Most AI MVP budgets go wrong in the same place. The team prices the screens and the backend like a normal web app, then adds “AI integration” as a single line item, as if calling a model were the same as calling a payments API. Then the first week of real testing shows the model getting a fifth of the answers subtly wrong, and the budget has nothing set aside for fixing that.
This article breaks an AI MVP into its real cost components, gives typical market ranges with the assumptions behind them, and shows how to estimate your monthly model costs before you write any code. The figures are not quotes. Treat them as a way to check whether a proposal you receive is in a sensible range.
The assumptions behind the numbers
Ranges only mean something if you know what they describe. The figures below assume a focused MVP: one core workflow, a web interface, user accounts, one or two integrations (a CRM, a document store, an email inbox), and one AI capability at the center, such as answering questions over a document set, classifying incoming requests, or drafting replies for human review.
They also assume an experienced small team rather than a single freelancer or a large agency. Rates vary widely by region. A team in South Asia or Eastern Europe may charge a third of what a comparable team in the US or UK charges, so the same scope can land at very different totals. Where ranges appear, the low end reflects lower-cost regions and tight scope; the high end reflects higher-cost markets or extra complexity.
Budget components, one by one
Discovery and scoping
Typically 5–10% of the build budget, or roughly $1,000–$8,000 as a standalone engagement. This is where you pin down the workflow, the inputs and outputs, and what counts as a correct answer. Skipping it does not save money. It moves the cost into rework later.
Design
Usually $2,000–$15,000 for an MVP. AI products need design time for states normal apps do not have: loading while the model thinks, showing sources, letting users correct or reject an output, and handling “I don’t know” responses gracefully.
Core build
The conventional software: authentication, database, admin screens, integrations, and the main user flow. For the scope above, typical market ranges run from about $10,000 to $60,000. This is often the largest single line, and the easiest to compare across vendors.
AI integration
Prompt design, retrieval setup if the model needs to read your documents, structured output handling, and fallbacks when the model fails. Expect $5,000–$30,000 depending on how many AI steps the workflow has and how messy the source data is.
Testing and evaluation
Standard QA plus an evaluation set: a collection of real inputs with known good outputs that you run every time the prompts or model change. Budget $2,000–$12,000. Teams that cut this line usually spend more on it later, in production, with customers watching.
Deployment
Hosting setup, environments, monitoring, logging of model inputs and outputs, and launch on your domain. Often $1,000–$5,000 as a one-off.
Ongoing costs
Hosting for an MVP commonly runs $50–$500 a month. Model and API usage is variable and covered below. Maintenance, meaning bug fixes, dependency updates, and adjusting prompts when a provider updates a model, is often priced at 10–20% of the build cost per year, or as a monthly retainer.
Why AI MVPs cost more than a normal MVP
Four things add cost that a conventional app does not carry.
- Evaluation. A button either works or it does not. A model answer can be mostly right, which is harder to test. Someone has to build the test set, define what “correct” means, and review results.
- Prompt iteration. The first prompt rarely survives contact with real data. An invoice arrives as a scanned PDF with a rotated table, and extraction that worked on clean samples breaks. Each round of fixing means changing the prompt, rerunning the evaluation set, and checking nothing else regressed.
- Guardrails. Input validation, output checks, limits on what the model can do, refusal handling, and human review steps for high-stakes outputs. This is engineering time, not a setting.
- Per-call API costs. Every request costs money, including the ones made during development and testing. Running a 200-item evaluation set fifty times during prompt tuning is 10,000 model calls before launch.
As a rough rule, AI MVPs tend to cost 20–50% more than a conventional app of similar screen count, mostly due to evaluation and iteration time.
Estimating monthly LLM costs with simple arithmetic
You do not need a spreadsheet model to get a usable estimate. The formula is:
Monthly cost = requests per month × tokens per request × price per token
Do it separately for input tokens (what you send: instructions, retrieved documents, the user’s message) and output tokens (what the model writes back), because providers price them differently.
Here is a worked example. The prices are illustrative only, chosen to make the arithmetic easy, not current rates for any specific model. Check your provider’s pricing page before relying on any number.
- Example price: $3 per million input tokens, $15 per million output tokens.
- Usage: 500 requests a day, about 15,000 requests a month.
- Each request sends 3,000 input tokens (instructions plus retrieved context) and receives 500 output tokens.
- Input: 15,000 × 3,000 = 45 million tokens. At $3 per million, that is $135.
- Output: 15,000 × 500 = 7.5 million tokens. At $15 per million, that is $112.50.
- Total: about $250 a month.
Then adjust for reality. Agent-style workflows often make three to ten model calls per user action, so multiply accordingly. Retries and failed calls add 5–15%. Retrieved context tends to grow as people add documents. A sensible budget takes the clean estimate and adds a 50–100% buffer for the first few months, then tightens it once you have real usage logs.
The same arithmetic tells you where to save. If input tokens dominate, trimming retrieved context or caching repeated instructions cuts cost. If volume is high and the task is simple, a smaller, cheaper model may handle it.
Timeline phases
For a traditional engagement with the scope described above, typical timelines look like this:
- Discovery: 1–2 weeks.
- Design: 1–3 weeks, often overlapping with discovery.
- Build and AI integration: 4–10 weeks, run in parallel where possible.
- Evaluation and testing: 1–3 weeks, though evaluation should start as soon as the first prompt works.
- Deployment and launch: under a week.
That adds up to roughly two to four months end to end. The variance comes from three places: how quickly the client provides data and access, how many decision-makers sign off on scope, and how far the real data differs from the samples.
How fixed-scope sprints change the math
Time-and-materials projects carry an open-ended risk: if prompt iteration takes three extra weeks, you pay for three extra weeks. Fixed-scope sprints flip that. The scope is agreed in writing before the price, the vendor carries the overrun risk, and anything outside the scope becomes a deliberate follow-on decision rather than a silent budget leak.
The trade-off is discipline. A fixed-scope sprint only works if the first version is genuinely narrow: one workflow, one AI capability, clear acceptance criteria. At WeaverSol, our Production Grade MVP follows this model, delivering deployable software in about 7 days on a fixed scope, with the client owning all of the code. Short sprints like this also produce real usage data quickly, which makes the next budget decision far better informed than any estimate written upfront.
Your next step
Before talking to any vendor, write one page covering the single workflow the MVP must handle, five to ten real examples of inputs with the correct outputs, the expected monthly request volume, and the systems it needs to connect to. Run the token arithmetic above on that volume. With that page in hand, you can compare proposals on substance rather than on headline price.
If you want a second opinion on scope or a cost estimate, get in touch and we will walk through it with you.