Inference Observability

High-resolution, inference-native profiling, tracing, and metrics across models, engines, and GPUs.

Inference profiling

Continuous, high-resolution profiling timelines exposing operation durations and resource utilization across inference workloads.

LLM tracing

LLM generation tracing with per-step timing, token throughput, and latency breakdowns for major inference frameworks.

System metrics

System-level metrics for inference engines and hardware (CPU, GPU, accelerators).

Error monitoring

Error monitoring for device-level failures, runtime exceptions, and inference errors.

AI optimization

Inference telemetry for AI agents to identify bottlenecks and drive targeted improvements across the inference stack.

Read more about inference performance monitoring and optimization

Article

autodebug: Telemetry-Driven Inference Optimization Loop

An autonomous agent that deploys inference services, collects telemetry, and continuously redeploys with better configurations - indefinitely.

Mar 24, 2026

Article

AI Debugging and Optimization For Production Inference

A practical workflow to debug production inference issues and optimize performance using Claude Code and Graphsignal debug context.

Mar 17, 2026

Article

Traditional Observability Is Blind to Inference

Inference observability monitors inference systems at millisecond granularity, exposing internal runtime and GPU behavior hidden by second-level metrics.

Mar 17, 2026