LLM gateways have evolved from experimental tools to critical production infrastructure. They handle multi-provider access, automatic failover, cost controls, and observability; requirements most teams can't build in-house.

The challenge: most gateways require extensive configuration before production deployment. Zero-config gateways reduce time-to-production from days to minutes.

This comparison examines the 5 leading LLM gateways for 2026.

Why Gateways Matter

Gateways solve production infrastructure problems:

Provider reliability: OpenAI, Anthropic, AWS Bedrock all experience outages. Without automatic failover, applications suffer complete downtime.

Cost control: LLM API costs spike unpredictably. Token-level tracking and per-team limits prevent budget overruns.

Multi-provider complexity: Organizations average 2.8 providers to avoid lock-in. Managing multiple APIs creates integration overhead and authentication complexity.

Observability gaps: Direct provider integration provides minimal visibility into token usage, latency patterns, and cost attribution. Enterprise teams need built-in dashboards, not integration projects.

Gateways centralize: unified API, automatic failover, budget controls, semantic caching, distributed tracing.

1. Bifrost by Maxim AI

Architecture: High-performance gateway written in Go. Zero-config deployment. Plugin-first architecture

Performance: 11µs overhead at 5,000 RPS. 50x faster than Python-based alternatives.

# Install and run locally
npx -y @maximhq/bifrost

# Or use Docker
docker run -p 8080:8080 maximhq/bifrost

# Open the built-in web interface
open http://localhost:8080

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello, Bifrost!"}]
  }'

Core capabilities:

• Unified API for 1,000+ models (OpenAI, Anthropic, Mistral, Bedrock, Groq, Gemini)
• Automatic failover with intelligent retry logic
• Semantic caching (40-60% cost reduction)
• MCP support for tool execution
• Virtual keys with granular budgets (per-team, per-customer, per-project)
• Built-in dashboard with real-time logs
• Native Prometheus metrics and OpenTelemetry tracing

Setup:

npx -y @maximhq/bifrost

Best for: Teams requiring enterprise governance, comprehensive observability, and production-grade performance without configuration overhead. Only gateway combining sub-100µs latency with zero-config deployment.

Docs:

Setting Up - Bifrost

Get Bifrost running as an HTTP API gateway in 30 seconds with zero configuration. Perfect for any programming language.

docs.getbifrost.ai

2. Cloudflare AI Gateway

Architecture: Edge-optimized gateway on Cloudflare's global network.

Core capabilities:

• 350+ models across 6 providers
• Edge caching reduces costs and latency
• Rate limiting and automatic retries
• Real-time analytics dashboard
• 100M logs total, available in 15 seconds
• Dynamic routing between models

Best for: Organizations using Cloudflare infrastructure.

Docs: https://developers.cloudflare.com/ai-gateway/

3. LiteLLM

Architecture: Open-source gateway supporting 100+ providers.

Core capabilities:

• Extensive provider coverage (Bedrock, Huggingface, VertexAI, Azure, Groq)
• Retry and fallback logic
• Budget limits and rate controls
• Observability integrations (Langfuse, MLflow, Helicone)
• 8ms P95 latency at 1K RPS

Best for: Teams prioritizing open-source flexibility.

Docs: https://www.litellm.ai/

4. Vercel AI Gateway

Architecture: Managed gateway integrated with Vercel's platform.

Core capabilities:

• Hundreds of models (OpenAI, Anthropic, Google)
• Sub-20ms routing latency
• Automatic failover during provider downtime
• OpenAI API compatibility
• Deep Next.js and React integration

Best for: Teams hosting on Vercel.

Docs: https://vercel.com/docs/ai-gateway

5. Kong AI Gateway

Architecture: Extends Kong's API gateway with LLM routing.

Core capabilities:

• Multi-provider routing via plugins
• Request/response transformation
• Enterprise security (mTLS, key rotation)
• MCP support
• Extensive plugin marketplace

Best for: Organizations using Kong for API management.

Docs: https://developer.konghq.com/ai-gateway/

Comparison

Selection Criteria

Reliability: Automatic fallbacks, circuit breaking, multi-region redundancy for mission-critical applications.

Observability: Distributed tracing, metrics export, request inspection. Native Prometheus integration simplifies monitoring.

Cost/Latency: Semantic caching reduces costs 40-60%. Per-team budgets prevent overruns.

Security: SSO, Vault support, scoped keys, RBAC for enterprise deployments.

Developer experience: OpenAI-compatible APIs reduce migration friction.

Integration: Alignment with evaluation workflows, agent simulation, production monitoring.

Choose Based on Your Stack

Bifrost: Enterprise governance, zero-config deployment, comprehensive observability with 11µs latency. The only gateway combining production-grade performance with instant setup. Strong choice for teams needing granular budget controls and built-in dashboards.

Cloudflare: Edge optimization for global applications. Best if already using Cloudflare infrastructure.

LiteLLM: Open-source flexibility with 100+ provider coverage. Requires infrastructure management expertise.

Vercel: Framework integration for Next.js/React. Natural choice for Vercel-hosted applications.

Kong: Enterprise API management consolidation. Extends existing Kong investment to AI workloads.

Most production teams prioritize performance, observability, and deployment speed. Evaluate based on these requirements first, then existing stack compatibility.

Resources:

Bifrost: https://docs.getbifrost.ai https://github.com/maximhq/bifrost

Cloudflare: https://developers.cloudflare.com/ai-gateway/

LiteLLM: https://www.litellm.ai/

Vercel: https://vercel.com/docs/ai-gateway

Kong: https://developer.konghq.com/ai-gateway/