Operations | Monitoring | ITSM | DevOps | Cloud

The model, the serving layer, and the agent asking questions all sit on local hardware. The first thing it debugs is the AI stack it is running on. No external inference provider in the path.

There’s a new class of cloud provider on the block in AI infrastructure, built specifically for GPU access instead of trying to do everything. We’ve got something in the works for exactly this kind of connectivity too Follow for weekly cloud watching with @digital.byte_ comment “cloud” to connect with our experts.

Learn how to implement end to end observability and monitoring for Grok Build, xAI's terminal coding agent, using OpenTelemetry and SigNoz. In this video, we walk through turning on Grok Build's native OpenTelemetry exporter, collecting metrics and structured session events, and visualizing everything in SigNoz to gain real time visibility into token usage, sessions and turns, tool calls and their outcomes, error categories, and startup latency. Grok Build ships its own exporter, so instrumenting it is a matter of configuration, with no library to install and no collector to run.

Learn how to implement end to end monitoring and observability for GitHub Copilot Chat using OpenTelemetry and SigNoz. In this video, we walk through enabling the OpenTelemetry exporter built into the Copilot Chat extension in VS Code, collecting a trace for every agent turn, and visualizing everything in SigNoz to gain real time visibility into model calls, tool executions, token usage, prompt cache savings, latency, and failures. Copilot Chat ships its own OTLP exporter, so there is no instrumentation library to install and no collector to run.