Helicone vs Langfuse
Helicone and Langfuse are the two leading open-source LLM observability platforms. Both ship a generous free tier, both are self-hostable, both support tracing across major LLM providers. The differences are about scope and price tiers.
Side-by-side
Pick Helicone if
- You want the cheapest path to LLM observability: 10K free requests/mo on Hobby
- You'll integrate via a drop-in proxy URL, not SDKs: fastest to production
- You're a startup under 2 years, under $5M funding: 50% off first year on Pro
Pick Langfuse if
- You need observability + evals + datasets in one product, not just tracing
- You'll integrate via SDKs (LangChain, LangGraph, Vercel AI SDK have native support)
- You're early-stage but expect to need SOC 2 + HIPAA: Langfuse Pro at $199/mo gates these, vs Helicone's $799/mo
The verdict
Both are credible. Helicone wins on time-to-first-trace: its proxy approach (point your OpenAI/Anthropic SDK at helicone.ai) puts data in the dashboard within minutes without code changes. Langfuse wins on scope: tracing is one of four products it ships (the others are evals, prompt management, and datasets), useful when you're building a real LLM application that needs offline evaluation, not just observability. Pricing diverges sharply on compliance gating. Helicone's SOC 2 + HIPAA tier is $799/mo (Team); Langfuse's is $199/mo (Pro). For a regulated SMB, Langfuse is dramatically cheaper. For non-regulated startups optimising for time-to-value, Helicone's $79 Pro tier is the better entry point. Both are open-source and self-hostable; both have generous free tiers. If you only need request logging, pick Helicone. If you'll need evals and prompt versioning eventually, Langfuse's bundled approach saves switching costs.
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