AI Integration Costs for Enterprise Apps UK 2026

Contents

The three types of AI integration (and their very different costs)
What UK businesses actually pay in 2026
The hidden costs nobody quotes upfront
Build vs API: the decision that drives most of the budget
How to scope your AI integration project

The three types of AI integration (and their very different costs)

When UK companies say they want to “add AI” to their product or internal systems, they usually mean one of three very different things. The cost difference between them is substantial.

API integration. Using an existing AI model (OpenAI, Anthropic, Google Gemini) via API. Your development team builds the integration layer — prompts, context management, error handling, rate limiting — but the model itself is hosted and maintained by the provider. This is the fastest and cheapest approach, with most of the cost in development time rather than infrastructure.

Fine-tuning an existing model. Taking a foundation model and training it further on your own data to specialise it for your use case. More expensive than API integration, requires more expertise, and only makes sense when a general model doesn’t perform well enough on your specific task.

Building a custom model. Training a model from scratch on proprietary data. Reserved for companies with very specific needs, large data sets, and budgets to match. Most enterprise companies don’t need this and shouldn’t pursue it.

What UK businesses actually pay in 2026

These figures are based on typical project scopes reported across the UK market in 2025-2026. They cover development costs only, not ongoing API usage fees.

Project type	Typical UK cost range	Duration	What drives the variation
AI chatbot or assistant (API-based)	£15,000 – £45,000	6-12 weeks	Complexity of context, integration with existing systems
Document processing / extraction	£20,000 – £60,000	8-16 weeks	Document variety, accuracy requirements, volume
AI integration into existing enterprise app	£30,000 – £90,000	3-6 months	Legacy system complexity, data quality, compliance requirements
Fine-tuned model for specific domain	£50,000 – £150,000+	3-5 months	Data preparation, training infrastructure, evaluation cycles
Internal AI tools (copilots, dashboards)	£25,000 – £70,000	2-4 months	Number of users, data sources, security requirements

Day rates for AI-specialist developers in London currently run £600–£950 per day. Outside London, £450–£700 is more typical. These rates have risen 15–20% since 2023, reflecting demand that has outpaced supply in the UK market.

The hidden costs nobody quotes upfront

The development quote is rarely the final number. Three costs consistently catch UK companies off-guard:

Data preparation. AI systems work with what you give them. If your data is in multiple formats, inconsistently structured, or needs cleaning before it can be used, that preparation adds time and cost that isn’t always included in the initial quote. For document processing projects especially, data preparation can account for 30–40% of total project cost.

Ongoing API costs. Using GPT-4 or Claude at scale isn’t free. A customer-facing AI assistant handling several thousand queries per day can cost £2,000–£8,000 per month in API fees alone, depending on message length and model choice. This is a running cost that needs to be budgeted separately from development.

Human review and iteration. Most AI integrations require a phase of supervised use — where staff review AI outputs and flag errors — before the system runs reliably at full autonomy. That review time has a cost that rarely appears in the development quote but is real and significant.

Build vs API: the decision that drives most of the budget

For most enterprise AI use cases, the right answer in 2026 is to build on top of existing API providers rather than develop proprietary models. The cost differential is significant and the capability gap has narrowed dramatically.

The cases where building custom or fine-tuning makes sense are specific: you have proprietary data that gives you a genuine edge, you’re in a regulated sector where sending data to third-party APIs is restricted, or the general model’s performance on your task is too inconsistent for production use.

Outside those cases, a well-engineered API integration with good prompt design, context management, and error handling will outperform a poorly resourced custom build at a fraction of the cost.

How to scope your AI integration project

Before approaching any development team, being clear on these points will get you a more accurate quote and avoid scope creep later:

What specific task are you automating, and what does success look like in measurable terms?
What data does the AI need access to, and where does that data currently live?
What are the accuracy requirements — and what happens when the AI gets it wrong?
Are there compliance or data security constraints that affect which AI providers you can use?
Who will handle the human review phase, and for how long?

At Yeeply, we work with certified AI development teams across the UK — and for companies with tighter budgets, we also work with Spanish teams offering the same technical expertise at 30–40% lower cost. If you’d like a realistic scoping conversation for your project, request a quote from the top right of yeeply.com/en or write to sales@yeeply.com.