Operations | Monitoring | ITSM | DevOps | Cloud

Bain says AI cost savings aren't arriving. But the value isn't missing, it's invisible. Most engineering teams can see token spend. They can see AI usage. What they can't see is whether any of it shipped, and whether it moved the needle on delivery. That's the measurement gap. And until it closes, AI ROI will keep looking worse than it should.

Artifact governance should not depend on manual checks. But for many teams, container images, software packages, and open-source dependencies are imported into registries from multiple internal and external sources. Without automated guardrails, vulnerable images, untrusted packages, end-of-life dependencies, or non-compliant artifacts can reach developers and delivery pipelines.

Managing MongoDB database changes shouldn't require manually creating and maintaining changelogs. In this video, you'll learn how Harness Database DevOps automatically generates MongoDB changelogs, helping teams capture existing database changes and bring them into version control for reliable CI/CD workflows. As a modern **database schema migration tool**, Harness Database DevOps helps teams automate database change management across relational and NoSQL databases, reducing manual effort and deployment risk.

What used to be $50 a month is now $3,000 — overnight. Microsoft just moved GitHub Copilot to token-based billing, and devs are split between calling it a "rug pull" and admitting someone always had to pay the bill. Here's the part that should worry every engineering leader: most can't tell you what percentage of their AI-generated code actually ships, or where the tokens went. When the meter is running on every prompt, "it feels productive" isn't good enough — you need to know that bug cost you $2,700 in tokens to fix.

AI spend doubled to $297B in two years — and most companies can't tell you what any of it shipped. Token spend is disconnected from outcomes on the dev side. Agents in production? The invoice is the only signal. Harness Cloud & AI Cost Management (CACM) gives teams unit economics at the inference level, cross-provider visibility across OpenAI, Anthropic, Bedrock, and Vertex AI, and request-level attribution to the agent, session, or workflow that triggered the spend.