AI Development Cost: Build, Evaluation and Operating Budget

“Add AI” is not an estimable requirement. “Prepare a draft answer from approved support documents, show the supporting source and route unresolved questions to a person” is a more useful starting point.

Specify the inputs, allowed outputs and completion condition. Identify whether the system advises a user, drafts a record or changes an external system. Define which actions require approval and which results should be rejected or escalated.

The delivery scope changes when the system must handle scanned files, reconcile extracted data or update a business record. A demonstration on a few clean examples does not establish that the complete task is ready for production.

Ask suppliers to distinguish known work from unresolved dependencies. If the source data has not been inspected, record that limitation in the estimate. If another system has no confirmed interface, include discovery for that dependency before presenting integration as a fixed, fully understood line item.

Consider a fictional internal assistant that retrieves approved business documents and drafts answers for staff. It has one source collection, one sign-in integration and no authority to modify customer records.

At an assumed rate of INR 2,000 per hour, 360 × 2,000 = INR 720,000, or INR 7.2 lakh. An illustrative 20% contingency adds INR 144,000, producing a modelled initial budget of INR 864,000, or INR 8.64 lakh, before taxes and separately priced services.

Replace the effort, rate and contingency with a reviewed project estimate. A different task, source format or integration can change the scope. Training a model, introducing multiple data sources or automating consequential actions is not included in this example.

For text-generation services billed by input and output tokens, a starting calculation is:

Model-call cost = input tokens ÷ billing unit × input rate + output tokens ÷ billing unit × output rate.

Then account for every call made by the workflow, including evaluation runs and retries where charged. Other modalities or services can use different billing units. Amazon Bedrock's pricing page illustrates why a budget must identify the specific model, service and region rather than use one universal AI rate. Consult the current provider terms for your actual design.

Keep currency and billing units consistent. Add applicable storage, retrieval, infrastructure and external-service charges separately. Do not assume a quoted model rate includes the whole application.

A lower cost per model request may not reduce the cost of the workflow if it creates more failed attempts or manual correction.

For an illustrative operating period, suppose model and platform services cost INR 12,000. Staff review and correction cost another INR 3,000 under the business's own labour-cost assumptions. If 3,000 tasks reach the agreed completion standard, the calculation is:

(12,000 + 3,000) ÷ 3,000 = INR 5 per completed task.

This is an invented scenario, not an observed performance result. Define completion before calculating the denominator. Exclude unresolved tasks from the completed count, but keep the cost of attempts that failed in the numerator. Track the failure reasons so the team can see whether a change improved the workflow or only moved effort to reviewers.

Set aside work to build an evaluation set with representative inputs and difficult cases. Record expected behaviour when an answer is unavailable, ambiguous or outside the permitted scope. Decide who approves a release and what happens if performance regresses.

After launch, source documents, user behaviour and external services may change. Assign responsibility for checking data updates, monitoring failures and reviewing material changes. Keep the evaluation and support arrangement visible in the commercial scope.

For related implementation discussion, see Drema's AI development service. The existing LLM operating-cost guide addresses cost-control techniques; this article's purpose is the broader project budget.

Can we estimate an AI project before inspecting the data?

You can create a provisional range with explicit assumptions. Data inspection and dependency discovery should resolve the uncertainties that materially affect a committed proposal.

Is a smaller model always the cheaper choice?

Compare the complete task. A design that needs more attempts or correction can have a higher operating cost even when its per-call rate is lower. Test quality and workflow cost together.

What should an AI development quote include?

Request the task boundary, workstreams, assumptions, acceptance method, recurring services, review responsibilities, ownership and support arrangement. Ask which unresolved inputs can change the estimate.

Bring sample inputs you are authorised to share, the desired outcome, the systems involved and the current process cost. State which errors require escalation. These inputs let a supplier explain a scoped delivery and operating budget rather than an unsupported price for “AI”.