Operations | Monitoring | ITSM | DevOps | Cloud

At Datadog, our broad Kubernetes footprint amplifies the significance of a familiar autoscaling tradeoff: Overprovisioning wastes cloud spend, while underprovisioning threatens reliability. We built Datadog Kubernetes Autoscaling (DKA) to help teams rightsize their workloads by generating intelligent resource recommendations and automating multidimensional workload scaling. Across Datadog, adopting DKA has eliminated more than $3 million in annualized idle compute costs while reducing reliability risks.

Feature flag migrations have a reputation problem. Ask anybody who’s been through one before and you’ll hear the stories, usually from someone still a little frustrated about a bad cutover, with a postmortem or two to show for it. The reputation is mostly undeserved. While the risks are real, they’re well understood and easily controlled. Getting a migration right doesn’t require a big coordinated effort.

AI teams have invested heavily in evaluation frameworks, yet getting those frameworks beyond local experimentation remains challenging. Teams using open source libraries like DeepEval and Pydantic Evals gain flexibility and research-grounded metrics, but operationalizing those evaluations still requires brittle custom integration code that doesn’t scale.