing and tells you what went wrong and how to fix it. Many Composo customers use Langfuse as their trace backend and Composo as their evaluation layer.
Langfuse surfaces what you tell it to check. Composo surfaces what you do not know to check.
At a glance
| Dimension | Composo | Langfuse |
|---|---|---|
| Primary function | Quality evaluation, failure detection, runtime guardrails | Tracing and observability |
| Source model | Closed source, deployed as a service | Open source with hosted SaaS option |
| Evaluation | Domain-calibrated reward model, learns from corrections | Lightweight eval features built on LLM-as-judge templates |
| Deployment effort | 2 to 4 week FDE deployment | Self-host in hours; hosted SaaS in minutes |
| Fits alongside | Any tracing layer (Langfuse, LangSmith, Datadog, custom) | Any LLM framework (LangChain, LlamaIndex, OpenAI SDK, custom) |
| Best for | Teams needing domain-specific quality at production scale | Teams needing trace visibility and a shared debugging view |
Where Langfuse is strong
- Open source and self-hostable. Full control over your data. The hosted SaaS is also priced fairly. The bar to start using Langfuse is very low.
- Strong trace visualisation. Good UI for inspecting agent runs, nested tool calls, and prompt trajectories.
- Broad LLM framework support. Works with LangChain, LlamaIndex, OpenAI SDK, and custom frameworks.
- Prompt management and versioning. Solid primitives for managing prompts as code alongside traces.
- Active community. Fast-moving open-source project with good documentation.
Where Composo is different
- Langfuse is a tracing tool with eval features. Composo is an evaluation platform. Langfuse's eval module is built on LLM-as-judge templates that you configure. Composo is a reward-model-based system calibrated to your specific failure modes.
- Catches failures your evals miss. Langfuse surfaces whatever you instrument it to surface. Composo learns the ways your AI fails from your traces and surfaces patterns you did not know to look for.
- Domain-specific calibration. A clinical-AI evaluation is different from a financial-AI evaluation is different from a legal-AI evaluation. Composo calibrates the evaluation model to your domain in the first two weeks.
- Deployed, not configured. A senior engineer deploys Composo into your stack, calibrates it, and hands it over. You do not write your own evaluator prompts.
- Composo integrates with Langfuse. If your traces are already in Langfuse, Composo reads them. You do not have to migrate.
When to pick which
Pick Langfuse if
- · You need trace visibility and a shared debugging view across your team
- · You prefer open source and self-hosting
- · Your evaluation needs are simple and you are comfortable writing LLM-as-judge prompts
- · You are early in the AI lifecycle and primarily need visibility, not catching failures at scale
Pick Composo if
- · You already have tracing (Langfuse or other) and the gap is evaluation quality
- · You need domain-specific failure detection, not generic checks
- · You want a deployed system calibrated to your AI, not a configurable tool
- · Regulatory or brand risk means silent failures are unacceptable
Frequently asked questions
Can I use Composo on top of Langfuse?
Yes. Composo reads traces from Langfuse (hosted or self-hosted) and runs evaluation on them. You do not need to migrate your tracing layer to use Composo.
Does Composo replace Langfuse?
No. Composo is not a tracing platform. It is a quality layer that sits on top of tracing. Many Composo customers keep Langfuse as their trace backend and use Composo for evaluation, failure detection, and guardrails.
Why use Composo if Langfuse has evals built in?
Langfuse's evals are built on LLM-as-judge prompts you configure. They work for simple quality checks but miss domain-specific failure modes and typically plateau around 70% alignment with human domain experts. Composo's reward-model approach, calibrated to your domain, reaches 90%+ alignment with human experts in most production contexts and catches failures that basic LLM-as-judge consistently misses.
Is Composo open source?
No. Composo is closed source, deployed as a service. The evaluation models are proprietary. Customer trace data remains the customer's property and is never used to train foundation models.
How long does it take to get value from Composo vs Langfuse?
Langfuse can be integrated in under an hour. Composo takes 2 to 4 weeks to deploy and calibrate to your domain. The trade-off is depth: Composo arrives with a domain-specific failure taxonomy, while Langfuse arrives as a blank tracing layer for you to instrument.
See what Composo catches on your own AI.
A clinical-quality failure report on your production AI, delivered in under a week.