DeepEval vs Langfuse (2026)
Updated March 10, 2026
Overview
DeepEval is an open-source LLM evaluation framework built for unit testing AI outputs. It provides 14+ evaluation metrics including hallucination detection, answer relevancy, and contextual recall. Integrates with pytest, supports custom metrics, and works with any LLM provider for automated quality assurance in CI/CD pipelines.
Langfuse is an open-source LLM engineering platform for tracing, evals, and prompt management, self-hosted or on Langfuse Cloud with a free plan.
Key features
Core capabilities each platform advertises.
- Tracing for LLM calls, tool runs, and agent steps
- Python and TypeScript SDKs plus OpenTelemetry
- LLM-as-a-judge and custom evaluations
- Datasets and experiments
- Prompt management and playground
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Open-source
- Comprehensive metrics
Cons
- Manual setup
Pros
- MIT-licensed core, self-hostable with Docker Compose or Kubernetes
- Tracing, evals, and prompt management in one tool
- Unlimited users on paid cloud plans
- Free Hobby plan with 50k units a month
- Native SDKs plus OpenTelemetry for other languages
Cons
- No LLM gateway, so routing, failover, and spend limits need a separate tool
- Hobby plan keeps 30 days of data and caps at 2 users
- Enterprise SSO and RBAC need the $300/month Teams add-on on Pro
- Self-hosting means running and scaling Langfuse's services yourself
DeepEval or Langfuse — which should you choose?
Choose DeepEval if you wantChoose if you want
Choose Langfuse if you wantChoose if you want
- Tracing and debugging LLM apps and agents
- Self-hosting LLM observability
- Versioning prompts across environments
- Scoring outputs with LLM judges and human review
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