Operations | Monitoring | ITSM | DevOps | Cloud

When I read about the Vercel breach tied to a Context AI compromise, I wasn’t surprised. I’ve been talking with customers for a while now about how AI was going to introduce a new kind of supply chain risk. This is exactly what that looks like. What stands out to me is how familiar the pattern is. We saw it with open source, then again with SaaS, and again with cloud.

AWS re:Invent 2025 made one thing very clear: enterprise interest in AI is no longer theoretical. The conversation has moved beyond curiosity. Teams are actively experimenting, leaders are looking for production-ready use cases, and engineering organizations are trying to figure out where AI can create real leverage across software delivery, security, platform engineering, and operations.

Why run Ubuntu on Windows? It’s about getting the best of both worlds. Many organizations rely on Windows applications, enterprise software, and policy configurations; but for developers and system administrators, Ubuntu’s native command-line tools, package managers, and server environments are invaluable. Likewise, with its broad ecosystem of machine learning tools and libraries, and silicon optimizations, Ubuntu is ideally suited for AI workloads.

Confluent Cloud pricing ranges from $1,000–$200,000+/month based on cluster type, throughput, and add-ons.

Learn exactly how Amazon Comprehend pricing works, NLP, PII, custom models, endpoints, and how to control costs before they spiral.

Application security & engineering teams are under pressure to move faster, cover more, and reduce the operational drag that often comes with security testing. But in practice, two problems keep slowing teams down and adding friction.

GPU infrastructure is expensive and increasingly central to production workloads. Whether you’re running ML training jobs, inference serving, video transcoding, or HPC workloads, understanding what your GPUs are actually doing, and what’s going wrong when performance degrades, is not optional.