Helicone
Helicone is an open-source observability platform for LLM applications that logs, monitors, and analyzes API requests to providers like OpenAI and Anthropic, giving teams visibility into cost, latency, usage patterns, and errors with…
Definition
Helicone is an open-source observability platform for LLM applications that logs, monitors, and analyzes API requests to providers like OpenAI and Anthropic, giving teams visibility into cost, latency, usage patterns, and errors with minimal integration effort. It typically works as a lightweight proxy placed between an application and its LLM provider.
Overview
Helicone was built to solve a common problem for teams shipping LLM-powered products: once an application starts calling an LLM API in production, it becomes hard to answer basic operational questions — how much is this costing, which prompts are slow, which requests are failing, which users are driving the most usage — without building custom logging infrastructure. Helicone provides this visibility with a minimal integration change, typically just swapping the API base URL to route requests through Helicone's proxy, which then forwards them to the actual provider while logging the request and response. Once integrated, Helicone's dashboard surfaces metrics like cost per request, cost per user, latency percentiles, token usage, and error rates, and lets teams drill into individual request logs to see the exact prompt and completion. It supports caching to reduce duplicate LLM calls, rate limiting, and request retries, layering some of the same reliability features found in full AI gateways. Helicone also offers prompt experimentation tools, letting teams test and compare prompt variations against real traffic patterns. Being open source, Helicone can be self-hosted for teams with strict data residency or privacy requirements, or used as a managed cloud service for faster setup. It integrates with major providers (OpenAI, Anthropic, Azure OpenAI, and others) and popular frameworks like LangChain. Helicone competes in the LLM observability and gateway space with tools like Portkey, Braintrust, and LangSmith, generally emphasizing simplicity of integration and strong cost/usage analytics as its core differentiators, with gateway-style routing and evaluation as complementary features.
Key Features
- Proxy-based integration requiring minimal code changes (often just a base URL swap)
- Detailed cost, latency, and token usage analytics per request, user, or prompt
- Full request/response logging for debugging and auditing
- Built-in caching to reduce redundant and costly LLM calls
- Rate limiting and retry support for production reliability
- Prompt experimentation and comparison tools
- Open-source with self-hosting option for data-sensitive deployments
- Integrations with OpenAI, Anthropic, Azure OpenAI, and frameworks like LangChain