Operations | Monitoring | ITSM | DevOps | Cloud

When I spoke at AWS London earlier this year, I had the chance to discuss something that more and more teams are starting to feel: traditional observability doesn’t cut it for AI systems. In AI, “Is it running?” is no longer enough. We have to ask, “Is it right?” When I delivered that line, I saw the heads nodding. Everyone’s excited to build with LLMs, but when it comes to actually monitoring them in production? That’s where things fall apart.

Cloud Observability’s curated Application Monitoring dashboards improve troubleshooting with best practices from Google SREs.

Following the popularity of our existing GitHub integration, we’ve extended similar capabilities to GitLab users. You can now ingest GitLab events directly into Mezmo Telemetry Pipelines and route them to any destination. This provides a powerful new way to monitor, alert, and react to activity within your GitLab repositories.

It’s 2 AM. An incident’s in progress. Error rates are climbing. You jump into the logs, filter by service, adjust the time window… and now you need a LogQL query. You write one. It errors out. You fix the syntax, try again, only to realize you need a different filter or a new aggregation. Back to rewriting. By the time you’ve got the query right, you’ve already lost 10–15 minutes. The system is still broken, and you still don’t know why.

Elastic now integrates with the NVIDIA Enterprise AI Factory validated design to provide users with a recommended vector database for their on-premises AI Factories. The validated design provides enterprises with a framework for building and deploying AI Factories on-premises.

Kibana gives you a structured way to explore log data indexed in Elasticsearch. With the right queries and visualizations, you can identify anomalies, debug issues more quickly, and track trends across services. This blog covers practical ways to query logs using Kibana’s Lucene and KQL syntax, build visualizations that surface meaningful signals, and set up dashboards for ongoing log-based monitoring.

Manual log investigation is one of those engineering tasks that quietly drains hours without offering much real value. You're debugging an incident. Monitoring shows elevated error rates. Now begins the familiar drill: It’s a tedious cycle, and it doesn’t scale. The whole process breaks down when you’re trying to automate incident response, run continuous security monitoring, or generate compliance reports.