Operations | Monitoring | ITSM | DevOps | Cloud

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

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.

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.

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.

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.

IT leaders are replacing traditional focuses with three things that now outrank everything else: AI readiness, operational resilience, and unified visibility. You can’t add another priority to the list. There’s no space left. Your team is already stretched managing hybrid infrastructure, responding to incidents, juggling tool sprawl, and delivering on AI promises while keeping costs under control.

Not every workload should scale on CPU. Qovery brings event-driven autoscaling into the application lifecycle, letting applications scale on real signals like queue depth or request latency.

A single disengaged employee can cost an organization approximately $2,246 annually. In today’s technology-driven workplaces, that cost increasingly comes from technology friction – the everyday delays, disruptions, and inefficiencies caused by poorly performing tools. “There’s not really any businesses today that are not fundamentally technology businesses now.”⁠⁠Dan Anthony, CIO at FedNow.