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AI Tool Comparison
Langfuse vs LiteLLM
A side-by-side breakdown to help you pick the right tool for your workflow.
Langfuse
Trace and score LLM application runs so teams can debug agent behavior and track cost per user or session.
Developer Tools
freemium
LiteLLM
Call 100+ LLMs with the same OpenAI code you already have. LiteLLM handles the translation, tracks costs, runs fallbacks, and proxies for your whole team.
Developer Tools
free
Bottom Line
Langfuse and LiteLLM are rated evenly — the right pick comes down to which workflow you're running.
Choose Langfuse if…
LLM Observability
Choose LiteLLM if…
Coding
| Attribute | Langfuse | LiteLLM |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Pricing | freemium | free |
| Pricing Detail | Free (50K units) / $29/mo Core / $199/mo Pro | Open source / Free (Enterprise proxy available) |
| Rating |
Key Features
Langfuse
- Full LLM call tracing
- Prompt version management
- User session tracking
- Cost and latency analytics
- Evaluation datasets
- Self-hostable
LiteLLM
- OpenAI-compatible interface for 100+ LLM providers
- Proxy server mode with centralized API key management
- Per-model and per-user cost tracking with budget limits
- Automatic fallback and load balancing across providers
- Streaming response support across all providers
- Integrations with Langfuse, Helicone, and other observability tools
Pros
Langfuse
- •One of the best open-source options in LLM observability
- •Works with any LLM provider
- •Eval framework helps catch quality regressions early
LiteLLM
- •Zero vendor lock-in — swap any provider with one config line
- •Largest provider coverage of any LLM abstraction layer
- •Fully open source with a large and active community
Cons
Langfuse
- Setup requires SDK integration in your codebase
- Dashboard can feel complex for simple use cases
LiteLLM
- Self-hosting the proxy adds operational overhead for teams
- SSO and audit log features require the paid enterprise tier
- Occasional lag keeping up with very new model API releases