# How Much Does It Cost to Build an AI App?

- Tool: Development Cost
- Last updated: November 2026

## TL;DR

AI app development cost runs $15,000 to $50,000 for an API-based MVP built on a hosted model like Claude or GPT, $50,000 to $250,000 for a production AI product with its own RAG pipeline and evaluation suite, and $150,000 to $650,000 for a custom-trained or fine-tuned model. The gap between tiers comes down to whether you're calling an existing model API or training your own. Running costs are usually a much smaller line, often just cents per AI call.

## AI app cost by build type

Most of the spread in AI app pricing comes from one decision: do you call an existing model's API, or do you train or fine-tune your own? API-based apps reuse a model that already works and spend the budget on the product around it. Custom-trained apps spend most of the budget on the model itself.

| Tier | Examples | Cost | Timeline |
| --- | --- | --- | --- |
| API-based feature | One AI feature added to an existing app, e.g. summarization or drafting | $15,000-$40,000 | 3-7 weeks |
| API-based AI MVP | Standalone product: LLM + RAG + UI + 1-2 integrations | $15,000-$50,000 | 6-12 weeks |
| Production AI product | Own RAG pipeline, eval suite, guardrails, multiple integrations | $50,000-$250,000 | 4-7 months |
| Custom or fine-tuned model | Model fine-tuned or trained on proprietary data | $150,000-$400,000+ | 7-12+ months |
| Enterprise AI platform | Multiple models, compliance, large-scale fine-tuning programs | $250,000-$650,000+ | 12+ months |

## What each component adds

| Component | Hours | Notes |
| --- | --- | --- |
| Discovery | 40-80 h | Scoping the use case, deciding API vs. fine-tune |
| Data audit and prep | 40-160 h | Often the most underestimated line item |
| LLM backend | 80-200 h | Prompting, orchestration, API integration |
| RAG pipeline | 80-200 h | Retrieval over your own content or documents |
| Eval set and tests | 60-160 h | The component most often missing from low quotes |
| Guardrails and access control | 40-120 h | Rate limits, input validation, permissions |
| UI | 60-160 h | The actual product surface users interact with |
| Integrations | 60-200 h per system | Each connected tool or data source |
| Fine-tuning (if needed) | 160-480 h | Only relevant for the custom-model tier |

## Cost by who builds it

| Option | Hourly rate | Total for a typical v1 | Trade-offs |
| --- | --- | --- | --- |
| US team, hourly | $80-$160/h | $12,000-$64,000 for a 150-400 hour MVP | Highest quality, highest hourly cost |
| Eastern Europe team, hourly | $35-$80/h | $5,250-$32,000 for the same scope | Lower cost, usually a bit more coordination overhead |
| Agency, fixed-price | Quoted as a fixed price, not hourly | $15,000-$50,000 for an API-based MVP | Price agreed before work starts; RapidDev's band sits here |
| In-house ML/AI engineer | Salary-based, not hourly | $150,000-$220,000+/yr loaded | Right once you need continuous model work, not a one-off build |

## Running cost per AI call (worked math, Nov 2026 prices)

Model running cost is calculated as (input tokens x input price + output tokens x output price) / 1,000,000. Worked example: an AI writing or summarizing feature using 3,000 input tokens and 800 output tokens per call.

| Model | Per call | 50,000 calls/mo | Per active user (30 calls/mo) |
| --- | --- | --- | --- |
| Claude Sonnet 5.5 / GPT-6.1 Sol ($2/$10 per M) | 3,000 x $2/1M + 800 x $10/1M = $0.014 | $700 | $0.42 |
| Claude Haiku 5.5 / GPT-6 Luna ($0.10/$0.50 per M) | $0.0003 + $0.0004 = $0.0007 | $35 | $0.02 |
| Gemini 3.8 Flash ($0.75/$3.75 per M, doubles Jan 1 2027) | $0.00225 + $0.003 = $0.00525 | $263 (rising to about $525 after Jan 1 2027) | $0.16 |
| Claude Opus 5.5 ($4/$20 per M) | $0.012 + $0.016 = $0.028 | $1,400 | $0.84 |
| Batch API, Sonnet 5.5 (async, -50%) | $0.007 | $350 | - |

Model fees are rarely the biggest running-cost line. Budget $200-$1,500/mo in infra for an API-based app, $2,000-$8,000/mo once you're fine-tuning, and $15,000-$60,000+/mo for a fully custom-trained model. Add maintenance of 15-25% of the build cost per year (or 3-8% per month, by one agency's rule of thumb), plus $0-$50/mo for a vector database if you're running RAG on something like Supabase pgvector or Pinecone's Builder tier.

