Helicone
Helicone is an open-source observability platform for LLM applications that logs calls, analyses cost and latency, offers caching and rate limiting, and integrates as a gateway or library.
Helicone logs model calls as a proxy or via an SDK and shows cost, latency, errors and usage per application. The project is open source and self-hostable — important when prompts must not leave your network.
Explanation
If you cannot see what your AI features do, you cannot steer them. Helicone sits in front of model calls and records: who asked, with which prompt, for how long, at what cost, with what result. From that come the banal but decisive insights — that a third of cost comes from a barely used feature, or that caching could serve repeat requests almost for free.
Marketing Relevance
Cost control for AI features is not a side topic but the most common unpleasant surprise in month three. Observability provides the basis for model decisions and for the question of which feature is worth its cost. With logs containing customer content, self-hosting is often simpler than a debate about data processing terms.
Example
After four weeks of operation, analytics show 62 percent of model cost falls on an internal research feature used by three people. The feature is moved to a smaller model — with the same benefit.
Common Pitfalls
Complete logs contain everything users typed — potentially personal data. Without rules on masking, retention and access, you create a data protection problem in the name of transparency. Second, observability is not evaluation: you see cost and latency, not quality.
Origin & History
Helicone started in 2023 as a lean proxy for call logs and grew into a platform with analytics, caching and gateway features. Its open-source approach made it the standard answer for teams with data protection requirements.
Further Resources
Marketing Use Cases
Cost transparency per application, feature and user group.
Debugging failed or slow model calls.
Caching repeat requests to reduce cost.
Frequently Asked Questions
Helicone or Langfuse?
Both are open source and overlap. Helicone comes from the gateway angle with strengths in cost and caching, Langfuse from tracing with strengths in nested flows and evaluation.
Can I self-host Helicone?
Yes, and that is one of the main reasons to pick it. Prompts and responses then stay in your own environment.
Related Services
Go deeper: Agentic AI Hub · Model comparison 2026