Budgeting AI Infrastructure

AI infrastructure budgets should include much more than the model API bill.

How the estimate is calculated

Model inference is often the most visible expense because providers charge for tokens, but production AI applications can also require application servers, databases, vector storage, search systems, queues, file storage, observability, analytics, and third-party tools.

Start by estimating model consumption.

What can change the total

Calculate:

Users × requests per user × tokens per request × token price

Plan with a realistic usage example

Separate input and output usage because they commonly have different prices.

Then add infrastructure.

Practical checks before you proceed

A retrieval-based application may need document processing, embeddings, vector storage, and database queries. An agent may generate many model and tool calls for a single user action.

Also budget for failed requests and retries. If each user action triggers an average of 1.2 model calls rather than exactly one, your financial model should reflect that.

Practical checks before you proceed

Create three forecasts:

Baseline: expected normal usage.

Practical checks before you proceed

Growth: traffic increases faster than expected.

Stress: unusually high usage or inefficient AI behavior.

Practical checks before you proceed

Set spending alerts before launch and record token usage by customer, endpoint, and feature.

This makes it possible to identify features with poor unit economics.

Practical checks before you proceed

Finally, calculate revenue relative to AI cost.

If a customer pays $20 per month but regularly consumes $18 of AI infrastructure, the product may have an unsustainable gross margin.

Practical checks before you proceed

Good AI budgeting therefore connects technical metrics such as tokens and requests to business metrics such as customer acquisition, retention, pricing, and gross margin.