Request Logging
Capture complete LLM request and response records with model, provider, latency, token count, cost, and outcome data for debugging, auditing, and production analysis.
FastRouter gives engineering, product, and platform teams a complete view of every LLM request from prompt to response. Trace latency, errors, token usage, cost, model choice, provider behavior, and output quality in one OpenAI-compatible control plane, so you can debug faster, prove performance, and operate production AI with confidence.
Trace requests, monitor performance, analyze spend, and debug production LLM workloads from one gateway.
Capture complete LLM request and response records with model, provider, latency, token count, cost, and outcome data for debugging, auditing, and production analysis.
View performance, usage, latency, and error trends across every connected model and provider in unified dashboards built for production AI operations.
Track latency, uptime, response times, error rates, and model quality signals in real time to detect slowdowns before users are impacted.
Break down token usage, request volume, and cost by model, provider, project, and team to understand exactly where AI consumption goes.
Receive real-time notifications when cost, latency, error rates, provider failures, or unusual usage patterns exceed the thresholds your team defines.
Score and compare model outputs over time to monitor quality, catch drift, and support evidence-based model selection for production workloads.
FastRouter turns fragmented LLM activity into a single operational view. Every request flowing through the gateway can be logged, measured, analyzed, and connected to cost, latency, provider health, routing behavior, and output quality. Instead of stitching together separate dashboards, teams get reliable request-level visibility for debugging, optimization, compliance review, and production AI decision-making.
See how unified tracing helps teams operate multi-provider AI systems with greater confidence and control.
"Excellent platform to test the latest LLMs for our use case. With new LLMs coming out every few weeks and benchmarks not giving the full picture, I rely on Fastrouter.ai to optimize my cost vs quality balance."
FastRouter helps teams operate LLMs with visibility, control, and production-grade reliability.
Centralize logs, metrics, routing, alerts, and analytics across every model provider.
Trace each request with latency, cost, tokens, provider, model, and outcome details.
Use alerts and monitoring to catch cost, latency, and reliability issues quickly.
Apply spend limits, roles, access controls, and audit logs at the gateway.
Built for teams operating LLMs in production.
FastRouter is built as an LLMOps control plane for teams moving beyond experiments into production AI operations. Rather than treating observability, routing, governance, evaluations, and billing as disconnected tools, FastRouter brings them together behind one OpenAI-compatible gateway. The platform is designed for engineering leaders, platform teams, product teams, and organizations running workloads across multiple models and providers. Its vision is to make production LLM infrastructure more reliable, measurable, and governable, while still giving teams the flexibility to test, compare, and adopt new models quickly as the ecosystem changes.
LLM tracing records the full lifecycle of a model request, including the prompt, selected model, provider, latency, token usage, cost, response, error status, and routing outcome. In FastRouter, tracing happens at the gateway, so every request across providers is captured consistently without adding separate logging code for each model integration.
Get practical guidance on tracing your production LLM traffic.
Compatible with existing OpenAI SDK workflows
Unified access to leading model providers
Centralized controls for production AI operations
Tell us about your current LLM stack, providers, traffic volume, and visibility gaps. We’ll help you evaluate how FastRouter can centralize tracing, observability, governance, and model operations across your production AI workloads.
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