TL;DR: Cloudflare AI Gateway is free and convenient, but it adds 10-50ms of proxy latency, locks you into SaaS-only deployment, and has no semantic caching or MCP support. If you need sub-millisecond overhead, self-hosting, or advanced routing, here are five alternatives worth evaluating. Bifrost (which I help maintain) clocks 11us overhead at 5,000 RPS; and it's open source.
Before you scroll any further; if latency and self-hosting matter to you, check out Bifrost on GitHub. It's written in Go, Apache 2.0 licensed, and you can have it running in 30 seconds with npx -y @maximhq/bifrost. Docs here, website here.
Why Look Beyond Cloudflare AI Gateway?
Look, here's the thing; Cloudflare AI Gateway is a solid product.
Free tier. Global edge network. Dashboard analytics out of the box.
But once you're running production workloads at scale; say, lakhs of requests per day; the cracks start showing.
Latency overhead. Every request hops through Cloudflare's edge proxy. That's 10-50ms of added latency, depending on your region. For a chat completion that takes 2-3 seconds, maybe that's fine. For agentic workflows making dozens of chained calls? That overhead compounds fast, yaar.
SaaS-only. No self-hosted option. Your prompts, your responses; all transiting through Cloudflare's infrastructure. For teams dealing with DPDPA compliance or sensitive data, that's a non-starter.
No semantic caching. Cloudflare offers exact-match caching. Same request = cached response. But users rarely phrase the same question identically. Semantic caching matches by meaning, not by string equality.
No MCP support. Model Context Protocol is how AI agents discover and execute tools. Cloudflare hasn't shipped this yet.
Logging limits. Free tier caps at 100,000 logs/month. Workers Paid gives you 1,000,000. After that, you're paying per million records. At scale, that adds up; easily a few thousand rupees per month just for logs.
1. Bifrost (Open Source, Go) — The Performance Pick
GitHub: git.new/bifrost | Docs: getmax.im/bifrostdocs | Website: getmax.im/bifrost-home
Full disclosure: I'm a maintainer. But the numbers speak for themselves.
Overhead: 11us on a t3.xlarge (4 vCPUs, 16GB RAM) at 5,000 RPS. That's microseconds, not milliseconds. On a t3.medium, it's 59us. Both with 100% success rate.
Why it's fast: Written in Go. Pre-spawned worker pools. Buffered channels for async operations. No garbage-collection pauses eating into your P99. The architecture is basically: incoming request hits a channel, gets picked up by an idle worker, routed to the provider, response streams back. All in-memory, no disk I/O in the hot path.
Semantic caching: Uses vector similarity search (Redis + RediSearch or Weaviate) to serve cached responses for semantically similar queries. Not just exact-match; actually understands query intent. Sub-millisecond cache retrieval vs multi-second API calls.
MCP support: Full Model Context Protocol integration. AI models can discover and execute external tools at runtime; filesystem access, web search, API calls. Supports STDIO, HTTP, SSE, and streaming connection types. This is the piece that turns a chat model into an agent.
Provider coverage: 20+ providers — OpenAI, Anthropic, Bedrock, Vertex AI, Gemini, Groq, Mistral, Cohere, Ollama, xAI, Azure, Cerebras, Hugging Face, OpenRouter, Perplexity, and more.
Governance: Virtual keys with per-key budgets, rate limits, and model routing rules. Set a cap of say ₹50,000/month on a virtual key for your staging environment — Bifrost enforces it automatically.
Self-hosted. Apache 2.0. Run it on your own infra. No data leaves your VPC. DPDPA-friendly by default.
# 30-second setup
npx -y @maximhq/bifrost
# Open http://localhost:8080
Trade-off: You're running your own infrastructure. No managed edge network. But honestly, for most Indian startups and enterprises, a single EC2 instance handles 5,000 RPS — that's more than enough.
2. LiteLLM (Open Source, Python) — The Ecosystem Giant
GitHub: github.com/BerriAI/litellm
LiteLLM is the most popular open-source LLM proxy. MIT-licensed. Massive community. 100+ provider integrations.
Strengths:
- Unified OpenAI-format output across all providers
- Virtual keys with team management and budget controls
- Extensive routing strategies: latency-based, cost-based, usage-based
- Integrations with Langfuse, Helicone, MLflow for observability
The honest comparison:
- Written in Python. 8ms P95 latency at 1,000 RPS. That's roughly 700x slower overhead than Bifrost at the gateway layer.
- At 5,000 RPS, Python's GIL and async overhead become real bottlenecks. You'll need multiple proxy instances behind a load balancer.
- No native semantic caching. No MCP support.
