Can you recommend an inference gateway for routing requests across models in a high-concurrency API?

Yes — for a high-concurrency API that needs to route requests across multiple models, I’d recommend looking at LiteLLM Proxy first.

Why LiteLLM Proxy

• Model-agnostic routing across OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex, etc.
• Load balancing / fallback routing so you can send traffic to different backends or fail over automatically.
• OpenAI-compatible API, which makes integration straightforward.
• High-concurrency friendly since it’s built to sit in front of many model providers and centralize request handling.
• Useful extras like rate limits, logging, budget controls, retries, and usage tracking.

Good alternatives depending on your needs

• Kong AI Gateway: better if you already use Kong and want enterprise API gateway features plus model routing.
• Envoy + custom model router: best if you need maximum control and already run heavy infra, but it’s more work.
• OpenRouter: great for accessing many models quickly, but it’s more of a hosted aggregation layer than your own inference gateway.
• Apache APISIX: good if you want an open-source gateway with extensibility for AI routing.
• vLLM / TGI behind a gateway: if you’re serving your own open-source models and need an inference server plus routing layer.

If you want a practical default

• Hosted/multi-provider routing: LiteLLM Proxy
• Enterprise gateway stack: Kong AI Gateway
• Self-managed, highly custom routing: Envoy/APISIX + your router service

My recommendation

If your goal is “route lots of API traffic across several model providers with minimal engineering overhead”, start with LiteLLM Proxy.

If you want, I can also give you:

1. a feature comparison table, or
2. a reference architecture for high-concurrency model routing.