Helicone

Helicone is an open-source observability platform and AI gateway designed for large language model (LLM) applications. It provides developers and AI teams with the tooling needed to monitor the behaviour, cost, latency, and output quality of LLM calls in production, enabling systematic debugging and optimisation of AI-powered products. Helicone was founded in 2023 as a Y Combinator W23 company and has processed over two billion LLM interactions as of 2025.

Purpose and Problem Addressed

Building production AI applications on top of LLM APIs such as OpenAI, Anthropic, Google Gemini, and open-source models introduces operational challenges that do not arise with conventional software. LLM calls are expensive relative to traditional API calls, outputs are non-deterministic, prompt changes can produce unexpected regressions, and debugging a failing AI feature requires understanding a chain of model interactions rather than a simple function call stack.

Helicone addresses these challenges by sitting between the application and the LLM provider as an observability proxy. When a developer routes their LLM calls through Helicone's gateway, every request and response is automatically logged, annotated, and made available for analysis in the Helicone dashboard, without requiring the developer to instrument their own logging code.

Architecture

Helicone's production infrastructure is built on Cloudflare Workers for the proxy layer, ClickHouse for analytics storage, and Kafka for event streaming. This architecture allows it to add an average of 50 to 80 milliseconds of latency per request while handling high-volume production workloads. The platform is also available as a self-hosted deployment for organisations with data residency requirements or security policies that preclude routing traffic through a third-party proxy.

Integration is designed to be minimal. In most cases, the only code change required is updating the base URL of the LLM client library from the provider's endpoint to the Helicone proxy endpoint, and optionally adding a Helicone API key header. This one-line-of-code integration philosophy is a deliberate design goal distinguishing Helicone from more invasive observability tools.

Core Features

Request logging captures every LLM request and response including the full prompt, completion, model parameters, token counts, cost estimates, and latency. Logs are searchable and filterable by arbitrary metadata properties attached by the application.

Cost tracking aggregates spending across models and providers, surfacing cost-per-user, cost-per-feature, and cost trends over time. This is important for AI product teams managing infrastructure budgets where LLM calls may account for a large fraction of operating costs.

Prompt management provides version control and deployment pipelines for prompts, allowing teams to iterate on prompts without hardcoding them in application source code. Prompt versions can be rolled back and compared against one another.

Sessions and traces group related LLM calls into logical sessions corresponding to a user interaction or agentic workflow, providing an end-to-end view of multi-step reasoning chains. This is particularly useful for debugging AI agents and retrieval-augmented generation pipelines.

Evaluations allow teams to annotate model outputs with quality scores, either manually or through automated LLM-as-judge pipelines, enabling systematic measurement of output quality over time and across prompt versions.

Gateway features include rate limiting, caching of identical requests to reduce cost, and routing between providers for cost optimisation or fallback.

Ecosystem Integration

Helicone integrates with major LLM providers including OpenAI, Anthropic, Azure OpenAI, Google Gemini, Cohere, and open-source model servers. It supports orchestration frameworks including LangChain, LlamaIndex, and LangGraph, providing trace-level observability for agent workflows built with these tools. The platform also integrates with the Vercel AI SDK, making it accessible to web developers building AI features in Next.js applications.

See Also

References

1. Helicone. (2025). LLM observability: 5 essential pillars for production-ready AI applications. Helicone Blog. https://www.helicone.ai/blog/llm-observability
2. Helicone. (2025). The complete guide to LLM observability platforms. Helicone Blog. https://www.helicone.ai/blog/the-complete-guide-to-LLM-observability-platforms
3. Y Combinator. (2023). Helicone (W23) company profile. YC Company Directory.
4. Helicone. (2025). Quickstart documentation. Helicone Docs. https://docs.helicone.ai/
5. Vercel. (2025). Observability integrations: Helicone. Vercel AI SDK Documentation. https://ai-sdk.dev/providers/observability/helicone