Datadog released a free eBook that tackles common cloud‑budget leaks in Kubernetes and ECS. The guide pinpoints idle containers, over‑provisioned pods, and unused clusters, then offers five hands‑on tactics. Readers learn to right‑size CPU and memory with resource requests, deploy spot instances, and apply savings plans, potentially trimming spend by up to 90%.

Separately, a ByteByteGo episode dissected the differences between MCP and Skill extensions for AI agents. MCP operates as a client‑server protocol that connects multiple agents to a single backend via JSON‑RPC, while a Skill is simply a directory containing a SKILL.md and optional scripts. MCP adds a separate runtime, whereas Skills run inside the agent’s own environment, keeping infra minimal.

These insights arrive as platform engineering evolves toward AI‑first workflows. By using MCP for live system integration and Skills for reusable agent knowledge, teams can avoid unnecessary complexity and cost. The Datadog eBook and the MCP/Skill comparison together give engineers concrete, immediately deployable strategies to cut cloud spending and streamline agent development, and reduce infra overheads while maintaining performance.