AI App Development Cost: A 2026 Cost Breakdown

An AI app costs less because of "AI" and more because of everything around it: how clean the underlying data is, which model you actually need, how many systems it has to talk to, and whether a regulator gets a vote. Most builds land in **$15,000 to $120,000+** depending on those four variables, over a **1-3 month** small-project window or 3-12 months for anything with real integration surface.

That range is wide on purpose. A fixed number before anyone has looked at your data or your compliance posture is a guess dressed up as a quote. Here is what actually moves the price, what a realistic build looks like at three scopes, and why we bill by the month instead of by the feature.

## The four things that actually set the price

"AI app" is not a price tier. A chatbot wrapper over a stable FAQ and a diagnostic model trained on a hospital's imaging data are both "AI apps," and they are not in the same universe of cost. Four variables do the real work.

**Data readiness.** If your data lives in one clean Postgres table with a documented schema, a model or pipeline can be built against it in days. If it is scattered across three CRMs, a spreadsheet, and a legacy system with no API, most of the budget goes to extraction, cleaning, and reconciliation before any model touches it. We saw this directly on a campaign-intelligence build: turning raw statewide voter files and federal contribution data into something a model could score meant building the ETL and verification layer first, the ML scoring second.

**Model choice.** Calling an existing API (GPT-4o, Claude, an embeddings endpoint) is cheap to integrate and fast to ship. Fine-tuning, training a custom model, or running inference offline for compliance reasons costs more in engineering time, not because the model is exotic but because you now own evaluation, versioning, and failure modes that an API call hides from you. A public-sector spend auditor we built for needed local models and zero external calls for compliance reasons; that constraint, not the fraud-detection logic itself, shaped a large share of the engineering effort.

**Integration surface.** A model behind a single form is a small project. A model that has to read from your EHR, write back to a billing system, respect role-based access, and show up inside an existing provider workflow is a large one. The developmental-dentistry platform we built shipped voice-to-chart transcription and radiograph analysis, but the bulk of the 80+ endpoints and 30+ provider-facing pages existed to wire that AI layer into scheduling, billing, and claims, not to run the model itself.

**Compliance requirements.** HIPAA, SOC 2 expectations, or public-sector data-residency rules do not change what the model does. They change what has to surround it: audit logging, consent flows, access control, and in some cases an air-gapped deployment with no external calls at all. The compounding-pharmacy platform we shipped carries a seven-year immutable audit log and 490+ unit tests specifically because a missed failover in that domain is a liability, not a bug to patch later. Read more on what that actually requires in our [HIPAA-compliant software guide](/blog/hipaa-compliant-software).

None of these four show up in a one-line price quote. That is the tell that the quote is a guess.

## Why a fixed price is usually a guess

Ask a vendor for a fixed price on an AI app before they have seen your data, your systems, or your compliance requirements, and you are asking them to price all four variables above sight unseen. They will pick a number, pad it for the unknowns, and then bill you separately when reality does not match the guess. That is what a change order is: an agency charging you for having priced the unknown wrong the first time.

The honest version of that conversation is capacity, not a quote. You buy a pod for a month at a fixed rate, the pod builds against your actual data and your actual constraints, and the roadmap adjusts as those constraints surface, without a change-order negotiation every time reality diverges from the original guess. That's the model behind our [pricing](/pricing): month-to-month, no per-hour billing, 30 days' cancellation notice either way.

## What each scope actually costs, mapped to a pod

Here is how the three common scopes map to pod tier, monthly cost, and realistic timeline, next to the industry's rough MVP-timeline consensus so you can sanity-check the numbers against what you've read elsewhere.

| Scope | Pod tier | Monthly cost | Typical duration | Total range |
|---|---|---|---|---|
| MVP / single build track (one model, one integration, no regulatory load) | Builder Pod | $5,000/mo | 1-3 months | $5,000-$15,000 |
| Medium build (two concurrent tracks, multiple integrations, moderate compliance) | Growth Pod | $10,000/mo | 3-6 months | $30,000-$60,000 |
| Enterprise build (3+ parallel tracks, audit-grade compliance, org-wide rollout) | Enterprise Pod | Custom | 6-12 months | Custom, scoped to tracks |

