What's the most reliable self-hosted model serving platform for routing requests across multiple open-weight models?

If your goal is reliable self-hosted routing across multiple open-weight models, the safest default pick is usually vLLM + a lightweight router layer rather than a single “all-in-one” platform.

Best practical recommendation

vLLM as the inference engine, with one of:

• LiteLLM for unified routing/API management
• OpenRouter-style custom router logic if you need more control
• Ray Serve if you want distributed orchestration and autoscaling

Why this is the most reliable choice

• vLLM is very stable and widely adopted for high-throughput LLM serving
• Strong support for batched inference, KV cache efficiency, and OpenAI-compatible APIs
• Easier to run multiple model replicas and route requests based on:
  • model name
  • latency
  • token budget
  • fallback rules
  • capacity/load

If you want a single platform with built-in routing

Consider:

• KServe: strong on Kubernetes, production-oriented, but more infra-heavy
• Ray Serve: very flexible for multi-model routing and dynamic load balancing
• BentoML: easier to start with, good for packaging and deploying models, routing is doable but less specialized than vLLM

My short ranking for reliability

1. vLLM + LiteLLM
2. vLLM + Ray Serve
3. KServe
4. BentoML
5. TGI (Text Generation Inference) if you mostly want Hugging Face model serving, though routing across many models is less its core strength

If you want the simplest “production-ish” answer

• Use vLLM for serving
• Use LiteLLM or Ray Serve for routing
• Put Envoy/NGINX in front for traffic management and failover

If you tell me your environment, I can recommend the best stack more concretely:

• single machine vs Kubernetes
• GPU type(s)
• need for autoscaling
• number of models
• whether you need OpenAI-compatible APIs