LLM Proxy Gateway for Fast, Reliable Requests

An LLM proxy server sits between your application and model providers, forwarding requests through a central layer instead of calling each provider directly. In production, a proxy can standardize authentication, logging, retries, rate-limit handling, routing, and policy enforcement. FastRouter extends this pattern with one OpenAI-compatible API, access to 100+ models, failover, governance, and observability across providers.

An LLM proxy usually focuses on forwarding and standardizing requests between applications and model APIs. An AI gateway is broader: it adds operational controls such as model routing, automatic failover, usage analytics, guardrails, evaluations, cost governance, and consolidated billing. FastRouter functions as an LLMOps control plane, giving teams both proxy-style compatibility and gateway-level production management.

FastRouter improves reliability by removing single-provider dependency. Requests can be routed across multiple providers and models, with fallback lists that automatically move traffic when a provider fails, slows down, or hits rate limits. This helps production AI applications maintain availability, improve effective capacity, and recover without manual intervention or emergency code changes.

Yes. FastRouter is OpenAI-compatible, so teams can often keep their existing OpenAI SDK code and change the base URL to route requests through the gateway. That preserves familiar development workflows while unlocking access to additional models, routing policies, observability, guardrails, failover, and consolidated governance from a single integration point.

FastRouter reduces AI costs by routing requests to models that meet your quality, latency, or throughput needs without automatically defaulting to premium models. Teams can apply project-level and API-key-level limits, analyze usage by model and team, and use audits or evaluations to identify workloads where lower-cost models perform well enough for production.

FastRouter provides unified visibility into request volume, latency, errors, token usage, provider performance, and cost across connected models. Detailed logs show model, provider, outcome, token counts, and response timing for individual calls. This makes debugging easier, helps teams detect regressions, and gives engineering and finance leaders a shared view of AI consumption.

Guardrails run at the gateway layer, so the same validation and safety policies can apply across every model and provider. They can inspect inputs and outputs, flag unsafe or non-compliant content, validate structured responses, and log violations for review. This is especially useful for teams that need consistent policy enforcement across multiple applications or regulated workflows.

Yes. FastRouter offers free credits for building, testing, and exploring the unified API, with no credit card required for initial access. Teams can evaluate model access, routing behavior, latency, observability, failover, and governance before adopting it in production. This helps platform and product teams validate fit without committing upfront spend.