How Much Does It Cost to Build an AI App in the UK? 2026 Guide

“How much does it cost to build an AI app?” has become one of the most common questions we hear.

The problem is that an “AI app” can mean almost anything.

A customer service tool using an existing large language model is very different from a platform that searches thousands of private documents, performs actions across business systems or runs a model directly on a user's device.

So rather than giving you a meaningless range of £10,000 to £500,000, this guide breaks down what different types of AI products actually cost to build in the UK.

Quick Cost Summary

The biggest mistake is assuming that the model itself is the expensive part.

Increasingly, it isn't.

The work is usually in everything around the model: your application, data, integrations, permissions, security, evaluation and the systems required to make AI output reliable enough for people to use.

What Type of AI Are You Building?

Before estimating development costs, it helps to separate AI applications into several categories.

1. AI Features Using Existing Models

The simplest approach is to connect an existing application to a hosted model.

For example:

• summarising documents
• extracting information
• generating reports
• categorising support requests
• rewriting content
• answering questions
• generating structured data from unstructured text

You don't need to train your own model for this.

The application sends information to a model through an API and processes the response.

For a well-defined use case, this can often be added to an existing product for £15,000–£30,000.

2. AI Chat and Copilot Applications

A more sophisticated product might provide users with an AI assistant that understands their account, documents or business data.

The chat interface itself is relatively straightforward.

The complexity comes from deciding:

• what information the AI can access
• what users are allowed to see
• how conversation history is handled
• how responses are validated
• what happens when the AI is uncertain
• how sensitive data is protected

A production-ready AI assistant typically starts around £25,000–£60,000, depending on the surrounding application.

3. Retrieval-Augmented Generation (RAG)

RAG allows an AI system to search private information before generating an answer.

Instead of relying entirely on what the base model already knows, the system can retrieve information from sources such as:

• company documents
• policies
• contracts
• knowledge bases
• product catalogues
• research papers
• support tickets
• databases

This sounds simple, but reliable retrieval requires considerable engineering.

Documents need to be processed, indexed, permissioned and searched correctly. The application also needs a way to determine whether the retrieved information is actually relevant.

For a production RAG application, expect the AI and data layer alone to add roughly £10,000–£30,000 to a project.

4. AI Agents

AI agents go beyond answering questions.

They can perform actions.

For example, an agent might:

1. read an incoming request
2. search a customer record
3. check an external system
4. decide which action is required
5. update the record
6. send a response
7. record what happened

The difficulty isn't getting a model to call an API.

The difficulty is making sure it calls the right API, with the right information, at the right time — and doesn't do something unexpected.

Agents therefore require additional work around permissions, validation, audit logs, error recovery and human approval.

Agent-based applications commonly fall into the £60,000–£150,000+ range once the surrounding product is included.

5. Local and On-Device AI

Not every AI application needs to send information to a cloud model.

Smaller models can increasingly run directly on laptops, phones and other devices.

This can be useful where:

• data is particularly sensitive
• internet connectivity is unreliable
• low latency is important
• organisations want greater control over their models
• recurring cloud inference costs are undesirable

However, on-device AI introduces different technical challenges.

Models need to fit within the memory and processing limits of the hardware, and the application may need different implementations for iOS, Android and desktop platforms.

Adding a significant on-device AI capability can add £15,000–£40,000+ depending on the model and platforms involved.

What Actually Drives AI Development Cost?

1. The Application Around the AI

An AI model rarely makes a complete product.

You may still need:

• authentication
• user accounts
• billing
• dashboards
• administration tools
• mobile applications
• file uploads
• notifications
• reporting
• integrations
• audit logs

For many AI products, these conventional software features account for more development time than the AI itself.

2. Your Data

If the AI needs to understand your own data, the quality and structure of that data becomes important.

Clean documents stored in a consistent format are relatively straightforward.

Twenty years of PDFs, spreadsheets, emails and legacy database records are not.

Data preparation can become a significant part of an AI project.

3. Integrations

AI becomes considerably more useful when it can interact with other systems.

Typical integrations include:

• CRM platforms
• accounting systems
• booking systems
• internal databases
• document storage
• email
• calendars
• external APIs

Each integration introduces authentication, data mapping, testing and error-handling requirements.

4. Reliability

A demo only needs to work during the demo.

A production application needs to work when users phrase questions differently, upload unusual documents or provide incomplete information.

Production AI therefore needs additional systems for:

• evaluation
• monitoring
• fallbacks
• validation
• logging
• testing
• model versioning

This is one of the biggest differences between an AI prototype and an AI product.

5. Security and Compliance

Applications handling financial, legal, health or commercially sensitive data need additional safeguards.

Depending on the product, this can include:

• encryption
• access controls
• audit logging
• data retention rules
• model-provider controls
• regional data hosting
• anonymisation
• human review processes

These requirements should be designed into the product rather than added immediately before launch.

