AI MVP Development: Costs, Evaluation and GDPR

An AI MVP should answer one question fast: is the model's output good enough, often enough, that people will use it and pay for it? With Easify that typically means 6 to 12 weeks and €8,000 to €25,000, built on a hosted model rather than one you train, with an evaluation set, a cost cap per user and a GDPR-sound data flow from the first release. Model usage is a running cost on top, billed by the provider.

This guide covers when AI is the product and when it is a feature, how to build the MVP, what the model will cost to run, how to evaluate it, data privacy and the GDPR, a typical scope and price, and the pitfalls specific to AI products.

At a glance

AI as the product, or AI as a feature

The single most useful question before scoping: if you removed the model, what would be left?

• AI is the product when nothing useful remains. The user pays for the model's output: an answer, a draft, a classification, a query. The MVP's question is whether that output is good enough, consistently enough, to replace what the user does today. Evaluation is the centre of the build.
• AI is a feature when a useful product remains: a SaaS tool, a marketplace, a booking system, with a model doing one step such as summarising, extracting or suggesting. The MVP should test the product first. The AI step can be simple, or even a person doing the step behind the scenes until the product proves itself.

Founders often pitch the second as the first. A scheduling tool "with AI" is a scheduling tool; it lives or dies on scheduling. That is not a criticism, it just tells you where to spend the budget.

How to build an AI MVP

1. Write down the task and what a good result is. "Answer questions about our sales data in plain English, correctly, with the query shown" is buildable and testable. "An AI assistant for finance teams" is not.
2. Collect real examples before writing code. Fifty to two hundred real inputs with the answers you would accept. These become the evaluation set.
3. Start with a hosted model and a prompt. Add retrieval (searching your documents or data and passing the relevant parts to the model) only if the task needs knowledge the model does not have. Leave training and fine-tuning out of the MVP; our comparison of RAG and fine-tuning explains why.
4. Build the evaluation before the interface. Score the prompt against the examples, fix the worst failures, score again.
5. Design for wrong answers. Show sources or the generated query, let users correct or flag a result, and make the safe failure ("I could not answer that") better than a confident wrong one.
6. Wrap it in ordinary, solid software. Auth checked on the server, usage caps, logging, error handling when the provider is slow or down. Most of the build is this, not the model.
7. Launch to a small group and read the logs. Real questions differ from the ones you imagined. Add the failures to the evaluation set.

Minimum feature set checklist

• The one core AI flow, end to end
• An evaluation set of real examples with expected results, run on every change
• Model and prompt versions pinned and recorded
• Logging of inputs, outputs and cost per request, with retention limits
• Per-user and per-account usage caps, and spending alerts at the provider
• Timeouts, retries and a clear message when the model fails
• A way for users to flag a bad answer
• Sources, citations or the underlying query shown where the task allows
• Sign-up, sign-in and authorisation on the server
• API keys kept on the server, never in the browser
• A data processing agreement with the model provider, and a privacy policy that names it
• Telling users they are dealing with an AI system
• Billing, if the question is whether people will pay

LLM costs

Model providers charge per token (roughly, per word fragment) for both the input you send and the output you get back, and publish their prices: see OpenAI's API pricing or Anthropic's pricing. The arithmetic that matters is cost per task:

• Tokens per call. Long system prompts, retrieved documents and chat history are all input tokens, sent again on every call.
• Calls per task. An "agent" that plans, calls tools and checks its work can make many calls for one user request. Each one is billed.
• Tasks per user. The heavy users decide the bill, not the average.

Practical controls for an MVP: cap usage per user and per account, set a spending limit and alerts at the provider, route simple steps to a smaller, cheaper model, trim what you send, and cache answers to repeated questions. Price any free tier from the cost per task, not from a guess. A free plan with no cap on a model that costs real money per request is an open invitation.

Evaluation

Without evaluation, every prompt change is a guess and every model update is a risk.

