Operations | Monitoring | ITSM | DevOps | Cloud

You shipped your AI app. But do you have all the answers? Do you actually know which model ran, how many tokens it consumed, or why it stopped? This is what LLM observability gives you, and most AI engineers are skipping it entirely. I built an SOS detection app and used OpenTelemetry to get full visibility into every single call. Token usage, model version, finish reason, and cost per call all in one place, standardised across any provider. Check out the OpenTelemetry GenAI docs in the link below; there is a lot more you can track than you think.

If you've standardized on OpenTelemetry (or you're heading that way), you probably know the collector gets your data out, but where it lands and how useful it is once it gets there are separate problems. Netdata now ingests both OTLP metrics and OTLP logs natively, so your OTel pipelines feed directly into the same monitoring experience as everything else in your infrastructure: same dashboards, same alerting, same query interface. No separate backends, no context switching.

Learn how to implement end-to-end monitoring and observability for Anthropic (Claude) API-based applications using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Anthropic API calls, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability, so you can debug faster, optimize latency, and confidently run Claude-powered AI systems at scale.