AI MVP development company
From $12,000AI MVP development that proves one thing, properly.
We build first versions of AI products for founders and teams: one core loop real users can finish, on a stack you keep. The AI step gets tested on real inputs before we build a single screen, so if the idea doesn't work, you find out before the budget goes into screens.
By Axit · Updated Sep 24, 2026 · Prices from 15 published sources, all linked
Type your idea. Get the smallest version worth building. What to cut, what to test first, and what it would cost to build and to run. The model writes the scope; the prices come from published sources, not the model.
Describe your idea. You get the smallest version worth building, what to cut, how to test the risky part first, and what it would cost to build and to run.
The short answer
An AI MVP development company builds the smallest real version of a product whose value depends on an AI model, tests that model on real inputs first, and puts one complete loop in front of real users. Studios that publish prices start around $15k, cost guides quote $15k to $40k for a lean AI MVP, and the AI itself usually costs cents per user per month.
- Our starting price
- $12,000
- scope sets the quote
- Published studio median
- $15k
- 7 studios
- Lean AI MVP, guides
- $15k–40k
- 8 cost guides
- AI cost per user
- $0.023–$1.56
- per month, 60 actions
Definition
What does an AI MVP development company do?
It cuts an idea down to one core loop, builds the smallest real version on a stack you can keep, and puts it in front of real users. For an AI MVP there's one more job: proving the model is good enough, fast enough and cheap enough before the rest gets built, because most AI MVPs fail on the AI, not the software.
Scope that fits
One core loop the user can complete end to end. Everything else goes on a later list, in writing.
Working product
Auth, the core AI feature, the data model and a UI people can actually use, deployed where you control it.
Cost and quality guardrails
Per-user model spend limits, logging of every AI call, and a small evaluation set for the core feature.
Clean handover
Your repo, your cloud accounts, a README a new developer can follow, and a call walking through the codebase.
Honest comparison
Prototype, proof of concept or MVP: which do you need?
They get used interchangeably, and they shouldn't. Each one answers a different question, and paying for the wrong one is the most common way to waste an MVP budget.
| Goal | Who uses it | What's real | Typical cost | Time | |
|---|---|---|---|---|---|
| Prototype | Show how it would look and feel | Investors, a few test users | Mostly faked | $1k to $8k | Days to 2 weeks |
| Proof of concept | Prove the hard part is possible (usually the AI) | Your team | The core step only | $3k to $10k | 1 to 3 weeks |
| MVP | Learn whether real users want it and will pay | Real users | One complete loop, end to end | $15k to $40k for a lean AI MVP | 4 to 8 weeks |
Lovable, Bolt, Cursor
Build it yourself with AI tools, or hire a team?
It's a sequencing decision, not either-or. AI app builders are the fastest way to find out whether anyone cares; our guide to AI app builders covers which one fits what. Hire when the answer is yes and the thing has to hold up.
| Your situation | Better choice |
|---|---|
| You want to see if anyone cares, and you can live with rough edges | Build it yourself with Lovable, Bolt or Cursor |
| You built it with AI tools and people are using it | Harden it: security, data model, tests, a deploy you control |
| The AI step is the product and it must be right most of the time | Hire people who evaluate models for a living |
| It handles money, health or personal data | Hire a team; don't ship vibe-coded auth |
| You need it maintained while you sell | A studio or an in-house hire, not a one-off freelancer |
Already built it in Lovable, Bolt or Cursor?
Usually we finish it rather than rebuild it. Hardening costs a fraction of a rebuild when the data model is sound. Any of these means it needs work before real users:
- Secrets or API keys anywhere in the browser code
- Auth you didn't configure yourself, or none
- Database rows any logged-in user can read
- No tests on the AI step, so nobody knows when it gets worse
- No cap on model spend per user
- The code only runs inside the builder's platform
Can ChatGPT build me an app?
It can write much of the code for a simple one, and tools built on these models can deploy it. What it won't do on its own is decide what to leave out, lock down your data, test the AI step on real inputs, or keep it working when the model underneath changes. That's the part you're paying a team for.
We build with AI coding agents ourselves; here's how.