• Grade automatically where you can. Exact matches, valid JSON, a generated database query that returns the same result as the correct one.
• Use a second model as a judge with care. It scales, but check a sample of its verdicts by hand, because it has its own blind spots.
• Track results over time. A change that fixes three examples and breaks five is a regression.
• Re-run when the provider changes the model. Pin model versions so they change only when you decide, and plan for older versions being retired.
• Test the hostile cases too. Inputs that try to override your instructions (prompt injection) are a known class of attack; the OWASP Top 10 for LLM applications is a good checklist.

Data privacy and the GDPR

If your users are in the EU, sending their data to a model provider is processing under the GDPR, and it needs the same care as any other processor. This is general guidance, not legal advice.

• A data processing agreement. The provider acts as your processor, which Article 28 requires to be covered by a contract. Major providers publish one.
• International transfers. If data leaves the EU, you need a valid transfer mechanism under Chapter V, such as the provider's certification under the EU-US Data Privacy Framework or standard contractual clauses. Some providers offer EU data residency; check what it covers.
• Retention and training. Check how long the provider keeps your inputs and whether they can be used to train its models, and choose the settings that fit your users' expectations.
• Send only what the task needs. Strip or pseudonymise personal data that does not change the answer. For a tool that queries a customer's database, consider sending the structure and the question rather than the rows themselves.
• Lock down what the model can touch. If the model's output runs anything, such as a database query, run it with a read-only account, row limits and timeouts.
• Plan for deletion. A user's right to erasure reaches logs and search indexes as well as the main database.
• Assess the risk. Large-scale processing of sensitive data may need a data protection impact assessment (Article 35).
• Be transparent. The EU AI Act adds transparency duties, including making clear to people when they are interacting with an AI system (Article 50).

Typical scope and price

Illustrative ranges inside our €5,000 to €25,000 MVP band, not a quote. Try the MVP cost calculator for your own scope.

Model usage and hosting are running costs on top, paid to the providers. For comparison, Vamasters estimates a simple MVP built in Eastern Europe at about $10k to $20k and a mid-range one at $25k to $60k (Vamasters); that is one source's estimate for MVPs in general, not AI products specifically.

Pitfalls specific to AI products

• No evaluation set. The demo works on the five questions the founder tried. Real users ask the sixth.
• Unbounded costs. No caps, a generous free tier and an agent loop can produce a bill larger than the build.
• API keys in the browser. Common in prototypes made with AI app builders, and one of the first things to fix. If yours started in Lovable, Bolt, v0 or similar, our €99 Prototype Check reviews the repository and live app within 48 hours.
• Confident wrong answers. An interface that hides uncertainty loses users the first time it is wrong about something that matters.
• Building an agent when a single call would do. More steps mean more cost, more latency and more ways to fail.
• Training a model before you have users. Expensive, slow, and answers a question you do not have yet.
• Personal data sent by default. Easier to design out at the start than to remove from logs later.

Case study: Easify AI

Easify AI is our own product: the AI sub-brand of Easify Technologies. It answers questions about a company's database in plain English, and we took it from an idea to a working MVP that people could use, built with WordPress and React.

Its case study is candid about the starting point. The platform existed as a plan rather than a product, and the questions that mattered could only be answered by users: whether the AI querying was genuinely easier than writing the query yourself, and whether anyone would pay for it. Building the full platform first would have answered those questions late and expensively. So the MVP was scoped around the core querying flow, with everything that could wait left out. Development ran in short cycles so that feedback changed the next iteration rather than a later release, the interface was kept plain enough for a new user to reach a result without onboarding, and the foundations were built to carry more, so the parts left out were postponed rather than designed out.

In the terms of this guide, AI is the product there, and the MVP was built to test exactly that.

When not to hire us

• You need research, not a product. Training new models or work at the edge of what models can do needs a machine learning research team.
• You have not tried the task by hand. Paste twenty real examples into a chat interface first. If the answers are not close to useful, an MVP will not change that.
• AI is a feature of an untested product. Then the product is the MVP. Read the SaaS MVP guide and add the model later.

For how we scope and run an MVP generally, see MVP development.