The hidden costs of AI adoption (and how to budget for them)

When teams budget for AI features, they almost always budget for inference. That's the smallest line item we've seen on a real production AI workload. Here are the four costs that quietly dominate, and how to plan for them.

1. Evaluation infrastructure

You can't improve what you can't measure. Real evaluation means a curated test set, a way to score model output (often with another model), regression tracking across releases, and — critically — humans labelling enough samples to keep the test set honest as your data drifts. Plan for an eval engineer or eval-eng-equivalent week as a permanent cost, not a one-off.

2. Human-in-the-loop review

For any AI workflow with non-trivial blast radius, human review is part of the system, not an afterthought. That includes the reviewer UI, the queue, the sampling strategy, the SLA on review turnaround, and the cost of the reviewers themselves.

If your AI feature is good enough to ship, it's good enough to deserve a human-review budget.

3. Vector storage and retrieval

RAG demos hide the operational reality. At enterprise scale, you'll be running a vector DB, an embedding pipeline, a chunking strategy that needs tuning, re-indexing on document churn, and (often) a re-ranker. Each of those is a compute and headcount line.

4. Observability and incident response

AI systems fail in ways traditional systems don't. You'll need prompt/response logging with PII redaction, model-version pinning, drift detection, and on-call rotation that understands what to do when a model starts misbehaving at 2am. None of this comes for free.

The honest budget

For a non-trivial enterprise AI feature, we'd typically expect inference itself to be 15-25% of total cost. Eval, review, retrieval, and observability often each rival or exceed it. Plan accordingly — and your CFO won't be surprised six months in.