Operations | Monitoring | ITSM | DevOps | Cloud

When engineering leaders review their cloud bills, they often focus on production costs—the infrastructure serving real users, processing real transactions, generating real revenue. But there’s a shadow cost lurking in every cloud environment that often goes unnoticed until it becomes painful: non-production infrastructure.

We’ve added a new enhancement to the StatusGator API that makes it faster and easier to identify service issues in real time: status-based filtering for monitors and components.

This report presents incident data from major cloud providers for December 2025, covering AWS, Azure DevOps, DigitalOcean, Fly.io, Heroku, Linode, Netlify, Railway, Render, and Vercel. The data includes both officially reported incidents from provider status pages and unconfirmed incidents detected by IsDown's monitoring system.

For years, the Kubernetes sidecar pattern has been a practical way to capture observability data. Running a collector alongside each application pod gave us deep visibility into traffic, including full request and response payloads across supported protocols. However, as cloud-native environments have grown more complex, the limitations of sidecars—such as resource overhead, operational complexity, and scaling challenges—have become more apparent.

It’s no secret that all software and more broadly, any technology that doesn’t move atoms is ripe for disruption by the current and future capabilities of large language models. Any workflow, application, or digital process that can be expressed in code can be redesigned, improved, and transformed at speed and scale. AI-first companies will outpace legacy players by orders of magnitude, and many workflow-based models with humans in the loop will be fundamentally reshaped.

AI that understands human insight and connects it to the changes that drive real incidents. At Harness, our story has always been about change — helping teams ship faster, deploy safer, and control the blast radius of every modification to production. Deployments, feature flags, pipelines, and governance are all expressions of how organizations evolve their software. Today, the pace of change is accelerating.

With all we’ve seen from AI in the last several years, it can be easy to forget that it’s still in its very early days. As torrid as its evolution has been thus far, it will only intensify. As SVP of Engineering at a B2B SaaS company, I’ve had a front-row seat for much of this evolution. Here are three ways I see AI heading in 2026.

I’d like to share a new influxctl ease-of-use feature in v2.12.0 that makes it easier to optimize important queries or debug slow ones. influxctl has had the capability to send queries and display the results in JSON or tabular formats for some time.

For many years, uptime and availability have been basic standard measures of server health monitoring. But if a server is up and responding to a ping or HTTP request, does that really mean that all is well? In reality, uptime and availability alone often provide a false sense of security. A server can be technically “up” while being seconds away from a crash, running out of memory, operating with an expired license, or silently failing critical updates.

Anyone who has ever attempted to learn the guitar knows the lure of buying high-end gear. Surely, an expensive guitar and a best-in-class amplifier will hide the fact that you only know a few chords and maybe the lead line to that one song you keep hearing on the radio. What most players find out, however, is that spending thousands of dollars on gear doesn't change the fact that you're not that good yet.