## Costs people forget

- Evaluation and AI-specific QA, typically 10-20% of the build on top of normal testing
- LLMOps setup, meaning monitoring, logging, and rollback tooling, often $20,000-$80,000+ for a production system
- Human-in-the-loop review for anything high-stakes, $15,000-$60,000+ depending on volume
- Safety, guardrails, and red-teaming, $20,000-$100,000+ for a regulated or high-risk use case
- A forced model migration at least once a year as providers retire older models with notice periods as short as 60 days
- Price changes on the model itself. Gemini's per-token price is scheduled to roughly double on Dec 1 2026 and again on Jan 1 2027
- Continuous improvement budget of 15-25% of build cost per year as usage patterns and model behavior shift

## How to spend less without regretting it

- Use a hosted model API instead of fine-tuning. Fine-tuning alone adds 160-480 hours to the build
- Route easy calls to a cheap model like Claude Haiku or GPT-6 Luna, roughly 20x cheaper per token than Sonnet or GPT-6.1 Sol, and reserve the expensive model for hard cases
- Use prompt caching wherever the same system prompt or context repeats. Cached input tokens cost around 5% of the uncached price
- Build RAG on Postgres with pgvector instead of standing up a separate vector database
- Before building, check whether a bought product solves it. Menlo Ventures found 76% of enterprise AI use cases in 2025 were bought, not built
- Scope the eval set and guardrails into the original quote. They're the line item low quotes leave out, and the one that causes the most rework later

## Frequently asked questions

### How much does it cost to build an AI app?

An API-based AI app built on a hosted model like Claude or GPT costs $15,000 to $50,000 for an MVP. A production AI product with its own RAG pipeline and evaluation suite runs $50,000 to $250,000, and a custom-trained or fine-tuned model climbs to $150,000-$650,000 or more.

### Why do AI app cost estimates vary so much?

Agency guides for 'AI app development' quote both a single AI feature and a full custom-trained model under the same phrase. The actual cost driver is whether you're calling an existing model's API or training your own; everything else is a smaller adjustment on top of that choice.

### Is it cheaper to use an AI API or train my own model?

Using a hosted API from a provider like Anthropic or OpenAI is cheaper almost every time. Fine-tuning adds 160-480 hours to the build on top of the base AI app, and running a custom-trained model costs $15,000-$60,000+ a month in infra versus $200-$1,500/mo for an API-based app.

### How long does it take to build an AI app?

An API-based AI MVP typically takes 6-12 weeks. A production AI product with evaluation and guardrails takes 4-7 months, and a custom-trained model project runs 7-12 months or longer.

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

At moderate volume, an API-based AI app usually costs $200-$1,500/mo in combined LLM and infra fees. A single AI call with Claude Sonnet 5.5 on a 3,000-token prompt costs about $0.014; at 50,000 calls a month that's roughly $700 in model fees alone.

### What's the single biggest hidden cost in an AI app build?

Evaluation and testing. Teams that skip it save 10-20% of the quote up front and then spend far more fixing hallucinations, bad retrieval, or unsafe outputs after launch. It's the line item most often missing from low quotes.

### Does RapidDev build AI apps?

RapidDev's $13,000-$25,000 fixed-price band covers API-based AI apps, meaning an LLM feature, RAG, a UI, and a couple of integrations built on a hosted model. Fine-tuned models and enterprise AI platforms are outside that range.

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Source: https://www.rapidevelopers.com/cost/ai-app-development-cost
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