AI workloads deliver disproportionate value - but their cost profile is fundamentally more challenging to manage than your traditional cloud spend:

Employees across engineering, support, sales, and ops are onboarding to AI faster than any technology in the last decade, unlocking net-new use cases week over week. But that adoption brings a management challenge: foundation model usage now spans dozens of teams, hundreds of users, and thousands of agents with a shifting mix of providers and model tiers. Spend controls need to apply uniformly across all AI workloads, so your organization can confidently lean into AI without worrying about surprises on the bill.

While spend controls need to apply uniformly, different parts of your organization need different cost controls. A platform team cares about workspace-wide totals. A FinOps lead cares about the org-level monthly burn. An engineering manager cares about per-developer experimentation budgets. AI Spend Controls let you set them all from one place and is deeply integrated with Databricks’ existing budgets:

The Cost section of your account console lets you respond to budget alert emails or proactively monitor the status of your live budgets. On the Budgets page, you see at a glance how your budgets are trending: