Operations | Monitoring | ITSM | DevOps | Cloud

AI adoption costs are going parabolic. The companies that can see what they're spending will invest with confidence. Everyone else is flying blind. Every company adopting AI is facing the same problem: the cost of AI adoption in products, in operations, and especially in engineering is accelerating with no alignment between spend and value. The competitive pressure is real. Companies that don’t invest in AI will be displaced by those that do. But the investment itself is becoming inscrutable.

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.

In the latest episode of This Month in Datadog, Jeremy shares how to run autonomous Cloud SIEM investigations, remediate vulnerabilities with auto-generated fixes, and use natural language to explore Datadog. Later, Sumedha Mehta spotlights the Datadog MCP Server, which gives AI agents real-time access to Datadog’s observability data. Then, Chetan Sharma walks through Datadog Experiments, which measures how product changes impact the user journey.

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.

Knowing something is broken is easy. Figuring out why is hard. Introducing three new, native AI diagnostic capabilities in the Kentik Network Intelligence Platform to accelerate root cause analysis and keep your network running better.