Operations | Monitoring | ITSM | DevOps | Cloud

For developers building AI applications, training models, or running inference pipelines, the GPU cloud market in 2026 has never offered more choice - or more complexity. Picking the wrong platform means overpaying, dealing with availability problems, or battling infrastructure that slows you down rather than accelerating your work.

Flat per-token pricing is wrong by 10–50× per request. Prefill vs decode, batch sharing, and cache effects break the math. How to attribute real GPU cost - compute, energy, and dollars - to each inference request.

A rolling deployment updates running application instances in batches, replacing old instances with new ones while the application keeps serving traffic. The concept applies to any system that can run multiple instances of an application, but Kubernetes has it built in as the default deployment strategy. Kubernetes terminates an old pod only after its replacement passes the configured readiness check, so no requests land on an unready instance.

Network outages are easy. Something goes down, alarms fire, you fix it, life moves on. Everyone understands a full outage. It's clean, binary, and at least somewhat predictable. Network instability is the opposite of all that. Nothing fully breaks. Nothing fully works. The ping responds. The connection shows active. And yet users are complaining about choppy calls, sluggish apps, and sessions dropping for no apparent reason. You run a speed test, and it's fine.

Technical teams want to know the newest, most cutting-edge tools they can implement to give themselves a competitive advantage, whether it’s the latest developer framework or modern CI/CD practices that boost velocity. But there’s one tool from all the way back in the 1920s that can improve any organization, no matter its scale: the randomized, controlled trial—or simply put, experiments.

Platform Engineering leaders are caught between two competing imperatives. You’re under pressure to flatten cloud spend but your team is still provisioning defensively because nobody wants to be the person who causes a production incident. You try to optimize, but six months later, when someone pulls a report, nothing has changed.

In this blog, we’re going to take a look at how you can set up a fully-functioning, robust data pipeline to centralize your data into an InfluxDB instance by collecting and sending messages with the MQTT protocol. We’ll start with a brief overview of the technologies and protocols used in the pipeline, then dive into how you can connect, configure, and test them to ensure your data pipeline is fully functional. It’s going to be a long post, so let’s jump right in.

Last month I was making a change to sx, our CLI. I updated a core flow, adding external catalogs as a source for sx add. Small change. Then came the testing. I knew I was messing with a core flow and wanted to be sure I hadn't broken anything. I spent about forty-five minutes setting up an isolated environment. Spinning up Docker. Fighting with tmux. Getting a clean install state I could run through the TUI a few times. Forty-five minutes of my afternoon that produced zero code. I complained in Slack.

Healthchecks.io ping endpoints accept HTTP HEAD, GET, and POST request methods. When using HTTP POST, clients can include an arbitrary payload in the request body. Healthchecks.io stores the first 100kB of the request body. If the request body is tiny, Healthchecks.io stores it in the PostgreSQL database. Otherwise, it stores it in S3-compatible object storage. We recently migrated from a managed to a self-hosted object storage.

Picture this: It's 2 a.m. Your phone is buzzing. A new feature just went out to your entire user base, and conversion rates are tanking. Your on-call engineer is digging through logs, your Slack channels are on fire, and you’re left wondering, Why didn't we just test this first? Every CTO has a version of this story. And most of them have quietly vowed never to repeat it.