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.

AG
Agent Toolkit · Community
Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

LiteLLM (Most Popular)

pip install litellm
litellm --model gpt-4o --port 4000

Bifrost (Fastest)

npx -y @maximhq/bifrost

Portkey (Enterprise)

pip install portkey-ai

What are LLM Gateways?

LLM gateways are proxy servers that sit between your application and LLM providers. They provide a unified API, automatic failover, load balancing, caching, cost tracking, and access to 100+ models through one endpoint. Essential for production AI applications that need reliability and cost control.

Answer-Ready: LLM gateways proxy AI requests through a unified API. Top tools: LiteLLM (open-source, 200+ models), Bifrost (fastest, sub-100us), Portkey (enterprise), OpenRouter (pay-per-use marketplace). Provide failover, caching, cost tracking, and multi-provider routing.

Best for: Teams running AI in production needing reliability and cost control. Works with: Any LLM provider and AI coding tool.

Gateway Comparison

Feature Matrix

Feature LiteLLM Bifrost Portkey OpenRouter
Type Open-source Open-source Enterprise Marketplace
Models 200+ 1000+ 250+ 300+
Overhead ~1ms <100us ~2ms ~50ms
Failover Yes Yes Yes Yes
Caching Yes Semantic Yes No
Cost tracking Yes Yes Yes Built-in
Load balancing Yes Yes Yes Automatic
Self-hosted Yes Yes Yes No
Free tier Unlimited (OSS) Unlimited (OSS) 10K req/mo Pay-per-use

When to Use Each

Gateway Best For
LiteLLM Teams wanting open-source flexibility with most provider support
Bifrost High-throughput apps needing minimal latency overhead
Portkey Enterprise teams needing compliance, guardrails, and analytics
OpenRouter Indie developers wanting simple pay-per-use access to all models

Setup Examples

LiteLLM — Universal Proxy

from litellm import completion

# Same API for any provider
response = completion(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello"}],
)
# Switch to: openai/gpt-4o, gemini/gemini-2.5-pro, etc.

Bifrost — Claude Code Integration

npx -y @maximhq/bifrost
claude mcp add --transport http bifrost http://localhost:8080/mcp

Portkey — With Guardrails

from portkey_ai import Portkey

client = Portkey(api_key="...", config={
    "retry": {"attempts": 3},
    "cache": {"mode": "semantic"},
    "guardrails": ["pii-filter", "toxicity-check"],
})

OpenRouter — Simple Access

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-...",
)
response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello"}],
)

Cost Optimization Strategies

Strategy How
Model routing Simple tasks → cheap model, complex → premium
Caching Cache identical/similar requests
Fallback chain Primary fails → cheaper backup
Budget limits Hard caps per project/user
Token tracking Monitor and optimize token usage

FAQ

Q: Do I need a gateway? A: For production, yes. For prototyping, direct API calls are fine. Gateways add reliability, cost control, and flexibility.

Q: Can I use a gateway with Claude Code? A: Yes, LiteLLM and Bifrost both support Claude Code integration via MCP or API proxy.

Q: Which is cheapest? A: LiteLLM and Bifrost are free (open-source, self-hosted). OpenRouter charges a small markup on model pricing.

🙏

Source & Thanks

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