Operations | Monitoring | ITSM | DevOps | Cloud

Last year, I wrote about how AI-driven search trends reshaped my digital marketing strategy in ways I hadn’t seen in two decades. At the time, the story was mostly observational: traffic patterns were changing, conversions were holding, and AI-generated search answers were clearly influencing buyer behavior. Fast-forward to the first quarter of 2026, and one thing is clear — this shift didn’t slow down; it accelerated.

How to say good-bye to your assets in a secure and safe way.

The infrastructure industry spent two decades chasing a single pane of glass. The future looks different: domain-expert AI platforms that reason deeply within their own data, connected through tool chaining when problems cross boundaries.

Runtime guardrails and control towers govern AI activity — but without a financial control plane connecting spend to outcomes, enterprises can't tell which AI bets are worth it. Most enterprises can answer exactly one question about their AI rollout: what did we spend?

The NoSQL Storm explores core Database DevOps concepts and the real-world challenges teams face while managing modern NoSQL environments. It highlights the complexities of schema evolution, scaling distributed systems, and maintaining operational reliability as applications grow.

Performance testing is critical to ensure your applications stay reliable under load, but writing the scripts themselves often feels like a chore. Most engineers already know the scenario they want to test; the hard part is translating that intent into a working performance test. Even experienced developers who use k6 can lose time looking up syntax, configuring load stages and thresholds, or debugging boilerplate code before they can run a meaningful test.

Cloud and SaaS spending continues to grow across teams, services, and providers, changing too quickly for retrospective cost management workflows to keep up. Finance and engineering leaders often rely on last month’s reports or manually maintained spreadsheets, which don’t reflect current usage. As a result, teams lack context on how spend is trending and often discover budget overruns only after they’ve occurred.

Let's walk through setting up SIEM (Security Information and Event Management) alerts to monitor security threats in applications. We will explain what SIEM alerts are, why they're relevant with regard to application security, and provide practical examples of common alerts a developer could implement. We will show how to configure simple alerts with Honeybadger Insights.

Compliance teams are entering a moment where the expectations placed on them far exceed the visibility tools they have available. AI-driven environments introduce new forms of variance, drift, and distributed decision-making that unfold across infrastructure, models, agents, and services. These patterns do not map cleanly to the evidence structures that compliance processes rely on.

Every single day, the Sumo Logic Platform analyzes more than four exabytes of log data. The good news? The answers to your application performance, infrastructure health, and security incidents are hidden in those logs. The challenge? Historically, uncovering those answers required query language fluency. That’s why we built Mobot, our conversational interface that connects users to advanced AI capabilities using natural language.