Pranay Batta

Posted on Mar 5

Best Open Source AI Gateway in 2026

#programming #ai #tutorial #opensource

TL;DR: Five open-source AI gateways compared on performance, features, and deployment. Bifrost (which I help maintain) leads on raw throughput; 11µs overhead at 5,000 RPS, written in Go. LiteLLM has the largest ecosystem but Python limits its ceiling. Kong and APISIX bring enterprise API management. Envoy AI Gateway is the newest entrant from the service mesh world. Here's what each actually delivers.

If latency and self-hosting matter to your stack; Bifrost on GitHub. Apache 2.0 licensed, running in 30 seconds: npx -y @maximhq/bifrost. Docs | Website

Why Open Source Matters for AI Gateways

Look, here's the thing — managed AI gateways are convenient. Portkey, Helicone, Cloudflare AI Gateway — they all work.

But the moment you're dealing with DPDPA compliance, sensitive prompt data, or just plain cost control at scale, "someone else's infrastructure" becomes a problem.

Open source gives you three things managed gateways can't:

Data sovereignty. Your prompts, your responses, your infra. Nothing leaves your VPC unless you explicitly send it.

No per-request pricing. Managed gateways charge per million requests, per seat, or per feature tier. Open source = your compute costs only. At lakhs of requests per day, that difference adds up to serious money.

Customisation. Need a custom caching strategy? A specific logging format for your compliance team? Fork it, extend it, PR it. Try doing that with a SaaS gateway.

Quick Comparison

Feature	Bifrost	LiteLLM	Apache APISIX	Kong AI Gateway	Envoy AI Gateway
Language	Go	Python	Lua/Nginx	Go/Lua	Go/C++
Overhead	11µs	~8ms	~1-2ms	~2-5ms	~1-3ms
AI Providers	20+	100+	Via plugins	10+	5+
Semantic Cache	Yes (Weaviate)	No	No	No	No
MCP Support	Yes	No	No	No	No
Virtual Keys	Yes	Yes	No	Yes	No
Budget Control	Yes (4-tier)	Basic	No	Enterprise	No
License	Apache 2.0	MIT	Apache 2.0	Apache 2.0	Apache 2.0
Web UI	Yes	Yes	Yes	Yes	No

1. Bifrost — The Performance-First AI Gateway

GitHub: git.new/bifrost | Docs: getmax.im/bifrostdocs

Architecture: Written in Go. Pre-spawned worker pools with buffered channels for async operations. Each provider gets an isolated worker pool — one provider going down doesn't cascade into others. No garbage-collection pauses in the hot path. Object pools with 85-95% hit ratios in steady state.

Benchmark numbers: 11µs overhead on a t3.xlarge (4 vCPUs, 16GB RAM) at 5,000 RPS. On a t3.medium, 59µs. Both with 100% success rate.

Semantic caching: Dual-layer — exact hash matching for identical requests, plus vector similarity search via Weaviate for semantically similar queries. Configurable similarity threshold (default 0.8). Sub-millisecond cache retrieval versus multi-second API calls. Streaming response caching included.

MCP support: Full Model Context Protocol integration — STDIO, HTTP, SSE, and Streamable HTTP connections. Code Mode reduces token usage by 50%+ by stripping tool definitions to essential schemas. Centralised tool registry with per-team access controls.

Governance: Four-tier budget hierarchy — Customer → Team → Virtual Key → Provider Config. Per-key rate limits, model restrictions, and spend caps. Set ₹50,000/month on a virtual key for staging and Bifrost enforces it.

# Running in 30 seconds
npx -y @maximhq/bifrost
# Open http://localhost:8080

Trade-off: Fewer provider integrations than LiteLLM (20+ vs 100+). Smaller community. You're running your own infra.

2. LiteLLM — The Ecosystem Giant

GitHub: github.com/BerriAI/litellm

LiteLLM is the most widely adopted open-source LLM proxy. MIT-licensed. Massive community.

