Find AI Tools
Helicone AI screenshot

What is Helicone AI?

Helicone AI is an open-source LLMOps platform designed to provide thorough observability and monitoring for large language model applications. It enables developers to track, debug, and optimise AI app performance with features like request logging, cost monitoring, and latency analysis. Built for teams deploying production AI applications, Helicone helps developers understand how their LLM-powered systems behave in the real world, identify bottlenecks, and ensure reliability at scale. The platform is particularly valuable for startups and enterprises building on top of APIs like OpenAI, Anthropic, and other LLM providers, offering routing capabilities to intelligently distribute requests and maintain application stability.

Key Features

Request Logging & Tracking

Detailed logging of all LLM API calls with full request/response visibility

Cost Monitoring

Real-time tracking of LLM API usage and associated costs across different models

Latency Analysis

Performance monitoring to identify slow requests and optimise response times

Model Routing

Intelligent request routing across multiple LLM providers for reliability and cost optimization

Error Tracking

thorough error logging and debugging tools to identify and fix issues

Custom Metadata

Ability to attach custom tags and metadata to requests for advanced filtering and analysis

Pros & Cons

Advantages

  • Open-source and transparent, allowing self-hosting and community contributions
  • Free tier makes it accessible to developers and small teams
  • Reduces debugging time with detailed visibility into LLM application behaviour
  • Helps control costs by tracking usage across multiple models and providers
  • Supports multiple LLM providers, not locked into single ecosystem

Limitations

  • Requires integration into existing codebase for full benefit
  • Self-hosting open-source version requires DevOps/infrastructure knowledge
  • Learning curve for teams new to LLMOps concepts and observability

Use Cases

Monitoring production AI applications to ensure reliability and performance

Cost optimization for teams using multiple LLM API providers

Debugging LLM application issues and understanding user interactions

Performance optimization by identifying bottlenecks in AI workflows

Multi-model testing and comparison to select best providers for specific use cases