What is LLM Observability?
The ability to monitor, trace, and analyze LLM API calls including tokens, costs, latency, and errors.
LLM observability refers to the tooling and practices that give you visibility into how your AI application is behaving in production. It covers token usage per request, cost attribution, latency distribution, error rates, and model performance over time.
Without observability, it is impossible to know which part of your application is driving costs, which prompts are failing, or whether a model change improved or degraded quality. Observability is the foundation for cost optimization — you cannot reduce what you cannot measure.
GateCtr provides built-in observability for every API call: tokens in/out, cost in USD, model used, latency, compression ratio, and routing decision. All data is available in real-time in the dashboard and queryable via the REST API.
GateCtr addresses llm observability automatically on every API call — no configuration required. The results are visible in real-time in the GateCtr dashboard, with per-request breakdowns of tokens, cost, and savings.