AI MVP Cost in 2026: What It Costs to Build and to Run

The Two Costs: Build and Run

"How much does it cost to build an AI app?" usually means the build. But an AI product keeps costing money after launch: every question or upload means paying the model provider for the text you send and the text you get back.

• Build cost is one-off: the app, prompts, retrieval, evaluation, guardrails and deployment. This is what a developer quotes.
• Running cost is monthly and grows with usage: model tokens, embeddings, hosting, the database, monitoring and the time people spend checking outputs.

A good quote covers the first and estimates the second. If a team cannot say roughly what each user action costs in tokens, the product is not designed yet.

Build Cost by Type of AI MVP

The table below uses minidev.pro's published prices from the MVP cost calculator. The $5,000 base covers sign-up, one core workflow, database, Stripe checkout or a waitlist, a basic admin view, a landing page, deployment and 30 days of post-launch fixes. Build days are working days.

From our published price list. The calculator shows a range because details such as screen count move the final number; a call turns it into a fixed quote.

For comparison, our MVP development cost guide puts general market ranges for an MVP at $8,000 to $25,000 with freelancers, $15,000 to $40,000 with offshore teams and $50,000 to $150,000 or more with agencies. These are general MVP ranges, not AI-specific quotes; AI adds evaluation and guardrail time on top.

Adding AI to a product you already have is scoped after a look at the code, or shipped as a feature on the Growth Partner retainer. More on what we build: AI development.

How Token Pricing Works

Hosted models charge per million tokens. A token is a chunk of text, often part of a word. You pay separately for:

• Input tokens: everything you send: instructions, documents or search results, conversation history and the user's message.
• Output tokens: everything the model writes back, priced several times higher than input.

The monthly formula: requests per month × tokens per request ÷ 1,000,000 × price per million, for input and output separately, then added. List prices from the official pages on October 15, 2026:

Standard API prices as of October 2026. Claude Haiku 5.5's price applies to prompts up to 100,000 tokens; longer prompts cost more. OpenAI lists higher long-context prices for these models. Both providers offer a Batch API at 50% off. See Sources.

Equal list prices do not mean equal bills: Anthropic notes its newer tokenizer (Claude 4.7 and later) produces roughly 30% more tokens for the same text. Measure your real prompts on each provider, and treat every price here as a snapshot.

Monthly Running Cost: Three Worked Examples

Each example uses a 30-day month and the standard prices above. The small models share a list price across both providers, as do the mid-size ones, so each row covers either. Swap in your own token counts.

1. Support chatbot over your help docs

Assumptions: 500 conversations a day, 4 replies each, so 2,000 requests a day and 60,000 a month. Each request sends 3,000 input tokens (1,000 of instructions, 1,500 of retrieved help articles, 500 of conversation history) and gets back 300 output tokens.

Monthly tokens: 60,000 × 3,000 = 180M input; 60,000 × 300 = 18M output.

Embedding each user question to search the help docs (about 50 tokens each) adds 60,000 × 50 = 3M tokens × $0.02 = $0.06 a month with text-embedding-3-small. Embeddings are rarely the cost that matters.

2. Document summariser

Assumptions: 200 documents a day, so 6,000 a month. Each request sends 10,500 input tokens (a 10,000-token document plus 500 of instructions) and gets back an 800-token summary.

Monthly tokens: 6,000 × 10,500 = 63M input; 6,000 × 800 = 4.8M output.

Summaries rarely need an instant answer, so batch processing fits: results come back later, at half the price.

3. Data extraction pipeline

Assumptions: 1,000 pages a day, so 30,000 a month. Each page sends 3,000 input tokens (2,000 of page text plus 1,000 of instructions and the output schema) and returns 600 tokens of structured data.

Monthly tokens: 30,000 × 3,000 = 90M input; 30,000 × 600 = 18M output.

Now add people. If a reviewer checks 10% of pages and spends one minute on each, that is 3,000 minutes, or 50 hours a month. At almost any professional hourly rate, that costs more than the model, so design reviews around the values the system is unsure about. Page images instead of text change the token counts, so measure on your own documents.

Other Running Costs

• Hosting. The app, background jobs and file storage, usually the same platform bill as a normal web app (we typically use Vercel or AWS). Long document jobs may need a queue and a worker.
• Vector storage. If the product searches your own documents, you store embeddings somewhere. pgvector is an open-source Postgres extension released under the PostgreSQL License, so it adds no licence fee to a Postgres database you already pay for. If you want a separate hosted service, Pinecone's Starter plan is free with up to 2 GB of storage, 2M write units and 1M read units a month; its Builder plan is $20 a month and Standard has a $50 monthly minimum.
• Monitoring and logging. Record every prompt, response, token count and error to debug and spot cost spikes, with a tool or simple logging in your database.
• Human review time. Someone checks flagged outputs, answers escalations and keeps the evaluation set current. As the extraction example shows, this is often the biggest line.
• Re-testing when models change. Providers retire old models. Each switch means re-running your evaluation set: cheap if you have one, painful if not.

