FinOps for AI Data Infrastructure: Optimising the Cost of Cloud Analytics and Agentic AI Workloads
A few months back, I was pulled into a call that started with someone staring at a Databricks bill and asking, quite genuinely, "did we get hacked?" We hadn't. What had happened was far more mundane and, honestly, far more common: a retrieval-augmented pipeline feeding a set of agents had quietly tripled its token consumption over three weeks because a prompt template got slightly more verbose and a retry loop wasn't capped properly. Nobody noticed until finance did.
That call is, in miniature, the reason FinOps for AI infrastructure has stopped being a nice-to-have conversation and become something closer to a survival skill. Cloud analytics costs were already messy enough with sprawling warehouses, unused clusters, and copy-paste ETL jobs nobody owns. Add agentic AI into the mix, where a single user request can spawn a chain of LLM calls, tool invocations, vector searches, and sub-agent delegations, and the cost surface...
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