For context, the industry-wide consensus across dev-shop benchmarks puts a typical MVP build at roughly 3-4 months, with lean MVPs landing at 8-12 weeks and complex healthtech- or fintech-grade builds stretching 6-9 months (see, for example, [Netguru's MVP timeline breakdown](https://www.netguru.com/blog/mvp-timeline)). Our own small-project window, 1-3 months, sits at the fast end of that range because we start inside your repository within five business days and ship the first working piece in week one or two, not after a discovery phase. See how that startup sequence works on [how it works](/how-it-works) and how each pod is staffed on [pods](/pods).

## A worked example: MVP-scope AI assistant

Say you need an AI assistant that answers customer questions against your existing documentation, logs every conversation for review, and hands off to a human when confidence is low. One build track, one model call (an existing LLM API, not a custom model), one integration point (your support platform).

That is a **Builder Pod**, $5,000/month, one active build track, a pod lead plus a two-engineer bench.

If the build takes the low end of a small project, one month: **$5,000 total**, prototype included, before any commitment beyond that first month.

If it takes the full small-project window, three months, because the handoff logic and logging need more iteration than expected: **3 x $5,000 = $15,000 total**, still month-to-month, still cancellable with 30 days' notice at any point.

Compare that to hiring a mid-level AI engineer to build the same thing in-house: $120,000-$160,000 a year in base salary alone, before benefits, recruiting, or the 3-6 months it typically takes to close the hire. You would still be interviewing candidates by the time the Builder Pod version has shipped and been in production for two months. That comparison holds even before you count what a fully senior AI/ML hire runs, which is $250,000 or more a year fully loaded. Our [ai engineer cost breakdown](/blog/ai-engineer-cost-2026-hire-vs-pod) walks the hire-vs-pod math in more detail if you're weighing that tradeoff directly, and [build pod vs in-house hire](/blog/build-pod-vs-in-house-hire) covers the same question from the org-design angle.

## What drives cost past the MVP scope

The jump from Builder to Growth Pod is not about the AI getting more complicated, usually. It is about the surface area growing. Two build tracks running at once (say, the assistant plus an internal admin dashboard to review flagged conversations) needs a third engineer on the bench and a bi-weekly strategy call to keep both tracks aligned with the roadmap, which is exactly what the Growth Pod adds at $10,000/month.

Compliance is the other lever, and it moves cost independent of model complexity. A healthcare-facing assistant that touches protected health information needs a signed Business Associate Agreement and HIPAA-aligned controls: encryption at rest and in transit, role-based access, audit logging, and a documented incident response path. We sign BAAs on request and have shipped two HIPAA-aligned platforms end to end; read our [security posture](/security) for what that covers. None of that is model cost. It is engineering time spent on the surrounding system, and it is the same reason the compounding-pharmacy build carried 490+ unit tests and a seven-year audit log rather than a bigger model.

## De-risking the estimate before you commit

The honest way to answer "what will this cost" before signing anything is to see the thing built, at least in prototype form, against your actual requirements. We build a free, clickable prototype in the first session, before any pod starts and before any money changes hands. If you walk away after seeing it, you keep the prototype. If you continue, the pod starts within five business days and ships weekly from there. That single step turns "what will an AI assistant cost" from a guess into a scoped decision, because you are pricing a build you have already seen, not a category. Our [AI MVP development services](/blog/ai-mvp-development-services) page covers what that prototype-to-pod path looks like in practice.

## The short version

- Cost is driven by data readiness, model choice, integration surface, and compliance load, not by "AI" as a category.
- Small builds run 1-3 months, medium builds 3-12 months, past a year is rare; cost tracks time directly because pods bill monthly, not per feature.
- A Builder Pod ($5,000/mo) covers an MVP-scope build, a Growth Pod ($10,000/mo) covers a medium build with two tracks, Enterprise is custom for three-plus parallel tracks.
- A fixed price quoted before anyone has seen your data or compliance requirements is a guess; buying monthly capacity lets the scope adjust to reality instead of generating change orders.
- A free clickable prototype, built before any commitment, is the fastest way to turn that guess into an actual number.