Operations | Monitoring | ITSM | DevOps | Cloud

Ford's competitors had the same electric motors he did. Most just swapped out the steam engine and kept the old factory layout, a costly mistake. Ford used the new tech to rebuild the plant around the flow of the car. Knowing how much power each machine drew, he knew his cost to produce a car, and made personal automobiles affordable for all.

Every on-call engineer has lost an evening to some version of this. Something breaks; the fix is usually somewhere in the logs, and the logs are scattered everywhere: a dozen servers, a few containers, a couple of cloud services, none of them in one place. So, you SSH into one box, grep, get nothing, move to the next, and an hour later there are fifteen terminal tabs open and still no clear sequence of events. Log aggregation tools kill that scramble.

A DevOps platform unifies the entire software delivery lifecycle. Unlike standalone tools that automate individual tasks, a DevOps platform connects CI/CD, security, governance, artifact management, infrastructure, and engineering workflows into a single software delivery system. Tool sprawl creates operational complexity as engineering organizations scale.

Application autoscaling has existed for years. Database autoscaling has lagged behind. Today, that changes on Upsun. Managed PostgreSQL and MariaDB read replicas now autoscale, joining worker autoscaling in production since March 2026. Apps, workers, and managed databases all autoscalead replicas saturate first, connection pools max, someone getse on one platform - natively, without a third-party database engine or a Kubernetes layer to operate. Three operational headaches just disappeared.

The new Advanced Pipeline Editor went live for all paid plans, we launched a public Blueprints library of ready-made pipeline patterns, we reworked plan pricing and raised the Free tier to 100 GB/day, and the source and processor catalog grew with an AWS Neuron source, an AWS CloudWatch metrics source, and a full set of XML processors. None of these are flashy on their own.

We’ve overhauled macOS monitoring in the latest Netdata release. Netdata already collects system metrics on Macs at per-second resolution; this release completes the picture with logs and hardware telemetry, areas that previously required users to run CLI tools like log show and powermetrics. The new collectors read this data through Apple’s own frameworks, allowing users to trace application and OS errors and catch hardware issues early.

Software systems are getting better at understanding themselves. The mix of richer telemetry, smarter pipelines, and agentic AI is shifting observability from a passive record of events into something more active and useful. That shift is what we mean by context engineering. We recently partnered with O’Reilly on a report by David Beale that introduces the discipline. Before you read it, here are five things worth knowing.

We’re delighted to share that Grafana Labs has been named a Leader in the Gartner Magic Quadrant for Observability Platforms for the third consecutive year. Notably, we’re also positioned furthest in “Completeness of Vision” for the second year in a row.

You run . Nothing is broken yet, but nothing will proceed either until the lock is cleared. This guide covers what actually does, when it is safe to use, how to find the lock ID on every major backend, and what to do if the command itself does not fix things.

We are thrilled to announce that Datadog has been named a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms, for the sixth consecutive year. We believe this recognition reflects our continued focus on helping customers observe, secure, and act on everything that matters across their technology stack. Datadog was positioned highest in Ability to Execute in the 2026 Gartner Magic Quadrant for Observability Platforms.