Operations | Monitoring | ITSM | DevOps | Cloud

AIOps (Artificial Intelligence for IT Operations) is not only a game changer, but the need of the hour as modern IT grows and becomes increasingly complex. The promises of AIOps are both overwhelming and tantalizing. AI-powered monitoring and observability can help predict issues, automatically resolve incidents, and optimize performance across the IT infrastructure. However, onboarding an AIOps monitoring tool can be more complicated than it sounds on paper.

The OpenTelemetry (OTel) Collector plays a central role in collecting, processing, and exporting telemetry data. If you’re deploying the Collector in production, chances are you’ve reached for the otelcol-contrib distribution. It’s the easiest, most flexible, and most documented distribution, used in nearly every demo and getting-started guide. But here’s the catch: It’s not actually recommended for production use.

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.

Kong Gateway is a popular API gateway that sits at the edge of your infrastructure, routing and shaping traffic across microservices. It’s fast, pluggable, and battle-tested, but for many teams, it remains a black box. You might have OpenTelemetry set up across your application stack. Traces flow from your app servers, databases, and third-party APIs. But the moment a request enters through Kong, observability drops off.

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.

A few weeks ago, a lead DevOps engineer at a fast-growing SaaS company hit an unexpected wall. “It used to just work… until we scaled,” the lead noted after their Terraform setup began buckling under the weight of a growing cloud footprint. Another chimed in: “We’re spending thousands on infrastructure every week, but we can’t trace it back to who deployed what, or why.” Sound familiar? You’re not alone.

Today, we are introducing Live Call Routing, a direct phone line that connects incoming calls to on-call engineers. It captures human-reported incidents that monitoring tools might miss—closing the loop between automated alerts and real-world observations so nothing falls through the cracks. It helps you respond to critical incidents faster by eliminating manual call routing, reducing response times from minutes to seconds.

When you're managing a handful of client sites, setting things up manually is fine. Though if you're managing dozens of them, you're going to think twice about your approach. For agencies, development teams and platforms who are responsible for loads of websites, having to repeat the same configuration over and over is not only inefficient but also more prone to errors. That’s where this blog post comes in handy.

Is slow product delivery holding you back? This article explores how traditional infrastructure management creates significant bottlenecks, from time-consuming provisioning to inconsistent environments. Discover 5 strategies to streamline your delivery without managing infrastructure, including fully managed services, on-demand ephemeral environments, GitOps, self-service deployment platforms, and intelligent container orchestration.