Helicone

Helicone is a lightweight LLM observability tool that prioritises ease of integration over feature depth. Where Langfuse and LangSmith require more setup investment for full value, Helicone can be integrated by changing a single API URL — routing OpenAI or Anthropic API calls through Helicone's proxy automatically captures all requests for logging and analysis.

The proxy-based architecture is Helicone's key design decision. Instead of adding SDK calls throughout application code, developers replace their OpenAI API base URL with Helicone's proxy URL and all subsequent requests are automatically logged, tracked for cost, and monitored for latency. For teams that want basic observability with minimal engineering overhead, this is the lowest-friction option available.

The dashboard shows request volume, cost breakdown by model, latency percentiles, error rates, and a log of every API request with full request and response content for debugging. For teams spending significant amounts on AI API costs, the cost visibility alone provides value by identifying inefficient prompts or unexpected usage patterns.

Caching is a practical feature that saves cost and latency by returning cached responses for identical or semantically similar requests. For applications with repetitive queries, caching can reduce API costs substantially.

Helicone is open source and can be self-hosted for teams with data privacy requirements that preclude routing production AI traffic through a third-party proxy. The cloud version is convenient for teams that do not have these constraints.

For teams needing advanced evaluation, prompt management, or dataset curation, Langfuse or Vellum are more comprehensive. For straightforward cost monitoring and request logging with minimal setup, Helicone is the fastest path to LLM observability.