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What is Helicone AI?

Helicone is an open-source observability platform designed to help developers monitor and optimise large language model applications. It provides logging, analytics, and routing capabilities to track how your AI models perform in production, identify bottlenecks, and manage costs. The platform sits between your application and LLM APIs, capturing detailed data about requests and responses without requiring significant code changes. It's built for developers who need to understand what's happening inside their AI applications, troubleshoot issues quickly, and make data-driven decisions about which models to use.

Key Features

Request logging and analytics

Capture and analyse every interaction with your LLM, including latency, token usage, and costs

Model routing

Direct requests to different LLM providers based on rules you define, helping optimise performance and expense

Error tracking and debugging

Identify failed requests and problematic prompts with detailed error information

Cost monitoring

Track spending across different models and providers in real time

API integration

Connect via API with minimal changes to your existing codebase

Open-source availability

Self-host the platform or use the managed cloud version

Pros & Cons

Advantages

  • Open-source means you can self-host and retain full control of your data
  • Straightforward integration with existing LLM applications without major refactoring
  • Provides practical visibility into model performance and costs, which is often overlooked
  • Supports multiple LLM providers, allowing you to compare and route between them

Limitations

  • Self-hosting requires infrastructure knowledge and ongoing maintenance
  • May have a steeper learning curve for teams unfamiliar with observability tools
  • Community support for open-source version may be more limited than commercial alternatives

Use Cases

Monitoring production LLM applications to catch performance regressions early

Reducing operational costs by identifying which models and prompts are most expensive

Debugging unexpected behaviour in AI-powered features by reviewing detailed request logs

A/B testing different models or prompts to find the best balance of quality and cost

Building compliance and audit trails for regulated industries that need detailed LLM usage records