Continuum Router:
Fast, Consistent Routing for Distributed LLM Requests

This brief is written for platform engineers and service operations teams that run external LLM APIs alongside self-hosted models and are evaluating an LLM gateway. It is especially suited to organizations that serve agent workloads on multiple model replicas, such as code review or document analysis agents that load an entire source repository or document set as context on every request. Teams that want to unify multiple provider APIs and self-hosted models behind a single OpenAI-compatible API without client changes, that need the gateway to isolate a single backend failure from the rest of the service, or that deploy gateways at multiple points such as sidecars and edges while keeping resource usage minimal can also evaluate it. Organizations that start with a standalone gateway and plan to extend to the Continuum Hub control plane later are also included.

Continuum Router provides backend selection and load balancing, KV cache-aware routing, backend failure handling through circuit breakers and model fallback chains, request policy enforcement with rate limits and guardrails, and observability in a single binary. See the full solution brief for routing quality, resource, and throughput measurements against central-proxy gateways such as LiteLLM, a proof-of-concept sequence, and recommended configurations by deployment purpose.