Sourced · dated Sep 24, 2026
What an AI MVP costs in 2026
Market ranges, reconciled from SFAI Labs, UZO LAB, HouseofMVPs, Inventiple and Perplexity's synthesis of 19 guides, fetched 2026-09-24. Scope moves the number more than anything else, then how many AI features and integrations it needs.
| Tier | What it is | Build cost | Time | AI and hosting |
|---|---|---|---|---|
| AI prototype | One AI workflow and a few screens, to test the idea with a handful of users. | $2k to $15k | 1 to 4 weeks | $10 to $100/mo |
| Lean AI MVP | Login, a database, one core loop and the AI feature it depends on, for real users. | $15k to $40k | 4 to 8 weeks | $50 to $200/mo |
| Production AI MVP | Several AI features, integrations, payments or roles, and the monitoring a paying customer expects. | $40k to $120k | 8 to 16 weeks | $200 to $800/mo |
Why quotes range from $1,450 to $70,000
Studios that publish fixed prices charge a median of $15k for an MVP with login and a database. Cost guides quote two to five times that for the same thing. Both are real: fixed-price studios deliver a defined scope with AI coding agents, and guides are mostly written by dev shops describing custom enterprise work. Ask any quote which of the two you're buying.
Telliant (service FAQ) (published)
What pushes the price up, or down
Unit economics · prices pulled Sep 24, 2026
What the AI costs to run, per user
The number no cost guide gives you. One “AI action” here is 3,000 input and 700 output tokens, and an active user does 60 a month. The spread between the cheapest and the most capable model is about seventy times, which is why model choice decides whether a $10 product has a margin. Agent-style features multiply the calls per action.
| Model | Per 1M tokens in / out | Per action | Per active user / month |
|---|---|---|---|
| DeepSeek V4 Flash | $0.089 / $0.177 | $0.0004 | $0.023 |
| Gemini 3.1 Flash Lite | $0.25 / $1.5 | $0.0018 | $0.108 |
| GPT-5 mini | $0.25 / $2 | $0.0022 | $0.129 |
| Claude Haiku 4.5 | $1 / $5 | $0.0065 | $0.390 |
| Claude Sonnet 5 | $2 / $10 | $0.013 | $0.780 |
| GPT-5.4 | $2.5 / $15 | $0.018 | $1.08 |
| Claude Opus 5.5 | $4 / $20 | $0.026 | $1.56 |
List prices from the OpenRouter model list on Sep 24, 2026. Providers change prices often; check before you budget. See also Claude pricing and OpenAI API pricing.
Anatomy of a build
How we build an AI MVP, in four steps
- 01
Cut scope
One core loop a user can finish end to end. Everything else goes on a later list, in writing.
- 02
Prove the AI first
Before any screens, the model runs on 50 real inputs against a pass bar agreed up front. If it misses, we stop here.
- 03
Build the product
Short cycles with a working build you can click through throughout, not a reveal at the end.
- 04
Launch and hand over
Deployed, monitored, with model-spend limits. Your repo, your accounts, a README and a walkthrough call.
Before any screens
Why we test the model first, and when we stop
Most failed AI MVPs fail on the AI, not the software. The model is not accurate enough on real inputs, or it is accurate but too slow, or it costs more per user than the product can charge. So the first thing we build is a harness, not a UI. These are the bars it has to clear, agreed with you before anyone looks at the results:
50 real inputs, not made-up ones
Enough to see the failure modes; small enough to label in a day.
A pass rate you agree before we look
Deciding the bar after seeing results is how bad ideas survive.
Cost per action under a fifth of the price per action
Otherwise every new user makes the margin worse.
Fast enough for the moment it's used in
A 20-second answer is fine for a report and fatal in a chat.
Scope
What an AI MVP should leave out
Admin dashboards, team accounts, billing tiers, integrations with ten other tools, a mobile app. All reasonable later, and all ways to spend months before learning whether anyone wants the core thing. Everything cut goes on a written later list, so it's parked, not lost.
SaaS and startups
Building a SaaS MVP?
The first version needs sign-up, one paid plan and the core loop. It rarely needs team accounts, usage-based billing or multi-tenancy tuned for scale. What it does need from day one is a per-user cap on model spend, because a single heavy user on a frontier model can cost more than they pay. For a startup, the first hundred users matter more than the architecture for the next hundred thousand.
The stack
Boring on purpose.
