What Is an AI MVP? Cost, Scope and How to Build One

What Is Different About an AI MVP

A regular MVP tests whether users want the product. An AI MVP also has to answer questions that only AI products face:

• Is the output good enough? The model has to give answers your users find useful, consistently.
• Is it fast enough? Users leave if they wait too long, so responses are often streamed as they are generated.
• What does each user cost? Every call to a language model costs money, and that cost grows with usage. We check how many tokens each call uses during testing.
• Is it protected? Because model calls cost money, rate limiting and proper sign-in are essential from the first version.
• What happens when the AI gets it wrong? The app needs a clear fallback when the model returns something unusable.
• Which data can it use? If the AI answers from your own documents or data, you decide early what it can access.

How Much an AI MVP Costs

If the app is very simple, it can be built on a template or with AI app builders and coding tools such as Lovable or Claude Code. These apps are generally not made for production use, for two reasons:

1. There is no technical depth. If a non-technical person can build it in an afternoon, so can anyone else, which makes it easy to copy.
2. Security, accessibility, and scalability suffer. When the builder has nobody who understands the technology behind the app, these issues tend to surface once real users arrive.

If the MVP is built by a developer or an external agency, expect to pay somewhere between $5,000 and $15,000 for the first version. Our MVPs cost between $3,999 and $8,999, depending mainly on your core feature. You can see what each tier includes on our MVP cost page.

On top of the build, an AI MVP has a running cost: the AI model's usage, paid directly to the provider. It depends on how many people use the AI feature, how often, and how much text each call sends and receives.

What to Include

The scope should be very minimal. An AI MVP focuses on the core problem and the one AI job that solves it, with only a few aesthetic or user-experience extras. A typical first version includes:

• One core AI feature, such as a chat assistant, document search, or content generation
• Sign-in, user accounts, and a database
• Rate limiting by IP address and by user
• Token usage and cost checks during testing
• Deployment so real users can try it

Custom-trained models, multiple AI features, and detailed analytics can come later, once users show the idea works.

How to Build One

We build AI MVPs with the same 2-week process as any MVP, with extra checks for the AI:

1. Day 1, scope: agree the one AI job the product must do, look at sample inputs, and decide what the model must never do.
2. Days 2 and 3, design: plan the screens around the AI feature and how results are shown.
3. Days 4 to 9, build: connect an existing model, build the feature and the screens around it, and add rate limiting.
4. Day 10, deploy: put the product online for real users.
5. Days 11 to 14, test and launch: test the full journey with people, check output quality and token costs, make one or two rounds of changes, and launch.

If your product needs its own data and model training, that work usually takes longer than two weeks. For the details of how we build AI products, see our AI MVP development page.