Operations | Monitoring | ITSM | DevOps | Cloud

Automation fails in ITOps because it’s treated as a local efficiency gain rather than a system-level change—an approach that breaks down at scale as AI raises the bar for context, ownership, and control. Modern ITOps environments are hybrid, distributed, and assembled from overlapping vendors and platforms. Services run across clouds and teams. Signals arrive continuously. Dependencies change faster than they can be documented.

Volkov Labs has been a longtime partner to Grafana Labs, with co-founder Mikhail Volkov contributing to Grafana in the early stages of the OSS project. On Sept. 26, the Florida-based company that recently created a suite of business intelligence (BI) plugins for Grafana announced it had been acquired. In light of the news, Grafana Labs committed to taking over the maintenance and development of their popular business intelligence (BI) plugin suite.

As budgets reset for 2026, engineering leaders are making a resolution: no more vendor lock-in. Here’s how to keep that promise by building on the technical foundations of data reliability and simplified collection. It’s January 2026, and if you’re like most engineering leaders, you’re staring at your observability vendor contracts with a mix of frustration and resignation.

You’re sitting at your desk, typing away, when all of a sudden you hear a “ping!” Unfortunately, you have a browser with fifteen tabs open, a task management application, email, messaging applications, and calendars all open, making it difficult to know exactly which technology just pinged you. To identify the source, you open your system settings and look at the notifications section to see which ones you allow to make a sound.

How streaming architecture and vector databases power instant, personalised retail experiences.

API performance monitoring has become a critical discipline for modern engineering teams, but most conversations around it stop at metrics, dashboards, and testing tools. Teams measure response time, track error rates, and run performance tests before release, yet APIs still slow down, silently fail, or violate SLAs in production. The problem isn’t a lack of monitoring. It’s a mismatch between how APIs are tested and how they actually behave in the real world.

A slow CI/CD pipeline costs more than just time. Developers context-switch while waiting for builds, feedback loops stretch longer, and compute costs add up with every inefficient run. Most teams know their pipelines could be faster, but optimizing configurations requires deep knowledge of caching strategies, parallelism, and resource allocation. The challenge compounds with AI-assisted development. As AI coding assistants help teams ship code faster, pipelines run more frequently.

d function there, and before long you’re navigating a codebase full of inconsistent patterns, repeated logic, and code that’s harder to maintain than it should be. Refactoring is essential, but finding the time to clean up code while shipping features is a constant challenge. The rise of AI-assisted development has accelerated this tension. AI coding assistants help teams ship features faster, but they don’t always produce consistent code.