LLM Observability Platform for AI Workloads

An LLM observability platform gives engineering, ML, and product teams visibility into how AI workloads behave in production. FastRouter captures metrics, logs, latency, cost, errors, and output quality across providers, so teams can debug failures, monitor model performance, detect regressions, and prove reliability without stitching together separate dashboards from every AI vendor.

FastRouter monitors AI workloads at the gateway layer, where every request passes through one OpenAI-compatible control plane. It records model, provider, latency, token counts, cost, outcome, and request activity, then presents this data in unified dashboards. Because monitoring is provider-agnostic, teams get consistent visibility across OpenAI, Anthropic, Google Gemini, xAI Grok, and other models.

FastRouter tracks latency, uptime, error rates, request volume, token usage, cost, provider behavior, model performance, and output quality signals. Teams can filter by model, provider, project, team, cost, latency, or errors to find regressions quickly. Detailed activity logs also make it easier to trace individual calls and understand exactly what happened.

Yes. FastRouter captures detailed request and response logs for LLM calls flowing through the gateway. Logs include useful operational data such as model, provider, latency, token counts, cost, and outcome. This helps engineers reproduce issues, audit usage, analyze failures, and maintain an authoritative record of AI activity across multiple providers.

FastRouter helps reduce AI spend by connecting observability with usage analytics, cost tracking, alerts, routing, and governance. Teams can see which models, projects, and workloads drive consumption, detect unusual spikes, and enforce project- or API-key-level limits. This makes it easier to optimize model selection while preventing surprise bill increases.

FastRouter supports real-time alerts for cost, latency, and error-rate thresholds across models and providers. Teams can be notified when budgets are at risk, providers degrade, models fail, or unusual usage patterns appear. Alerts connect back to unified dashboards and logs so engineers can move from notification to root cause quickly.

FastRouter Evaluations score and compare model outputs so teams can measure quality, consistency, and performance over time. Evaluations can be used to choose the right model for a task, benchmark new releases, and catch quality drift as prompts, traffic, or providers change. This gives teams evidence-based confidence in production AI decisions.

FastRouter is designed for production teams running AI across multiple models, providers, projects, or internal applications. It is especially useful when reliability, cost attribution, debugging, governance, and quality monitoring matter. Teams can start by routing traffic through the OpenAI-compatible gateway and using unified dashboards, logs, alerts, and evaluations to operate more confidently.