Operations | Monitoring | ITSM | DevOps | Cloud

We’re excited to announce that JFrog has officially earned the Microsoft Solutions Partner with certified software designation for Azure. This status is granted to partners who complete a technical review audit for interoperability with Microsoft products and demonstrate a consistent track record of customer success.

DCIM (Data Center Infrastructure Management) software plays a critical role in supporting this mission. By providing real-time monitoring, streamlined incident response, automated compliance tracking, and predictive maintenance capabilities, DCIM platforms offer healthcare teams the tools needed to safeguard patient safety and meet complex regulatory requirements.

Stop fighting CrashLoopBackOff on your AI deployments. Learn why traditional Kubernetes primitives fail large models and GPU workloads, and how to orchestrate AI infrastructure without shadow IT.

Stop fighting cluster sprawl. Learn why traditional scripting and GitOps fail at scale, and discover how to achieve fleet-wide consistency without the complexity of Kubernetes Federation.

Last updated: March 2026 Python applications built on Django, Flask, FastAPI, and other frameworks have the same monitoring needs as applications built in any other language: you want to know which endpoints are slow, why the database is getting hammered, what errors are firing in production, and ideally all of that in a form that does not require three separate tools to reconstruct a single incident.

If you've already instrumented your app with OpenTelemetry, you don't have to rip it out to use Sentry. Two environment variables and your logs start flowing into Sentry, no SDK changes, no re-instrumentation. Here's how to set it up in a sample app, and when the native Sentry SDK might be the better call.

This month, two FinOps research reports landed in close proximity. One from the FinOps Foundation — their 6th annual State of FinOps, drawing on a broad global practitioner community. One from CloudZero: FinOps in the AI Era: A Critical Recalibration, built on responses from 475 senior leaders at cloud-mature, AI-active organizations, with a focused lens on how AI is reshaping cloud cost management. Read each alone, and you get a useful snapshot for your business.

We recently hosted a webinar on AI-assisted development with DORA, and the audience had a lot of questions—far more than we could get to in an hour. I picked out six that get at the stuff people are wrestling with day to day. These aren't the easy questions, and I don't think there are necessarily easy answers, but I've spent the past year building and shipping with AI coding tools and observing (literally) what happens when that code hits production. Here's what I have.

Aiven put Diskless Kafka through 2,200 logical hours of chaos testing using Antithesis. Here's how it held up. Testing is a necessary pillar in any software development lifecycle. At the same time, it's a fundamentally incomplete process - a cat-and-mouse game. You can't test what you can't imagine and distributed systems have a nasty habit of producing failures that no one on the team imagined.

That is the defining business tension of 2026. A vanguard of roughly 12% of global enterprises, per PwC's 29th Global CEO Survey of 4,454 chief executives, has managed to deliver both revenue growth and cost reductions from AI simultaneously. These organizations are not just ahead. They are structurally pulling away. For everyone else, the 56% of CEOs who report no significant financial benefit from AI despite sustained investment, the clock is compounding against them.