Model Comparison
Langfuse vs Braintrust vs Phoenix
Three observability options for production LLM apps — open-source tracing, eval-first SaaS, and RAG-focused OSS from Arize.
Verdict
Self-host tracing + cost control: Langfuse. Eval-first quality loops: Braintrust. RAG debugging and OSS: Phoenix.
Edited by The AIKnowHub team · Editorial teamAt a glance
Langfuse
WinnerBest for
Production traces, prompt versions, cost tracking, self-host
Not for
Teams that want zero ops and evals-only focus
Braintrust
StrongBest for
Eval datasets, regression tests, trace-to-experiment pipeline
Not for
Self-hosting or tight infra budgets
Phoenix
SolidBest for
RAG span inspection, embedding visualization, local debugging
Not for
Full prompt management and team collaboration
| Dimension | Langfuse | Braintrust | Phoenix |
|---|---|---|---|
Self-hosting | Yes | No | Yes |
Trace visualization | Best | Strong | Strong |
Eval workflows | Solid | Best | Moderate |
RAG / retrieval debugging | Solid | Moderate | Best |
Cost tracking | Best | Moderate | Weakest |
Prompt versioning | Best | Strong | Weakest |
Time to first trace | Moderate | Fastest | Moderate |
Pricing at scale | Best (self-host) | Moderate | Best (OSS) |
Best pick by use case
| If you're doing… | Pick | Why |
|---|---|---|
| Startup shipping first agent to production | Langfuse | Self-host on Fly.io, trace every call, track cost per feature from day one. |
| PM and eng running weekly eval regressions | Braintrust | Traces become eval cases; experiments are first-class. |
| RAG answers are wrong and you need to see retrieval | Phoenix | Embedding and retrieval span visualization pinpoints chunk failures. |
| Fintech with data residency requirements | Langfuse | Self-host keeps traces inside your VPC. |
| Research team iterating on prompts with scoring rubrics | Braintrust | Scoring UX and dataset management beat rolling your own. |
The short version
| Need | Pick |
|---|---|
| Self-hosted production traces | Langfuse |
| Evals and experiments as core workflow | Braintrust |
| Debug RAG retrieval locally | Phoenix |
| Cost per user/feature tracking | Langfuse |
| Zero ops, fastest onboarding | Braintrust |
See LLM Observability for concepts, and the support triage case study for Langfuse in production.
Langfuse — the default OSS tracer
Langfuse traces LLM calls, versions prompts, tracks cost, and supports eval datasets. Self-host or use cloud.
Pick when: you want framework-agnostic tracing under your control. The support triage case study logs every draft-to-sent diff here.
Braintrust — evals first
Braintrust connects production traces to eval datasets and experiments. Strong scoring UX for PM + eng collaboration.
Pick when: quality regression testing is as important as uptime. Best fit for teams with dedicated eval cadence (weekly or per-release).
Phoenix — RAG microscope
Phoenix from Arize is OSS tracing with exceptional RAG and embedding visualization.
Pick when: answers are wrong and you suspect retrieval, not generation. Inspect which chunks arrived, how they scored, and where embeddings cluster.
What to instrument
Minimum viable observability for any agent:
- Input hash — what went in (not always raw PII)
- Model + params — which model, temperature, token limits
- Latency + cost — per request and per user
- Tool calls — name, args, result status
- Output + human override — what shipped vs what the model proposed
All three tools support most of this. Langfuse and Braintrust add prompt versioning; Phoenix adds retrieval forensics.
Related
Frequently asked questions
Related on AIKnowHub
Tool Guide
Langfuse Guide
The definitive guide to Langfuse for LLM observability — traces, prompt versions, evals, cost tracking, self-host setup, and when to pick it over Braintrust or Phoenix.
Concept
Evals Explained
Evals are the unit tests of AI systems. Without them you're flying blind. Here's how to build a useful eval set without going overboard.
Concept
LLM Observability in Production
You can't debug what you can't see. LLM observability means tracing every prompt, token, latency spike, and failure — here's the production stack.
Directory
Braintrust
AI evaluation and observability platform — traces feed directly into eval datasets, regression tests, and prompt experiments.
Directory
Langfuse
Open-source LLM observability — traces, evals, prompt versioning, and cost dashboards for production AI apps.
Case Study
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