Real-Time LLM Routing for Low-Latency Apps

Real-time LLM routing sends each request to the best available model at the moment it is made. FastRouter can prioritize low latency, cost efficiency, output quality, or high throughput across 100+ models. Instead of hard-coding one provider, your app calls a single OpenAI-compatible endpoint while routing policies select the right model dynamically.

FastRouter supports low-latency apps by routing requests to quicker-responding models and providers, monitoring response times in real time, and failing over when a provider slows down or errors. Teams can compare latency across models, set routing strategies, and avoid single-provider bottlenecks that can create unpredictable response times in production.

Yes. FastRouter provides a single OpenAI-compatible API that works with existing OpenAI SDK-based code by changing the base URL. Through that one integration, teams can access models from providers such as OpenAI, Anthropic, Google Gemini, xAI Grok, and others across text, image, video, embeddings, and speech use cases.

FastRouter can automatically fail over to fallback models or providers when requests hit outages, rate limits, or model errors. Teams define prioritized fallback lists or virtual model lists, and the gateway reroutes failed requests without application code changes. This helps maintain uptime for production AI workflows that cannot depend on a single provider.

Routing can be configured around cost, latency, quality, or throughput. Cost-optimized routing helps reduce spend by selecting lower-cost models that still meet your quality bar, while low-latency routing favors faster responses. Project-level and API-key-level limits add governance so teams can prevent surprise bill spikes during high-volume usage.

Virtual Model Lists let teams expose one stable model alias to applications while managing the underlying model priorities centrally. You can define a primary model, ordered fallbacks, and policy-based selection rules. This allows engineering teams to swap, re-prioritize, or fail over models without redeploying every application that depends on them.

FastRouter includes real-time observability with metrics and logs across models and providers. Teams can monitor latency, error rates, token usage, cost, and request outcomes from unified dashboards. Detailed request logs help engineers debug issues, compare provider performance, detect regressions, and prove production AI performance to internal stakeholders.

FastRouter is designed for production teams that need reliable, governed AI infrastructure. It includes automatic failover, multi-provider redundancy, usage analytics, access controls, spend limits, guardrails, evaluations, and consolidated billing. These features help teams operate LLM applications at scale while maintaining performance, accountability, and control across departments.