Typical AI Feature Costs

These aren't menu prices. Features interact with one another, so a project containing five items from the table won't necessarily cost the sum of all five.

They are useful, however, for understanding where complexity enters a project.

Build Cost vs AI Running Cost

AI applications have two separate budgets.

The first is the cost of building the product.

The second is the cost of running it.

Running costs can include:

• model inference
• hosting
• vector databases
• document processing
• storage
• monitoring
• backups
• third-party APIs

A small application may initially spend relatively little on model usage.

A product processing millions of documents or AI requests can have substantial ongoing infrastructure costs.

This is why model choice shouldn't only be based on which model performs best in a benchmark.

You need to consider the cost per useful task.

Example AI Project Budgets

Example 1: Internal Knowledge Assistant

A business wants employees to ask questions about internal policies and documents.

Features: Authentication, document upload, RAG search, AI chat, citations, admin tools

Technology: Web application, PostgreSQL, vector search, hosted language model

Timeline: 10–14 weeks

Indicative budget: £30,000–£45,000

Example 2: AI-Powered SaaS Product

A company wants to sell an AI tool that analyses customer documents and generates structured reports.

Features: User accounts, subscriptions, document processing, AI extraction, report generation, dashboard, admin system

Timeline: 4–6 months

Indicative budget: £60,000–£90,000

Example 3: Private On-Device AI Platform

An organisation needs sensitive information processed locally rather than sent to a cloud model.

Features: Local model inference, document search, model management, local storage, desktop or mobile application, optional private fine-tuning

Timeline: 6–9 months

Indicative budget: £100,000–£150,000+

The Technology Stack We Recommend

There isn't a single AI technology stack that works for every product.

For many projects, however, we use a combination of:

Web: React / Next.js

Mobile: React Native

Backend: Node.js or Python depending on the workload

Database: PostgreSQL

Vector search: PostgreSQL with vector extensions or a dedicated vector database where required

Models: Hosted commercial models, open-source models or local models depending on privacy, performance and cost requirements

Infrastructure: Managed cloud infrastructure for the first version, moving to more specialised infrastructure when scale requires it

The important architectural decision is keeping the application separate from the model provider where practical.

Models are changing quickly.

Your entire product shouldn't need to be rewritten because a better model becomes available.

Typical Timeline

A four-month AI application might look like this:

AI projects often benefit from a short technical proof of concept before full development.

The purpose isn't to build the application cheaply.

It's to answer the expensive technical questions before committing to the complete build.

Hidden Costs to Budget For

Model Usage

Hosted AI models are generally charged according to usage.

Your costs will therefore grow with the amount of information users send and receive.

Evaluation

AI output is probabilistic.

Unlike a conventional function, the same input doesn't always guarantee exactly the same output.

Testing therefore needs to measure quality across a collection of representative examples rather than simply checking whether a function returns the expected value.

Data Preparation

AI projects regularly expose problems with existing business data.

Documents may need cleaning, converting or restructuring before they are useful to a model.

Human Review

For higher-risk workflows, AI may assist a person rather than make the final decision.

That human-review process needs to be designed into the application.

Ongoing Model Changes

Models improve rapidly.

A production AI application should make it possible to test a new model before switching users across to it.

How to Reduce AI Development Costs

1. Don't Train a Model Unless You Need To

Most businesses do not need to train a foundation model.

Start with existing models and establish where they fail.

2. Solve One Workflow First

“An AI assistant for the whole company” is difficult to scope and test.

“An AI assistant that answers questions about these 500 policy documents” is much easier.

3. Keep Humans in the Loop Initially

Automating 100% of a process can be considerably more expensive than automating 80% and allowing a person to approve the result.

4. Test the AI Before Building the Interface

If the entire business proposition depends on an AI model performing a particular task, test that task first.

Don't spend three months building dashboards around an AI capability that doesn't work reliably enough.

5. Measure Quality

Create a set of representative test cases early.

When models, prompts or retrieval systems change, you then have an objective way to see whether the product has improved.

Do You Need AI at All?

Sometimes the cheapest AI feature is the one you don't build.

If a deterministic rules engine can solve the problem reliably, it may be cheaper, faster and easier to maintain.

AI becomes valuable when the problem involves things conventional software struggles with: language, documents, images, complex patterns or ambiguous information.

The goal shouldn't be to add AI.

It should be to solve a useful problem.

Next Steps

If you're planning an AI product:

1. Define the task — What exactly should the AI do?
2. Test the model — Can existing models perform that task reliably?
3. Understand your data — Where will the AI get its information?
4. Define the boundaries — What is the AI allowed to do?
5. Build the smallest useful version — Prove the workflow before expanding it.

Marketplace Labs designs and builds AI-powered web and mobile products, including private data systems, AI workflows and on-device AI.

If you're planning an AI application and want a realistic assessment of the cost and technical approach, get in touch.