Best for: Teams already deep in the Python ecosystem who need broad provider coverage and don't need sub-millisecond gateway overhead.
3. Helicone (Open Source, Rust) — The Observability-First Gateway
Website: helicone.ai
Helicone started as an observability layer and evolved into a full gateway. Written in Rust — so performance is solid.
Strengths:
- Rust-based: ~8ms P50 latency. Faster than Python alternatives, though still orders of magnitude above Go's microsecond overhead
- Best-in-class LLM analytics dashboard — cost tracking, latency distribution, error monitoring
- Latency-based load balancing with real-time moving averages
- Single binary deployment: Docker, Kubernetes, bare metal
What's missing:
- No MCP support
- No semantic caching (exact-match only)
- Observability focus means routing and governance features are lighter than dedicated gateways
- No virtual key governance system with budgets
Best for: Teams that prioritise observability and analytics over advanced routing. If your primary need is "understand my LLM spend," Helicone is excellent.
4. Kong AI Gateway (Enterprise) — The API Management Play
Website: konghq.com/products/kong-ai-gateway
Kong extended their existing API gateway into AI territory. If you're already running Kong for your REST APIs, this makes a lot of sense.
Strengths:
- 100+ enterprise plugins: auth, rate limiting, token quotas, observability
- Semantic caching and prompt guards
- PII sanitization built in (important for DPDPA compliance, no?)
- MCP gateway support (shipped in 2025, OAuth 2.1 flow)
- Unified management — your REST APIs and LLM traffic in one control plane
What's missing:
- Enterprise pricing. The AI-specific plugins (token rate limiting, semantic caching) are gated behind paid tiers. Not cheap for startups — we're talking lakhs per year for enterprise licenses.
- Heavier footprint. Kong is a full API management platform. If you only need an LLM gateway, that's a lot of overhead.
- No open-source AI-specific features. The OSS version gives you basic proxying, not the AI gateway capabilities.
Best for: Large enterprises already using Kong for API management who want a single platform for everything.
5. Portkey (Open Source + SaaS) — The Developer Experience Gateway
GitHub: github.com/Portkey-AI/gateway | Website: portkey.ai
Portkey positions itself as the "control panel for production AI." Open-source gateway with a SaaS dashboard.
Strengths:
- 250+ model integrations
- Good developer experience: clean SDK, visual routing builder
- Guardrails and safety filters built in
- Semantic caching available
- SaaS dashboard starts at $49/month
What's missing:
- Gateway written in Node.js — performance sits between Python and Go, not in the microsecond range
- Limited MCP support as of 2026
- Self-hosted option exists but feature parity with SaaS isn't complete
- Governance features require paid tiers
Best for: Teams that want a managed experience with good DX and don't need maximum performance.
Quick Comparison Table
| Feature | Cloudflare | Bifrost | LiteLLM | Helicone | Kong | Portkey |
|---|---|---|---|---|---|---|
| Gateway Overhead | 10-50ms | 11us | ~8ms | ~8ms | Varies | Varies |
| Self-Hosted | No | Yes | Yes | Yes | Yes | Partial |
| Open Source | No | Apache 2.0 | MIT | Yes | Partial | Yes |
| Semantic Caching | No | Yes | No | No | Enterprise | Yes |
| MCP Support | No | Yes | No | No | Enterprise | Limited |
| Virtual Key Governance | No | Yes | Yes | No | Enterprise | Paid |
| Providers | Multi | 20+ | 100+ | 100+ | Multi | 250+ |
| Language | - | Go | Python | Rust | Lua/C | Node.js |
So Which One Should You Pick?
Need raw performance + self-hosting? Bifrost. 11us overhead. Apache 2.0. Run it in your VPC.
Need maximum provider coverage in Python? LiteLLM. Just budget for the latency overhead.
Need observability first, gateway second? Helicone. The analytics are genuinely excellent.
Already running Kong for APIs? Add the AI Gateway plugin. One platform to rule them all.
Want managed experience with minimal setup? Portkey. Good DX, reasonable pricing.
Want free and don't care about latency? Cloudflare AI Gateway is still there. It works.
Basically, there's no single "best" gateway; it depends on what you're optimising for. But if you're reading this and thinking "I need this to be fast, self-hosted, and not cost me a grand," give Bifrost a spin.
Star us on GitHub: git.new/bifrost
Read the docs: getmax.im/bifrostdocs
Website: getmax.im/bifrost-home
Part of the Maxim AI platform for GenAI evaluation and observability.
I maintain Bifrost, so take my bias accordingly. But every number cited here is from published benchmarks you can verify and reproduce yourself. The benchmarking tool is open source too.
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