Operations | Monitoring | ITSM | DevOps | Cloud

On November 18th, 2025, a large Cloudflare outage briefly broke big chunks of the internet. For several hours, users around the world were greeted with 500 errors, including platforms like X, ChatGPT, Spotify, and many others that run behind Cloudflare’s network. At UptimeRobot, we sit in a slightly unusual spot during events like this: So when Cloudflare has a bad day, we see it twice: once in the alerts we send to our customers, and again in how it affects parts of our own infrastructure.

Having just returned from the 2025 EDUCAUSE Annual Conference in Nashville, I want to share some insights on the future of campus IT from the higher education technology leaders in attendance. Every year, this conference provides an opportunity for technology providers and higher ed professionals to connect and explore the latest innovations in higher education technology. Two themes emerged as critical priorities.

Every growing engineering organization eventually faces the seemingly impossible decision between building a custom solution or buying one off the shelf. It’s a debate that often (and incorrectly) ends by choosing whichever option is less expensive. However, it’s become clear that solving the build vs. buy puzzle boils down to understanding what you want to be good at and whether your internal build is actually unique.

We just launched our Cursor Cloud Agent integration. Now when Seer finds a bug, it can hand it off to Cursor—replete with all the context Sentry has about the issue—to write the fix and create a PR for you.

Redgate Monitor v14.1.0 now provides a simple, built-in integration with ServiceNow that uses its REST API directly. This replaces the older webhook-based approach and removes much of the setup and maintenance overhead.

On November 18, 2025, X, ChatGPT, Shopify, and many other major sites went offline simultaneously. Even Downdetector, Ookla’s popular outage tracking website, briefly went offline. What caused this issue? Why were so many major websites affected by it? And what steps can you take to reduce the impact on your own applications? ‍

Today, many DevOps and security teams operate in a world of complex, hybrid, or multi-vendor environments. As more teams look to avoid lock-in by adopting open standards, OpenTelemetry (OTel) is quickly gaining adoption as the primary open source method for DevOps and security teams to instrument and aggregate their telemetry data. However, OTel alone may lack the advanced processing functions, native volume control rules, and hybrid environment support that large organizations need.

AI observability closes the gap between “something’s wrong” and “here’s what to fix.” If you run AI in production, you might have felt the whiplash. Yesterday, your LLM answered in 300 milliseconds (ms). Today p99 crawls, costs spike, and nobody’s sure if the culprit is model behavior, data freshness, or GPUs stuck at the ceiling. Dashboards light up, but they don’t tell you which issue puts customers at risk. That’s the gap AI observability closes.

AI workloads break every assumption you have about infrastructure management. AI is everywhere. Machine learning-based tools are answering customer service questions, accelerating incident resolution, catching fraudulent transactions, spotting defects on production lines, and powering late-night searches that delve into the random topic that pops into your head right before bedtime. Behind every prediction, response, or generated sentence is massive computing power doing serious, continuous work.

Monitoring AI systems isn’t business as usual. Monitoring AI isn’t like monitoring traditional systems. You can’t just track uptime or response times and call it a day. AI models evolve, data shifts, and behavior drifts over time, which means your monitoring has to evolve, too. If you’re running AI workloads in production, you already know this. Your models might look healthy according to your infrastructure metrics, but they’re still making bad predictions.