Operations | Monitoring | ITSM | DevOps | Cloud

We optimized log processing pipeline by moving from sequential to concurrent processing at the entry level, achieving 30% higher throughput and better resource utilization without increasing infrastructure costs. When customers start sending millions of logs per minute, you quickly discover whether your processing pipeline can actually scale with vertical scaling.

As organizations rely more on complex IT systems and cloud-based services, keeping everything running smoothly — and reliably — has become a top priority. That’s where IT service performance monitoring comes in, giving teams the visibility they need to make sure systems stay healthy and responsive. By tracking a range of technical and user-focused metrics, businesses can quickly identify and address issues before they impact operations or end users.

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.

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.

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.

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.