WorkflowsApr 8, 2026·3 min read

LLM Gateway Comparison — Proxy Your AI Requests

Compare top LLM gateway and proxy tools for routing AI requests. Covers LiteLLM, Bifrost, Portkey, and OpenRouter for cost optimization, failover, and multi-provider access.

TL;DR
Compare LLM gateways like LiteLLM, Portkey, and OpenRouter for unified API routing and cost control.
§01

What it is

This comparison covers the leading LLM gateway and proxy tools that sit between your application and LLM providers. Gateways like LiteLLM, Portkey, OpenRouter, and Bifrost provide a unified API, automatic failover, cost tracking, and request routing across multiple AI providers.

The comparison helps engineering teams choose the right gateway for their needs -- whether that is cost optimization, high availability, or provider flexibility.

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How it saves time or tokens

Without a gateway, switching between OpenAI, Anthropic, and Google requires rewriting API calls for each provider. An LLM gateway provides a single endpoint with automatic failover, so provider outages do not break your application. Cost tracking and rate-limit management across providers are built in.

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How to use

  1. Choose a gateway based on your priorities: self-hosted (LiteLLM), managed (Portkey, OpenRouter), or performance-first (Bifrost).
  2. Replace your direct provider API calls with the gateway's unified endpoint.
  3. Configure routing rules, fallbacks, and cost budgets in the gateway dashboard or config file.
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Example

# LiteLLM: unified API for 100+ LLM providers
import litellm

# Same function call, different providers
response = litellm.completion(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'Hello'}]
)

# Switch to Claude with one line change
response = litellm.completion(
    model='claude-sonnet-4-20250514',
    messages=[{'role': 'user', 'content': 'Hello'}]
)

# Automatic fallback chain
response = litellm.completion(
    model='gpt-4o',
    fallbacks=['claude-sonnet-4-20250514', 'gemini-pro'],
    messages=[{'role': 'user', 'content': 'Hello'}]
)
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Related on TokRepo

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Common pitfalls

  • Gateway latency adds 10-50ms per request. For latency-sensitive applications, benchmark the gateway overhead against your SLA requirements.
  • Not all gateways support streaming for every provider. Verify streaming compatibility before deploying to production.
  • Cost tracking accuracy depends on the gateway correctly mapping token counts. Cross-check gateway cost reports against provider invoices monthly.

Frequently Asked Questions

What is the difference between an LLM gateway and a direct API call?+

A direct API call goes straight to one provider (e.g., OpenAI). An LLM gateway sits in between, providing a unified API, automatic failover between providers, cost tracking, rate limiting, and request caching. It decouples your code from any single provider.

Which LLM gateway is best for self-hosting?+

LiteLLM is the most popular self-hosted option. It supports 100+ providers through a single OpenAI-compatible endpoint and can run as a Docker container or Python process in your own infrastructure.

Does OpenRouter support all major LLM providers?+

OpenRouter aggregates access to models from OpenAI, Anthropic, Google, Meta, Mistral, and many open-source model hosts. It is a managed service so you do not self-host, and it provides a unified API with per-model pricing.

Can I use multiple gateways together?+

Technically yes, but it adds complexity. A more common pattern is to pick one gateway and configure it with multiple provider backends. The gateway handles failover and routing internally.

How do LLM gateways handle rate limits?+

Most gateways track rate limits per provider and automatically route requests to available providers when one hits its limit. LiteLLM and Portkey both support rate-limit-aware routing out of the box.

Citations (3)
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