Strengths: 100+ provider integrations. Unified OpenAI-format output across all providers. Virtual keys with team management. Latency-based, cost-based, and usage-based routing. The ecosystem is genuinely impressive — if a provider exists, LiteLLM probably supports it.

The Python ceiling: 8ms P95 at 1,000 RPS. Python's GIL limits single-process throughput. At scale, you're running multiple proxy instances behind a load balancer. That's more infra to manage, more latency hops.

No semantic caching. Exact-match only. No MCP support either. For agentic workflows, that's an increasingly painful gap.

When to pick LiteLLM: If you need maximum provider coverage and your throughput stays under ~250-300 RPS per instance. The community and documentation are excellent. Credits to the BerriAI team for building something this widely used.

3. Apache APISIX — The API Management Approach

GitHub: github.com/apache/apisix

APISIX is a cloud-native API gateway that's added AI plugins. It's not AI-first — it's an API gateway that happens to handle AI traffic.

Strengths: Battle-tested at massive scale for traditional API management. Dynamic plugin loading. Multi-language plugin support (Lua, Go, Python, Java). If you already run APISIX for your API layer, adding AI routing is a natural extension.

AI-specific features: AI proxy plugin for OpenAI, Anthropic, and a handful of other providers. Request/response transformation. Rate limiting per route. But no semantic caching, no virtual keys with budget enforcement, no MCP.

Trade-off: Great general-purpose gateway. But if your primary use case is AI-specific — budget controls, semantic caching, MCP tool management — you'll be writing custom plugins. That's a lot of Lua.

4. Kong AI Gateway — Enterprise API Management Meets AI

GitHub: github.com/Kong/kong

Kong is the most widely deployed API gateway in the world. Their AI Gateway plugin adds LLM-specific features to the existing Kong infrastructure.

Strengths: Enterprise-grade. If your org already uses Kong for API management, the AI Gateway plugin slots right in. Rate limiting, authentication, logging — all built on Kong's proven infrastructure. 10+ AI provider integrations.

AI features: Multi-LLM support, prompt engineering plugins, AI request/response transformation. The enterprise version adds advanced governance, analytics, and compliance features.

Trade-off: The open-source version is limited. Advanced AI features (semantic caching, detailed analytics, compliance) require Kong Enterprise. That's not free. The architecture adds latency — Kong is Nginx + Lua, plus the AI plugin processing. Typically 2-5ms overhead.

5. Envoy AI Gateway — The Service Mesh Approach

GitHub: github.com/envoyproxy/ai-gateway

Envoy AI Gateway is the newest entrant. Built on Envoy Proxy — the foundation of Istio and most service mesh deployments.

Strengths: If you're running Kubernetes with Istio, Envoy is already in your stack. The AI Gateway extension adds LLM routing, rate limiting, and cost tracking. Cloud-native by default. 1-3ms overhead, which is solid for a proxy-based architecture.

AI features: Multi-provider routing, token-based rate limiting, cost estimation. Integration with Kubernetes Gateway API.

Trade-off: Very early stage. Limited provider support (5+). No semantic caching. No MCP. No virtual keys or budget hierarchy. The Envoy configuration model (xDS) has a steep learning curve if you're not already in the Envoy ecosystem.

The Decision Framework

You need raw performance + AI-native features: Bifrost. 11µs overhead. Semantic caching. MCP. Budget controls. Apache 2.0.

You need maximum provider coverage: LiteLLM. 100+ providers. Accept the Python latency trade-off.

You already run APISIX/Kong for API management: Extend your existing gateway. Don't introduce another proxy layer.

You're deep in Kubernetes/Istio: Envoy AI Gateway. Native service mesh integration.

The math is straightforward: if AI traffic is your primary use case, pick an AI-native gateway. If AI is 10% of your API traffic, extend your existing API gateway.

Getting Started with Bifrost

# Option 1: NPX
npx -y @maximhq/bifrost

# Option 2: Docker
docker pull maximhq/bifrost
docker run -p 8080:8080 maximhq/bifrost

# Test it
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"}]}'