Helicone AI

New

Monitor and optimize LLM API usage and costs in production.

8.4 (64.745 score)

freemiumAPI Available

Overview

Helicone provides observability for language model applications, helping developers track API calls, costs, latency, and performance across different LLM providers. It works with OpenAI, Anthropic, Azure, and other providers, offering logging, analytics, and caching features. Teams use it to debug issues, optimize spending, and understand user behavior without changing application code.

Pros

Works with multiple LLM providers without vendor lock-in
Tracks costs and latency automatically across all API calls
Request caching reduces API calls and lowers expenses
Open-source core allows self-hosting and customization
Logs detailed request and response data for debugging

✕ Cons

Free tier has limited request history and analytics features
Requires code integration or proxy setup to use effectively
Learning curve for teams unfamiliar with observability platforms

Key Features

Multi-provider LLM logging

Cost and latency analytics

Request caching layer

User feedback tracking

API gateway and proxy

Custom properties and tagging

Use Cases

Teams building ChatGPT-powered apps who need cost visibilityEngineers debugging LLM response quality and latency issuesProduct managers analyzing user interactions with AI featuresCompanies optimizing LLM spending across multiple API providers

Best For

ML EngineersDevOps TeamsAI Product ManagersCost-Conscious AI Startups

Frequently Asked Questions

What does Helicone cost?▾

Helicone offers a free tier for development and testing, with paid plans based on request volume and features. Self-hosting the open-source version is also available at no cost.

How hard is it to set up Helicone?▾

Setup is straightforward—integrate via API key or proxy layer in minutes. The learning curve is minimal for basic monitoring, though advanced caching and analytics features may require additional configuration.

Does Helicone integrate with other tools?▾

Helicone works as an API gateway supporting OpenAI, Anthropic, Azure, and other LLM providers. It provides webhooks and logging APIs for integration with external monitoring and analytics platforms.

What are the main limitations of Helicone?▾

Helicone focuses on LLM monitoring and cost optimization, not general application observability. Real-time latency data may have slight delays, and extensive custom analytics require API-level queries.

Who should use Helicone?▾

Teams running multiple LLM-powered applications in production who need visibility into API costs, latency, and usage patterns across different providers without vendor lock-in.