What an AI MVP Actually Costs in 2026

Ask three AI vendors what an MVP costs and you'll get three different numbers, usually with more hedging than math behind them. Some of that variance is real — a single AI feature bolted onto an existing product and a multi-tenant platform with provable data isolation are not the same job. Here's what the number actually depends on, with real anchors instead of vague ranges.

The short answer

A single AI feature added to a product you already run starts from $5,000 and ships in 2–4 weeks. A complete AI MVP — model integration, retrieval, a working front end, a user in front of it — starts from $5,000 and ships in 4 weeks. A multi-tenant platform built to survive a compliance review starts from $13,000 and takes 6–10 weeks. These are floors, not averages: the figure moves up from there depending on scope, never down.

Typical price tiers

• Website or portal, no AI in scope: from $3,000, 1–3 weeks — a marketing site or a client-facing portal.
• Integration, connecting an AI system to what you already run: from $3,500, 2–4 weeks.
• AI feature add-on: from $5,000, 2–4 weeks — one model-backed feature shipped into an existing product.
• AI MVP build: from $5,000, 4 weeks — a new AI product end to end: retrieval, model integration, a usable interface.
• Custom platform: from $13,000, 6–10 weeks — multi-tenant, with the isolation and audit guarantees a regulated buyer will actually ask for.

What actually drives the cost

Five things move the price more than anything else: how much of your data is clean enough to retrieve against, how many existing systems the AI layer needs to read from or write to, whether you need provable tenant isolation and an audit trail — a regulated buyer's requirement, not a nice-to-have — how much of the surrounding product (auth, billing, admin) already exists versus needs building, and how much ongoing change you expect after launch. A single feature reading from data you already have is fast and cheap. A platform that has to prove to an auditor that one tenant can never see another's data is a different job.

Why the cheapest quote usually costs more

The lowest quote in your inbox is usually missing something, not doing the same work for less. A cut-rate AI build commonly skips the parts that make output trustworthy: no check that the model's numbers are actually grounded in your data, no server-side citation construction, no isolation tests. That gap doesn't show up in the demo — it shows up the first time the model invents a figure in front of a customer, or an auditor asks how a decision was made and nobody can answer. Fixing that after launch costs more than building it correctly the first time.

How we keep the estimate honest

Two senior architects scope and review every engagement personally — there is no bench of juniors the estimate is quietly built on. AI-assisted development is part of how we move at this speed, and every deviation from the agreed scope still becomes a numbered ADR citing exactly what changed and why, so the speed doesn't come at the cost of a record you can audit. You own your code and your data on delivery, so there's no lock-in inflating the real cost later.