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AI Tool Comparison
Helicone vs Langfuse
A side-by-side breakdown to help you pick the right tool for your workflow.
Helicone
Track and debug every LLM API call, with visibility into cost, latency, and errors across an AI application — hosted or self-hosted open source.
Developer Tools
freemium
Langfuse
Trace and score LLM application runs so teams can debug agent behavior and track cost per user or session.
Developer Tools
freemium
Bottom Line
Langfuse edges ahead on rating (4.7 vs 4.5), but the right pick still comes down to which workflow you're running.
Choose Helicone if…
LLM Observability
Choose Langfuse if…
LLM Observability
| Attribute | Helicone | Langfuse |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Pricing | freemium | freemium |
| Pricing Detail | Free (10K requests) / $79/mo Pro / $799/mo Team | Free (50K units) / $29/mo Core / $199/mo Pro |
| Rating |
Key Features
Helicone
- Proxy-based setup, one line of code
- Cost and latency dashboards
- Prompt versioning
- Caching to reduce API costs
- A/B testing models
- Team dashboards
Langfuse
- Full LLM call tracing
- Prompt version management
- User session tracking
- Cost and latency analytics
- Evaluation datasets
- Self-hostable
Pros
Helicone
- •Fastest observability setup in the category, no SDK required
- •Significant cost savings from intelligent caching
- •Works with all major LLM providers
Langfuse
- •One of the best open-source options in LLM observability
- •Works with any LLM provider
- •Eval framework helps catch quality regressions early
Cons
Helicone
- Proxy adds a small latency overhead
- Less evaluation depth than Langfuse or Braintrust
Langfuse
- Setup requires SDK integration in your codebase
- Dashboard can feel complex for simple use cases