Helicone is the open source LLM observability platform that sits between your application and any LLM API, capturing every request and response so you can monitor costs, debug failures, and iterate on prompts without guessing at production behavior.
The Problem
LLM API calls are opaque by default. You send a prompt, get a response, and your logs show an HTTP 200. You have no visibility into which model version handled the request, how much it cost, whether latency spiked on a specific user's session, or why a request returned a degraded result. Datadog and similar APM tools cover infrastructure metrics but do not understand the semantic structure of LLM interactions.
How Helicone Solves It
Helicone proxies your API calls — one URL change, no SDK required — and indexes every request in its dashboard. Add a user ID header and costs are automatically segmented per user. Add a prompt version tag and you can A/B test two prompts in production and compare outputs side by side. The prompt experiments feature replays historical requests against a new prompt without calling the live API.
Key Features
- One-line integration: change api.openai.com to oai.helicone.ai and logging starts immediately
- Cost tracking per user, session, model, and custom property
- Request search and replay for debugging failed or degraded responses
- Prompt versioning and A/B experiments against historical traffic
- Rate limiting and caching to reduce API spend
- Supports OpenAI, Anthropic, Azure OpenAI, Gemini, and any OpenAI-compatible API
Who It's For
Helicone is best for AI application developers who need production visibility into LLM costs and behavior, teams debugging prompt regressions between model versions, and organizations that need LLM observability without sending call data to a third-party analytics vendor.
Compared to Datadog
Unlike Datadog or general-purpose APM tools, Helicone understands the structure of LLM interactions — it tracks prompt versions, segments costs by user, and replays historical requests for debugging, rather than treating LLM calls as generic HTTP events.
