How Much Does AI Consulting Cost?

Q: What drives the price of an AI engagement?

- Scope breadth. One use case or several, one system or many. Each added use case is its own proof. - Data readiness. Clean, accessible, labeled data is quick to use. Data that must be found, cleaned or licensed before anything can be tested is a frequent source of delay. - Integration depth. Reading from a system is usually cheaper than writing back to it, and a system with no clean interface costs more than either. - Verification. Independent checking of the running system adds time and a second senior person. A quote that leaves it out is not the same product. - Risk and compliance context. Access controls, audit evidence and review steps add work wherever the output feeds a regulated decision. - Run environment. Expected usage, latency targets and hosting decide the recurring cost. - Team and duration. Seniority, calendar time and, in a retainer, the size of the pod.

Q: What is and is not included in the price?

- Credits. The AI Compass and AI Rescue fees are credited toward a subsequent build, on the terms in the engagement letter. - Third-party costs. On the AI Production Build and Your AI Team, model usage, third-party licenses and cloud infrastructure are separate: billed at cost, or paid directly by you. - Verification. Every engagement that builds something ends with independent verification on the running system. The person who builds it does not sign it off. - Proof. Under NDA we do not name clients. Past work is described by sector on the Work page, and the method is on How we work.

Q: Where should you start, and what does the first step cost?

- Several ideas competing, no clear first move: the free call, or AI in a Day from $2,500. - One use case and a budget owner who needs a go or no-go on your own data: POC in 3 Weeks from $15,000. How to read one is in what an AI proof of concept should prove; the rate to ask a vendor for is in what a good POC-to-production rate looks like. - A pilot that stalled before production: AI Rescue from $25,000, with a real off-ramp after week one. - A validated use case that now has to run in production: AI Production Build from $40,000, phased. - How to measure whether it paid off: measuring AI ROI in production.

Last updated: September 2026. Prices are the starting prices published on the engagements page on 2026-09-29. Every external figure is attributed and dated in the text.

In short: AI consulting has no standard price, because the phrase covers stages that differ in scope by an order of magnitude. Ask which stage you are buying. NewGenApps publishes starting prices for six: AI in a Day from $2,500, AI Compass from $5,000, POC in 3 Weeks from $15,000, AI Rescue from $25,000, AI Production Build from $40,000, and an embedded team from $10,000 a month. Gartner has estimated that CIOs who do not understand how their generative AI costs scale could make a 500% to 1,000% error in their cost calculations (Gartner, 2024-10-21; also reported by CIO Dive), so buy one stage at a time, each with its own exit.

How much does AI consulting cost?

There is no market-standard price to quote. In preparing this page we looked for an independent, non-vendor benchmark of AI consulting fees and found none. The fee guides we found were published by firms that sell the service, and we have not repeated their figures.

The nearest external evidence comes from the buyer's side. According to Gartner, as reported by CIO Dive from its 2024 IT Symposium keynote, organizations that deployed AI spent between $300,000 and $2.9 million in 2023 on the proof-of-concept phase alone. That is total organizational spend on the phase, not consulting fees, but it is still a spread of nearly ten to one for the same named phase. The likeliest explanation is scope rather than brand: which data, how many systems, what has to be proven, and who verifies it. So compare what two prices buy, not the two prices.

The price ladder: six engagements, six starting prices

Prices are in US dollars and shown as "from" figures, because the fee is set against a scoped engagement after a free 30-minute call. Source: the engagements page, as published 2026-09-29.

Engagement	The question it answers	Duration	Starting price
AI in a Day	Where does AI pay off for us, and what do we do first?	One working day	from $2,500
AI Compass	What is the costed, ranked roadmap, and is our data ready?	Typically 2 to 6 weeks	from $5,000, credited toward the build
POC in 3 Weeks	On our real data, does this clear the quality bar at an acceptable cost and latency?	3 weeks	from $15,000
AI Rescue	Can this stalled pilot be recovered, re-scoped or retired?	About 90 days	from $25,000, credited toward the build
AI Production Build	Can this validated use case run in production, independently verified?	Phased, milestone-gated	from $40,000
Your AI Team	Can we have a durable senior AI capability without hiring it ourselves?	Month to month after an initial term of typically three months	from $10,000 per month for the smallest pod

The 30-minute working session costs nothing: book it here.

How are AI consulting engagements priced?

Quotes usually take one of four forms, and they differ mainly in who carries the risk of an overrun. A fixed-scope fee gives the buyer a known price for a written deliverable and puts overrun risk on the vendor for that scope only. Time and materials bills the hours worked, which puts the risk on the buyer. A monthly retainer buys a standing team's capacity. An outcome-linked fee ties part of the price to a measured result, and is only as good as the baseline agreed before the work starts.

NewGenApps' six published engagements are five fixed-scope or phased stages and one monthly retainer. The production build is priced in phases, with milestone gates, because its scope is easiest to bound once a proof has shown what has to be built.