We build on the same stack as our own products: Next.js for the app, a TypeScript or Python backend, Postgres for data, and Claude or OpenAI for the model. None of it is exotic, which means any competent developer you hire later can pick it up. The model layer is kept behind a small interface so you can switch providers or models as prices and quality change, which they do every few months.
Proof
We ship our own AI products first.
SalesClawd. An AI marketing employee for small businesses. Three autonomous agents run SEO, email and bookings, with a human approval queue in front of every action.
The SEO agent coordinates ten specialist Claude agents in parallel, each with its own tools and evidence trail. Every change is read back from the live source and verified before anyone sees it. Fastify, Next.js, Postgres with Drizzle, BullMQ on Redis, the Anthropic SDK.
See SalesClawdInteractive checklist
10 questions to ask any MVP development company (including us)
Tick the ones they answered well. If they can't tell you what they'd cut, or what the AI costs per user, keep looking.
Tick each question the agency answered well.
0/10
A good fit if
- A founder with a clear problem and a specific user, who needs it built
- A company testing a new AI product line before committing a full team
- You want to own the code and keep building on it after launch
Not a fit if
- The idea is still "AI for X" with no user who has asked for it
- You need a clickable design mockup, not working software
- The first version must include every feature on the roadmap
Questions
Frequently asked
How much does it cost to build an AI MVP in 2026?+
Our AI MVPs start from $12,000 for one core workflow with login, a database and the AI feature it depends on; scope sets the final quote. Across the market, studios that publish prices start from about $1,500 to $15,000 (median $14,900), while cost guides quote $15,000 to $40,000 for a lean AI MVP and $40,000 to $120,000 for a production one.
How long does it take to build an MVP?+
Market ranges run from 1 to 4 weeks for an AI prototype, 4 to 8 weeks for a lean AI MVP and 8 to 16 weeks for a production one. Studios that use AI coding tools sit at the fast end; the scope and how quickly decisions get made matter more than the stack.
What is an AI MVP?+
A minimum viable product whose core value depends on an AI model. It is the smallest version that lets real users complete the main task, so you learn whether the idea works, including whether the AI is good enough, before building the full product.
What does an MVP development company do?+
It cuts an idea down to one core loop, builds the smallest real version on a stack you can keep, and puts it in front of real users. For an AI MVP, it also tests the model on real inputs before building screens, and sets limits on what the AI costs per user.
How much will the AI cost to run per user?+
It depends on the model and how often a user triggers it. At 60 actions a month of about 3,000 input and 700 output tokens, one active user costs from about $0.02 a month on DeepSeek V4 Flash to about $1.56 on Claude Opus 5.5, at OpenRouter list prices on 2026-09-24.
Should I build my MVP myself with Lovable, Bolt or Cursor, or hire someone?+
It's a sequencing decision. Build it yourself to find out whether anyone cares. Hire when people are using it and it needs to be secure and maintained, or when the AI step has to be right most of the time.
Can ChatGPT build me an app?+
It can write much of the code for a simple app, and tools built on these models can deploy one. What it won't do on its own is decide what to leave out, secure your data, test the AI on real inputs or keep it running as models change.
I already have a prototype built with AI tools. Do you rebuild it or finish it?+
Usually finish it. We check auth, secrets, data access, tests on the AI step and whether it runs outside the builder's platform. If those can be fixed, hardening is cheaper than a rebuild; if the data model is wrong, a partial rebuild is.
What is the difference between a prototype, a proof of concept and an MVP?+
A prototype shows how it would look, a proof of concept proves the hard part is possible, and an MVP is a small real product that real users can complete one task in. Each costs more and teaches you more than the last.
What should an AI MVP leave out?+
Admin dashboards, team accounts, billing tiers, many integrations and a mobile app. All reasonable later, and all ways to spend months before learning whether anyone wants the core thing.
Do we own the code, and which AI model will you use?+
You own the repository, cloud accounts and keys from the start. We test the models that fit your task on your real inputs, pick the cheapest one that clears the bar, and keep it swappable.
What happens after the MVP?+
If it earns it, we keep building, or your team continues in-house on the same mainstream stack with our handover. Either way you have the code, the evaluation set and the cost limits.
Bring the idea. We'll bring the cut list.
A 30-minute call with the person who would build it. You leave knowing what v1 is, what it leaves out, and how we'd prove the AI part first.