How to Cut Running Costs

1. Use the smallest model that passes your tests. In all three examples the small model costs 20 times less than the mid-size one. Classification, routing and short summaries often do well on small models; route only hard cases to a bigger one.
2. Cache repeated prompt text. In the chatbot, the same 1,000-token instructions go out 60,000 times: 60M tokens, or $120 a month at Claude Sonnet 5.5's $2 input price. Read from the prompt cache, those tokens cost $0.10 per million, or $6. Cache writes cost extra ($2.50 per million for Sonnet 5.5's 5-minute cache) and the cache expires when idle, so savings depend on steady traffic. OpenAI lists the same $0.10 cached-input price for gpt-6.1-sol.
3. Batch anything that can wait. Both providers take 50% off for batch requests. Overnight summaries, back-catalogue extraction and report generation all qualify.
4. Use retrieval to shorten prompts. Send the three or four most relevant passages, not the whole manual. Input is often the bigger share of the bill.
5. Cap output length. Ask for short, structured answers and set a maximum. Output tokens cost five times input on every chat model in the table above.
6. Evaluate before you scale. A test set of real inputs with known correct outputs tells you whether a cheaper model is good enough, before you pay for thousands of wrong answers.

What Pushes the Build Cost Up

Calling a model takes a few lines of code. These are the things that take the real build time:

• Evaluation sets. Collecting real examples, agreeing the right answers and scoring the system against them. Slow work, and the best predictor of whether an AI product works.
• Guardrails. Stopping the product from answering out-of-scope questions, leaking data across customers, or acting on instructions hidden inside uploaded documents.
• Accuracy requirements. "Usually helpful" is cheap. "Never wrong on a number" needs cross-checks, confidence handling, review queues and the option to refuse rather than guess.
• Regulated data. Health, care, financial or legal data needs audit trails, retention rules and access controls, which is why compliance is a +$3,000 add-on on our price list. See compliance software for what that covers.
• Messy inputs. Scanned PDFs, inconsistent table layouts and multiple file formats add parsing work before any AI step can run.
• Integrations. Each connection to a help desk, CRM or file store adds build time and its own failure cases.

When We Are Not the Right Choice

Our MVP development model is a fixed scope delivered in 30 days or less. It is a poor fit if you need to train or fine-tune your own model, run models on your own hardware, or do open-ended research where nobody knows whether the task can be done; those need an ML research team and room for dead ends. It is also more than you need if a no-code tool with a built-in AI step already solves the problem.

It fits when the task is clear, a hosted model can do it, and the value is in the product around the model: the workflow, review screens, integrations and evidence that it is accurate enough to trust.

Frequently Asked Questions

How much does it cost to build an AI MVP?

At minidev.pro, an MVP whose core value is an AI workflow starts at $8,000 fixed ($5,000 base plus $3,000 for the AI product work). A normal app with AI features on top starts at $7,500, and document extraction with audit trails and access controls starts at about $12,000. As a general market guide for MVPs, freelancers typically quote $8,000 to $25,000 and agencies $50,000 to $150,000 or more.

How much does it cost to run an AI app per month?

For an MVP, model usage is often tens to hundreds of dollars a month. In our worked examples, a support chatbot handling 2,000 replies a day costs about $27 a month on a small model and $540 on a mid-size one, at October 2026 list prices. Hosting, a database and human review time come on top, and review time is often the largest line.

Is OpenAI or Anthropic cheaper for an AI MVP?

As of October 2026 their list prices are close at each size: Claude Haiku 5.5 and gpt-6-luna both cost $0.10 per million input tokens and $0.50 per million output tokens, and Claude Sonnet 5.5 and gpt-6.1-sol both cost $2 and $10. Providers count tokens differently, so run your own prompts through both and compare the bills and the quality.

Do I need a vector database for an AI MVP?

Only if the product searches your own documents to answer questions. Even then, most MVPs can store embeddings in the Postgres database they already have using pgvector, an open-source extension with no licence fee. Hosted options such as Pinecone have a free Starter plan if you prefer a separate service.

Should I fine-tune or train my own model for an MVP?

Almost never at the MVP stage. Start with a hosted model, good prompts, retrieval over your own data and an evaluation set. Fine-tuning or training only makes sense once real usage shows a specific gap that prompting and retrieval cannot close, and you have the data to prove it.

Why do quotes for AI apps vary so much?

Because the hard part is not calling the model. It is deciding how accurate the output must be, building the test set that proves it, adding guardrails, and handling regulated data. A demo that works on five examples is cheap. A product that must be right on thousands of real documents costs more, and quotes differ in how much of that work they include.

Sources

All pricing pages checked October 15, 2026. Prices change often; check the provider's page before you budget.

• Anthropic, Pricing (model prices, prompt caching, Batch API, tokenizer note, Haiku 5.5 prompt-length tiers): platform.claude.com/docs/en/about-claude/pricing
• OpenAI, API pricing (model prices, cached input, Batch tier, long-context tiers, embeddings): developers.openai.com/api/docs/pricing
• pgvector, README (open-source vector similarity search for Postgres): github.com/pgvector/pgvector
• pgvector, LICENSE (PostgreSQL License): github.com/pgvector/pgvector/blob/master/LICENSE
• Pinecone, Pricing (Starter, Builder and Standard plans): pinecone.io/pricing
• minidev.pro, MVP cost calculator price list: minidev.pro/mvp-cost-calculator
• minidev.pro, MVP development cost guide 2026 (freelancer, offshore and agency ranges): minidev.pro/blog/mvp-development-cost-guide-2026

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