Pricing structures compared

Our working definitions, from the buyer's side of the table:

Structure	You pay for	Who carries an overrun	Fits best	Watch for
Fixed scope, per stage	A written deliverable at a set price	The vendor, for the scope written down	Decisions and proofs: an audit, a POC, a triage	Anything outside the scope is a change request; read the change-control clause
Time and materials	Hours worked at a rate	The buyer	Exploratory work with no defined end	A budget that is a forecast, not a commitment, and no built-in exit
Monthly retainer	A standing team's capacity	Shared: you pay for capacity, the vendor answers for productivity	Continuous delivery and operation	A vague backlog; ask for a monthly review and a defined exit
Outcome-linked fee	Part of the price tied to a measured result	Shared, by formula	Work with a measurable baseline	No agreed baseline, so the fee formula becomes the argument

Why do AI project budgets slip?

Gartner's public statements point at the same place from three angles. In July 2024 it predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value. That is a forecast, not a measured rate. Rita Sallam of Gartner said "costs aren't as predictable as other technologies." In October 2024 Gartner estimated that CIOs who do not understand how their generative AI costs scale could make a 500% to 1,000% error in their cost calculations (Gartner, 2024-10-21; also reported by CIO Dive). And in a March 2026 report Gartner predicted that through 2028 at least 50% of GenAI projects will overrun their budgeted costs, because of poor architectural choices and a lack of operational know-how (reported by Campus Technology, 2026-06-22).

Notice what the last prediction blames: how the system is designed and run, not the model. That is the reason to price the stages separately. A proof runs for weeks at a fixed scope. A build runs longer and is phased. Running the system is a third cost, recurring and driven by usage. The FinOps Foundation's State of FinOps 2026 found that 98% of the 1,192 FinOps practitioners it surveyed now manage AI spend, up from 31% two years earlier (2026-02-19). Ask every quote for the build fee and the run cost as separate lines.

These are analyst predictions and press-reported figures, not audited measurements. Read them as direction, not as a forecast for your project.

What drives the price of an AI engagement?

Scope breadth. One use case or several, one system or many. Each added use case is its own proof.
Data readiness. Clean, accessible, labeled data is quick to use. Data that must be found, cleaned or licensed before anything can be tested is a frequent source of delay.
Integration depth. Reading from a system is usually cheaper than writing back to it, and a system with no clean interface costs more than either.
Verification. Independent checking of the running system adds time and a second senior person. A quote that leaves it out is not the same product.
Risk and compliance context. Access controls, audit evidence and review steps add work wherever the output feeds a regulated decision.
Run environment. Expected usage, latency targets and hosting decide the recurring cost.
Team and duration. Seniority, calendar time and, in a retainer, the size of the pod.

What is and is not included in the price?

Credits. The AI Compass and AI Rescue fees are credited toward a subsequent build, on the terms in the engagement letter.
Third-party costs. On the AI Production Build and Your AI Team, model usage, third-party licenses and cloud infrastructure are separate: billed at cost, or paid directly by you.
Verification. Every engagement that builds something ends with independent verification on the running system. The person who builds it does not sign it off.
Proof. Under NDA we do not name clients. Past work is described by sector on the Work page, and the method is on How we work.

How do you compare two AI consulting quotes?

Put both quotes through the same seven checks before you compare the totals.

Name the stage. Is each quote for a decision, a build or an operating team? Do not compare a proof with a production build.
Get the deliverables in writing. A working system, an evaluation report, the code, an architecture document: each named.
Ask for the acceptance criteria. What result makes each deliverable done, and who decides?
Ask what happens on "do not build". A proof that can only end in "build" is a sales step, not a test. A reasoned no should count as a successful outcome.
Split build from run. Ask for the recurring cost at your expected volume, not the pilot's.
Ask who verifies. The person who builds the system should not be the only person who signs it off.
Read the exclusions and the change clause. What is out of scope, and what does a change cost?

The same checks apply when you choose an AI partner more broadly, and to any build-versus-partner decision.

Is the first conversation free?

Yes. The 30-minute working session costs nothing, with no deck and no pitch. You leave with a view of where AI pays off and what a first proof would take. Book it here.

Are the starting prices fixed quotes?

No. They are floors, not quotes. The fee is set against a scoped engagement after the call, so you see the deliverable and the price before you commit. Larger builds and larger pods cost more, and the tier is confirmed at scoping.

Where should you start, and what does the first step cost?

Several ideas competing, no clear first move: the free call, or AI in a Day from $2,500.
One use case and a budget owner who needs a go or no-go on your own data: POC in 3 Weeks from $15,000. How to read one is in what an AI proof of concept should prove; the rate to ask a vendor for is in what a good POC-to-production rate looks like.
A pilot that stalled before production: AI Rescue from $25,000, with a real off-ramp after week one.
A validated use case that now has to run in production: AI Production Build from $40,000, phased.
How to measure whether it paid off: measuring AI ROI in production.