llm-observability

LLM observability enables structured tracing, latency profiling, and per-call cost attribution for production AI agents. It applies OpenTelemetry GenAI semantic conventions to monitor distributed LLM deployments across self-hosted or cloud environments.

Drift detection in LLMOps identifies performance degradation and behavioral changes in high-stakes LLM deployments. It works by running online evaluation workflows and grounding checks alongside continuous telemetry monitoring.

Per-call LLM cost attribution uses four-layer token accounting to measure resource consumption for individual inference requests. This approach tracks token usage across distributed deployments to deliver precise operational cost measurement.

Yes, OpenTelemetry GenAI semantic conventions are supported for telemetry conformance checks. The system enforces conformance rules and budgets while applying registry-driven prompt management to ensure auditable AI operations.

Prompt versioning policies are managed through a centralized prompt registry enforcing stable operational templates. This registry-driven approach delivers reliable governance by maintaining strict versioning discipline across distributed LLM systems.

Yes, LLM observability supports TTFT and ITL latency profiling to measure end-to-end production performance. These metrics enable detailed latency analysis for distributed LLM deployments, ensuring reliable and auditable AI operations.