Helicone AI Gateway provides a unified, high-performance interface for interacting with over 100 LLM providers, acting as the "NGINX of LLMs." It targets developers and organizations seeking to simplify AI integrations, manage costs, and improve application latency by abstracting away provider-specific APIs and offering intelligent routing, rate limiting, and caching.

How It Works

Built in Rust, the gateway functions as a reverse proxy, accepting requests via a familiar OpenAI-compatible API. It then intelligently routes these requests to various LLM providers based on configurable strategies like latency, cost, or weighted distribution. Key features include response caching (Redis/S3), per-user/team rate limiting (requests, tokens, dollars), and observability through Helicone's platform or OpenTelemetry.

Quick Start & Requirements

• Install: npx @helicone/ai-gateway@latest
• Prerequisites: Environment variables for provider API keys (e.g., OPENAI_API_KEY).
• Setup: Seconds to configure .env and run.
• Links: 🚀 Quick Start • 📖 Docs • 💬 Discord • 🌐 Website

Highlighted Details

• Claims significantly lower P95 latency (<10ms vs. ~60-100ms), memory usage (~64MB vs. ~512MB), and cold start times (~100ms vs. ~2s) compared to typical setups.
• Supports 20+ LLM providers with a unified OpenAI-compatible interface.
• Offers smart load balancing strategies (latency-based P2C, weighted distribution, cost optimization).
• Includes robust rate limiting and response caching capabilities.

Maintenance & Community

• Actively developed by the Helicone team.
• Community support via 💬 Discord Server and GitHub Discussions.
• Updates and announcements on Twitter.

Licensing & Compatibility

• Licensed under the Apache License 2.0.
• Permissive license suitable for commercial and closed-source applications.

Limitations & Caveats

Preliminary performance metrics are provided; detailed benchmarking methodology is available in benchmarks/README.md. The project is positioned as "The NGINX of LLMs," implying a focus on high-throughput, low-latency proxying rather than LLM-specific fine-tuning or agentic capabilities.