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.

Lightrun's Gidi Freud demonstrates how your AI coding agent can now investigate and fix production errors, autonomously. Watch how Cursor, guided by Lightrun's Error Remediation skill, picks up a Sentry error, instruments the live service with a runtime snapshot, captures real evidence, and opens a validated PR for approval.

This walk-through shows how Splunk's Cloud can discover schema and partition keys for Microsoft Azure Blob Storage datasets and create searchable Splunk managed tables. Once the data is mapped, analysts can use Splunk Federated Search to query Azure Blob data where it lives, bringing cloud-resident logs into security, observability, and operational work-flows without re-ingesting the data.

I recently sat down with Simon Overbey and Lovepreet Singh - the Engineering Manager and systems engineer (respectively) at Getty Images to talk about their experiences implementing Cribl. After getting a rundown of the pre-Cribl environment (described above) I asked to jump straight to the end, the net benefits. If the "before" was a terrifying tidal wave of cost and complexity, what did the "after" look like?