Braintrust vs Langfuse vs Laminar vs Arize Phoenix
The LLM observability and eval space did not consolidate in 2026 — it fractured along workflow lines. Braintrust is eval-first: if prompt regression in CI is your primary pain, it is the strongest option. Langfuse is prompt-management-first and is now Clickhouse-owned (acquired January 2026), with a large open-source community and strong tracing. Laminar is agent-debugging-first: purpose-built for tracing multi-step agent runs. Phoenix (Arize AI) is OpenTelemetry-native and the natural choice for teams already on OTel infrastructure. Most teams in production run two of these, not one.
Find your tool
Answer 5 questions to get a scored recommendation.
Question 1 of 5
Which pain hurts most today?
Be honest about which workflow is actually broken, not which you think should be fixed first.
Question 2 of 5
Is open-source self-hosting required?
Question 3 of 5
Do you have existing OpenTelemetry investment?
If your services already emit OTel traces, Phoenix can ingest LLM traces into the same pipeline.
Question 4 of 5
Which best describes your team's profile?
Question 5 of 5
Budget sensitivity?
Comparison matrix
10 dimensions across 4 tools.
| Dimension | Braintrust | Langfuse | Laminar | Arize Phoenix |
|---|---|---|---|---|
| Licence and self-host | Closed source; cloud SaaS only; no self-host option | MIT open source; self-host or Langfuse Cloud; Clickhouse-backed | Apache 2.0; self-host or Laminar Cloud | Elastic 2.0 (source available); self-host or Arize Cloud |
| Primary strength | Eval datasets, scoring functions, and CI-gated prompt regression | Prompt versioning, A/B testing, and high-volume tracing at Clickhouse scale | Multi-step agent trace visualisation and agent workflow debugging | OpenTelemetry-native ingestion and RAG eval metrics (faithfulness, relevance) |
| Tracing model | Proprietary tracing SDK; spans and traces for LLM calls | Proprietary SDK + OpenTelemetry ingest; detailed span metadata | Proprietary SDK; agent-step-aware trace structure | OpenTelemetry (OpenInference / OpenLLMetry); first-class OTel support |
| Prompt management | Prompt versioning with linked eval results; deployment-aware | First-class prompt versioning, A/B testing, and production deployment tracking | Basic prompt management; not a primary focus | Not a primary feature |
| Eval harness | Purpose-built: dataset management, scoring functions, CI integration, human review UI | Eval runs and scoring; less opinionated than Braintrust on CI workflow | Eval support present; not the primary use case | Strong RAG-specific metrics (faithfulness, context precision/recall); Phoenix Evals library |
| Agent debugging UX | Span-level trace view; adequate but not purpose-built for agents | Good trace visualisation; step-level nested spans | Agent-step-aware trace UI; purpose-built for multi-step agent debugging | OTel trace view; good for service-level agent debugging |
| CI/CD integration | First-class: `braintrust eval` command, GitHub Action, score threshold enforcement | API-based; eval runs can be triggered from CI; no dedicated CI action | API-based; CI integration requires custom scripts | API-based; integrates via OTel pipeline; no dedicated CI action |
| OpenTelemetry support | Limited; proprietary SDK preferred | OTel ingest supported; growing investment post-acquisition | OTel ingest supported; growing | First-class OTel support via OpenInference and OpenLLMetry; designed for OTel |
| Pricing model (May 2026) | Freemium; paid plans from ~$100/month; enterprise pricing on request | Free self-host (MIT); Langfuse Cloud free tier + usage-based paid; enterprise available | Free self-host (Apache 2.0); Laminar Cloud free tier + usage-based; enterprise available | Free self-host; Arize Cloud has enterprise pricing; Phoenix OSS is free to run |
| Notable users / adoption signals | Used by several YC companies and AI-native startups; strong in eval-first teams | Large open-source community; 10k+ GitHub stars; Clickhouse backing | Growing adoption in agentic workflow teams; backed by Y Combinator | Used by ML teams already on Arize platform; strong in enterprise MLOps |
Honest verdicts
When each tool is the right call, and when it isn't.
Shines when
- Best-in-class eval dataset management and CI integration
- Scoring function library covers common eval patterns out of the box
- Human review UI makes annotation workflows efficient
- Deployment-linked prompt versioning ties eval results to specific releases
Falls down when
- Closed source — no self-host option; data leaves your infrastructure
- Weaker than alternatives for agent-step debugging
- Pricing starts at $0 but scales quickly with team size and trace volume
Braintrust is the clearest choice for teams who treat prompt regression as a CI problem and need strong dataset management.
Shines when
- Best prompt versioning and A/B testing workflow of the four options
- MIT licence with strong self-host path; Clickhouse-backed for scale
- Large, active open-source community with wide SDK coverage
- Good tracing for standard LLM call patterns
Falls down when
- Clickhouse acquisition introduces strategic uncertainty about long-term roadmap
- Eval harness is present but less opinionated than Braintrust's
- Agent-step debugging UI is adequate, not purpose-built
Langfuse is the default choice for teams who need prompt management and versioning with a self-host option they can trust.
Shines when
- Purpose-built agent-step trace visualisation — genuinely better than alternatives for multi-step agent debugging
- Apache 2.0 licence with real self-host path
- Y Combinator-backed with focused product development in 2025–2026
Falls down when
- Narrower feature set than Langfuse or Braintrust — not the right choice if you need broad eval harness
- Smaller community and ecosystem than alternatives
- Prompt management features are basic
Laminar is the right choice for teams whose primary pain is debugging multi-step agent workflows; add a second tool for eval if needed.
Shines when
- Best OpenTelemetry integration — designed for teams already on OTel infrastructure
- Strong RAG-specific eval metrics (faithfulness, context precision, context recall)
- Free to self-host (Elastic 2.0) with real production viability
- Natural extension for ML teams already using the Arize platform
Falls down when
- Elastic 2.0 licence has restrictions for commercial redistribution
- Weaker prompt management than Langfuse
- UI is more ML-platform-oriented than QA-workflow-oriented
Arize Phoenix is the clear choice for teams already on OpenTelemetry or the Arize ML platform; others should evaluate Braintrust or Langfuse first.