Buyer's guide

What AI implementation actually costs.

Enterprise AI implementation is usually priced in two stages: a fixed-scope proof of value over 4–8 weeks, then a production build over 3–6 months. Cost is driven by data access, integration surface, and compliance scope far more than by the model itself. Ongoing running cost — inference, monitoring, and change — is typically a small fraction of the build.

Pricing for enterprise AI work varies by an order of magnitude for the same scope. This guide explains what drives the range, how engagements are structured, and how to compare quotes on equal terms.

Proof of value: 4–8 weeks, fixed scope, a small senior team. Produces a working system against real data plus an evaluation harness.
Production build: Typically 3–6 months per use case, depending on data access, integration surface, and security review.
Biggest cost driver: Integration and data access, not model choice. Systems that need many upstream connections cost more than systems with clean data.
Running cost: Inference, observability and monitoring. Usually far smaller than the build, and reducible through model routing and caching.

How we keep cost predictable

Scope the smallest useful system

Cost control starts with scope. One use case, one data source, one measurable outcome — expanded only after the first version proves itself in production.

Price the first phase fixed

A fixed-scope first phase caps your exposure and gives both sides a clean exit. Time-and-materials before anything is built transfers all risk to the buyer.

Budget for evaluation and security

Evaluation harnesses, PII handling, audit logging and security review are not optional extras in regulated environments. Programs that omit them from the budget stall before launch.

Plan the run cost from day one

Model routing, caching, and choosing a smaller model where it performs equally keep inference cost predictable as usage grows.

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What drives the cost of an AI implementation?

Data access is the largest and most underestimated driver. If the data a use case needs is spread across systems with no clean interface, most of the engagement is integration work rather than AI work. Teams with a usable data layer routinely ship the same use case in half the time.

Integration surface is the second driver. A system that reads from one source and writes to one destination is straightforward. A system that must sit inside an existing workflow, respect permissions, and write back to systems of record is a much larger build.

Compliance scope is the third. HIPAA, PCI-DSS and internal model-risk frameworks add PII handling, audit logging, review cycles and documentation. This is real work and should be budgeted, not discovered late.

Model choice matters least. Frontier model pricing has fallen consistently, and most production systems route between models by task. Choosing the right model saves money at runtime; it rarely changes the shape of the build.

Why do quotes for the same AI project vary so much?

Staffing model explains most of the variance. A pyramid-staffed engagement bills many junior consultants under a small number of senior partners; a specialist firm bills a small senior team. The headcount difference, not the scope difference, produces multiples in price.

Scope definition explains most of the rest. A quote covering discovery, prototype, production, change management and a year of managed service is not comparable to a quote for a production build. Ask every bidder to price the same fixed first phase so the numbers can be compared.

Watch for quotes that exclude evaluation, security review, or handover. They look cheaper and become expensive later, usually as a change order.

How do you avoid overpaying for AI consulting?

Buy a small fixed-scope first phase before committing to a program. It costs a fraction of a full engagement and tells you more about the partner than any procurement process.

Insist on named senior engineers with a stated allocation. You are paying for the people who write the code; make sure they are the people on the invoice.

Require handover as a deliverable, including documentation and runbooks, so you are not structurally dependent on the vendor for routine change.

Measure the system against a defined business metric from the first phase. Programs without a metric expand indefinitely because nobody can say when they are done.

Frequently asked questions

How much does an AI proof of value cost?

A focused 4–8 week proof of value with a small senior team is typically priced in the tens of thousands of dollars. It should deliver a working system against your real data, an evaluation harness, and a costed plan for production.

How much does a production AI implementation cost?

Production implementations are typically 3–6 month engagements per use case. The range depends mostly on data access, integration surface, and compliance scope rather than on the model or the use case category.

What are the ongoing costs after launch?

Inference costs, observability and monitoring, and the engineering time to handle change. For most enterprise systems this is a small fraction of the build cost, and it can be reduced substantially with model routing and caching.

Is a fixed price or time-and-materials better?

Fixed price for the first phase, because it caps risk while both sides learn. Increment-based pricing for production, because production scope legitimately changes as the system meets real users.