Introduction
OpenLLMetry applies OpenTelemetry principles to large models and generative AI scenarios. It captures request traces, response quality and latency metrics to help developers and operators diagnose inference workflows and improve observability.
Key Features
- Distributed tracing for model request call chains and timelines.
- Metrics aggregation for latency, error rates and response quality.
- Pluggable collectors to embed instrumentation in inference services or proxies.
Use Cases
- Monitoring performance and quality of LLM services.
- End-to-end diagnosis and root-cause analysis for inference requests.
- Integration with Prometheus/Grafana to build AI-specific monitoring dashboards.
Technical Details
- Built on open standards, compatible with OpenTelemetry data models and exporters.
- Lightweight collectors suitable for microservices and inference gateways.
- Designed for scalability to handle high-concurrency model request telemetry and sampling.