What is involved in building an MVP with AI or LLM features?
Short answer
An AI MVP is a normal product plus five extra pieces: a model accessed through an API, the prompts and context that steer it, a way to give it your data, checks on what it returns, and an evaluation set that tells you whether it is good enough. The model is the easy part. The evaluation set is what separates a demo from a product.
By Kailesk Khumar, founder of HouseofMVPs. Last reviewed .
The five extra pieces
| Piece | What it is | Why it matters |
|---|---|---|
| Model access | A hosted model called through an API | No training needed for most products |
| Prompts and context | The instructions and data sent with each request | Most of the quality comes from here |
| Your data | Retrieval from your documents or database | Lets the model answer about things it was not trained on |
| Output checks | Validation of structure, facts and safety before anything reaches a user | Models are confident when wrong |
| Evaluation set | Real examples with known good answers | Tells you if a change made things better or worse |
What it costs to run
Model prices are published per million tokens and fall into tiers. As read from the vendor pricing pages on 9 October 2026, small models such as GPT-6 Luna and Claude Haiku 5.5 list at $0.10 input and $0.50 output per million tokens. Mid tier models such as GPT-6.1 Sol and Claude Sonnet 5.5 list at $2 and $10. That is a twentyfold difference, so sending easy requests to a small model is the main cost control.
For a product answering 10,000 short questions a month, that works out to about $2 on a small model and about $40 on a mid tier one at list price. Agents that carry long context or call many tools cost far more.
The order to build in
- Collect 30 to 50 real examples of the task with the answer you would accept.
- Test the riskiest AI step against them before building anything else.
- Build the product around the step that works.
- Add output checks and a fallback for when the model fails.
- Add cost and quality monitoring before launch, not after.
What you usually do not need
- Your own model. Hosted models cover most products.
- Fine tuning. Retrieval and better prompts solve most quality problems, and OpenAI has closed self serve fine tuning to new customers.
- A vector database on day one. Start with what your existing database can do.
How HouseofMVPs handles this
HouseofMVPs builds AI MVPs at the same fixed tiers as other MVPs: $3,999 for one AI feature in 1 week, $7,499 for a production AI MVP in 2 weeks, from $14,999 for multi agent or custom retrieval work. Every build includes output validation, an evaluation set and cost monitoring.
Get a written scope and fixed priceRelated questions
Do I need to train my own model for an AI MVP?
Almost never. A hosted model with good prompts and access to your data handles most products. Training or fine tuning is worth considering only after a working product shows a specific gap that prompts and retrieval cannot close.
How do I know if the AI is good enough to launch?
Decide the pass mark before you test. Build a set of real examples, define what a correct answer is, and measure. If you cannot say what good enough means in numbers, you are not ready to launch.
Which model should I use?
Start with a mid tier model from the provider your team knows, get the product working, then test whether a small model passes the same evaluation set. If it does, switch and save most of the cost.
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