Arize Phoenix vs Langfuse
Detailed side-by-side comparison to help you choose the right tool
Arize Phoenix
🟡Low CodeAI observability
Arize Phoenix provides open-source tracing, evaluations, and MCP tooling for ai observability workflows. It includes MCP compatibility.
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FreeLangfuse
🔴DeveloperAI observability
An open-source observability and evaluation platform for language-model applications.
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FreeFeature Comparison
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Arize Phoenix - Pros & Cons
Pros
- ✓Open-source and self-hostable
- ✓Trace timelines expose intermediate failures
- ✓AX Free includes 25,000 monthly spans
- ✓Datasets support regression testing
Cons
- ✗Instrumentation and evaluator design require engineering
- ✗Pro includes only 50,000 spans and 30-day retention
- ✗Self-hosting shifts operations to the user
- ✗Unredacted traces can capture sensitive data
Langfuse - Pros & Cons
Pros
- ✓Open source with free self-hosting — full feature parity without usage limits
- ✓Free Hobby tier on cloud with no credit card — lowest barrier to entry in the category
- ✓Trace graphs for multi-agent systems are genuinely useful for debugging complex failures
- ✓Prompt management + evals turns prompt engineering into a systematic, measurable process
- ✓40,000+ builders using it — extensive community resources and integrations
- ✓Integrates natively with LangChain, LlamaIndex, OpenAI SDK, and Anthropic
Cons
- ✗Pro plan units pricing ($8/100k) can add up for high-volume production applications
- ✗Enterprise SSO requires the $300/month Teams add-on on top of Pro — costly for mid-size teams
- ✗Self-hosting requires Docker/Kubernetes operational knowledge
- ✗UI can feel overwhelming for teams who just want simple cost/latency dashboards
- ✗Real-time alerting features are less developed than commercial-first alternatives like Arize
- ✗Enterprise tier at $2,499/month is priced for large organizations — no mid-market option
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