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If your team builds with AI, someone is defaulting to the biggest model available (say, Fable) because it feels like the safe pick, and the safe pick is almost always the most expensive one. One over-powered choice looks harmless on its own, but multiplied across every prompt, agent, and workflow, and you get a big number on the P&L. All that, yet nobody chose which model on purpose. As we like to say, using a default is not a decision.

AI cost reduction means lowering what you spend to run AI (tokens, inference, and compute) without sacrificing quality. The highest-leverage tactics, prompt caching, batching, and routing easy work to smaller models, cut spend 50 to 90% by removing waste, not capability. Somewhere right now, a finance leader is opening an AI bill that has quietly tripled, with no new product to show for it. Nobody approved it. No single decision caused it.

Hundreds of files vanished from SharePoint. How to tell ransomware or a wipe from a user re-filing a folder, using the signals in the audit log that actually separate them.

Harness shipped 71 features in July, about one every 10 hours. That's more than June's 62, and the surge lines up with what AI is doing to the rest of the SDLC: coding agents are writing more of the code, test agents are now generating and running more of the tests by default, and every stage downstream: deployment, security, cost, and resilience has to absorb that pace without falling over.

As organizations ship software faster than ever, runtime behavior changes are becoming just as frequent as code releases. Teams need a way to update application behavior without waiting for code deployments while maintaining visibility, governance, and control. ‍ Now available in beta, Config Management provides a governed runtime control plane that separates runtime configuration from application deployments, enabling organizations to deliver configuration changes instantly across environments.