Operations | Monitoring | ITSM | DevOps | Cloud

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.

I remember the first time I used an AI coding assistant. I watched the cursor dance across my screen and generate a hundred lines of code in seconds. It felt like I had finally found a cheat code for software engineering. That initial rush of productivity is a dopamine hit that's intoxicating and makes you think you can do anything with just a simple prompt or two.

We’re excited to announce a new integration between Bindplane and Statsig, making it easier to collect, process, and route OpenTelemetry signals into Statsig at scale. This integration provides a seamless way to connect Statsig with the OpenTelemetry ecosystem using Bindplane’s vendor-neutral, OpenTelemetry-native telemetry pipeline. Focus on product insight, not collector operations.

For many IT directors, the service desk feels permanently stretched. It’s a math problem that is forever in motion. Every quarter brings new apps, new devices, new access rules, and new ways for small issues to become daily interruptions. Even when tooling improves, the queue still grows because the work expands with the environment. The pressure shows up in familiar places, like rising ticket counts, tighter SLAs, and a large backlog of projects that need help.

Distributed tracing has become essential for modern software teams. As applications evolve into complex distributed systems with microservices, APIs, databases, and third-party integrations, understanding how a single user request travels through your entire stack is no longer optional, it’s critical for maintaining performance, reliability, and user satisfaction.

When we launched Seer, our AI debugging agent, we built it on a core belief: production context is essential for understanding the complex failure modes of real-world software. Seer uses the detailed telemetry that Sentry collects (errors, spans, logs, metrics, and more) to accurately root cause and fix bugs. Because this telemetry is trace-connected, Seer can deterministically traverse all the data relevant to a problem rather than relying exclusively on imprecise time-range searches.