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[!IMPORTANT] :rocket: Gateway 2.0 (Pre-Release) Portkey's core enterprise gateway is merging into open-source with our 2.0 release. You can try the pre-release branch here. Read more about what's next for Portkey in our Series A announcement.

🆕 Portkey Models - Open-source LLM pricing for 2,300+ models across 40+ providers. Explore →

AI Gateway

Route to 250+ LLMs with 1 fast & friendly API

Docs | Enterprise | Hosted Gateway | Changelog | API Reference

The AI Gateway is designed for fast, reliable & secure routing to 1600+ language, vision, audio, and image models. It is a lightweight, open-source, and enterprise-ready solution that allows you to integrate with any language model in under 2 minutes.

• Blazing fast (<1ms latency) with a tiny footprint (122kb)
• Battle tested, with over 10B tokens processed everyday
• Enterprise-ready with enhanced security, scale, and custom deployments

What can you do with the AI Gateway?

• Integrate with any LLM in under 2 minutes - Quickstart
• Prevent downtimes through automatic retries and fallbacks
• Scale AI apps with load balancing and conditional routing
• Protect your AI deployments with guardrails
• Go beyond text with multi-modal capabilities
• Explore agentic workflow integrations
• Manage MCP servers with enterprise auth & observability using MCP Gateway

[!TIP] Starring this repo helps more developers discover the AI Gateway 🙏🏻

Quickstart (2 mins)

1. Setup your AI Gateway

# Run the gateway locally (needs Node.js and npm)
npx @portkey-ai/gateway

The Gateway is running on http://localhost:8787/v1

The Gateway Console is running on http://localhost:8787/public/

Deployment guides:   Portkey Cloud (Recommended)   Docker   Node.js   Cloudflare   Replit   Others...

2. Make your first request

# pip install -qU portkey-ai

from portkey_ai import Portkey

# OpenAI compatible client
client = Portkey(
    provider="openai", # or 'anthropic', 'bedrock', 'groq', etc
    Authorization="sk-***" # the provider API key
)

# Make a request through your AI Gateway
client.chat.completions.create(
    messages=[{"role": "user", "content": "What's the weather like?"}],
    model="gpt-4o-mini"
)