Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

Event Intelligence Solutions are redefining how organizations manage complexity and risk across digital ecosystems. Their true power lies not only in detecting anomalies or suppressing noise, but in providing actionable, explainable intelligence that connects IT events to business impact.

InfluxData prides itself on prioritizing developer happiness. A key ingredient to that formula is providing client libraries that let users interact with the database in their chosen language and library. Data analysis is the task most broadly associated with Python use cases, accounting for 58% of Python tasks, so it makes sense that Pandas is the second most popular library for Python users.