Which is better: Langfuse or Helicone?

There's no single 'better' — they target different points in the Eval & Observability space. Pick by your install constraints (license, MCP-native vs generic, hosting model) and the tag overlap above.

Can I use both Langfuse and Helicone?

Usually yes. Both are eval & observability — they're not mutually exclusive in most stacks. Many teams run both during evaluation and standardize on one after a sprint.

Where do the recommendations come from?

ClaudSkills Arcade is an open community catalog. Discovery is mined from 7 public sources daily. Ranking is content-derived — no paid placements.

Langfuse vs Helicone — Eval & Observability compared on ClaudSkills Arcade

Langfuse

OSS, self-hostable LLM observability — traces, evals, prompts, and datasets.

License: MIT
Install: pip package
Website: https://langfuse.com
GitHub: https://github.com/langfuse/langfuse

View Langfuse →

pip install langfuse

Helicone

One-line proxy for LLM logging, caching, rate limiting, and cost tracking.

License: Apache-2.0
Install: env key
Website: https://www.helicone.ai
GitHub: https://github.com/Helicone/helicone

View Helicone →

export HELICONE_API_KEY=...

Shared tags

observability

Only in Langfuse

eval open-source tracing

Only in Helicone

caching logging proxy

When to reach for which

Langfuse — OSS, self-hostable LLM observability — traces, evals, prompts, and datasets.

Helicone — One-line proxy for LLM logging, caching, rate limiting, and cost tracking.

Both sit in the Eval & Observability category — they're substitutes, not complements. Pick by your install constraint (MCP-native, license, hosting model) and which tag overlap matters most to your stack.

More Eval & Observability comparisons

See all comparisons →

LangfusevsHelicone