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.
Langfuse
Trace and score LLM application runs so teams can debug agent behavior and track cost per user or session.
Bottom Line
Last reviewed: August 2026
Helicone and Langfuse both compete in Developer Tools, overlapping most directly on lLM Observability. Langfuse carries the higher rating (4.7 vs 4.5), but a gap that size rarely overrides a real workflow fit on its own.
Choose Helicone if…
Best for developers who want instant LLM call visibility without changing their existing SDK integration, and its edge is a proxy-based setup that requires one line of integration, no SDK rewrite, to get logging, cost tracking, and caching. The fastest observability setup in the category, Langfuse still goes deeper on evaluation.
Choose Langfuse if…
Best for engineering teams who need production visibility into LLM application behavior that standard monitoring tools miss, and its edge is one of the strongest open-source LLM observability platforms, working with any provider rather than locking you in. A genuinely capable eval and monitoring layer, setup requires real SDK integration into your codebase.
| 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