Adding AI to an app is one of the most common requests our UK app and SaaS development studio gets in 2026. And almost every founder budgets for only half of it. Unlike a normal feature you build once, AI has an ongoing cost that grows with usage. Here’s the honest, full picture — both cost buckets, real ballpark numbers, and how to keep the bill sensible.
The two cost buckets
- Build cost (one-off): designing and engineering the feature — the interface, the prompts, connecting to a model, handling errors, testing quality.
- Running cost (ongoing): what you pay the AI provider every month, mostly billed per token (roughly, per chunk of text going in and coming out). This scales with usage.
Miss the second one and a successful launch becomes a budgeting shock. Let’s size both.
Build cost by AI feature
Ballpark UK build costs to add a well-made AI feature to an existing app (2026):
| AI feature | Typical build cost | Notes |
|---|---|---|
| Simple chatbot / assistant | £3,000 – £6,000 | FAQ-style, off-the-shelf model |
| RAG search over your content | £6,000 – £15,000 | Answers grounded in your data |
| Summarise / draft / extract | £4,000 – £10,000 | Emails, documents, notes |
| Recommendations / personalisation | £5,000 – £12,000 | Depends on data available |
| AI agent (multi-step tasks) | £12,000+ | More testing, guardrails, oversight |
These assume you already have an app to add to. For a fuller picture of AI feature strategy, see which AI features actually move SaaS revenue, and our AI app development service page.
Running costs: the part founders forget
Most AI features call a large language model API and are billed per token. The exact price depends on which model you use — and there’s a huge range. A small, fast model can be orders of magnitude cheaper per request than a top-tier one, so the single biggest lever on your running cost is choosing the right model for each job.
What drives your monthly bill:
- Usage volume — how many requests your users make. This is why cost scales with success.
- Model choice — premium models cost far more per token than smaller ones.
- Context size — how much text you send in each request (long documents, chat history) adds up fast.
- Output length — longer generated responses cost more.
A worked example
Say you add an AI assistant used by 1,000 active users, averaging 20 messages each per month. That’s 20,000 requests. On a small/efficient model with tight prompts, that might cost tens of pounds a month. On a premium model with long context and verbose answers, the same feature could cost several hundred — or more. Same product, very different bill, decided almost entirely by engineering choices. That gap is exactly where good build decisions pay for themselves.
How to control running costs
- Right-size the model. Use a smaller model by default; reserve the expensive one for genuinely hard requests (“model routing”).
- Cache. Identical or similar requests shouldn’t hit the model twice — cache answers where you safely can.
- Trim context. Send only what’s needed, not the entire chat history or document every time.
- Set limits. Per-user rate limits protect you from runaway costs and abuse.
- Measure quality. Evaluate whether the cheaper model is actually good enough — often it is.
Done properly, these routinely cut AI running costs several times over with no visible difference to users. It’s one of the clearest cases where the way a feature is built directly decides what it costs to run.
Build vs buy
For most startups, calling a hosted model API (OpenAI, Anthropic, Google, or an open-source model via a provider) is the right, cheapest-to-start choice. Self-hosting an open-source model only makes sense at real scale or with strict data-residency needs — it trades API fees for infrastructure and expertise. Start with an API; revisit self-hosting if and when the volume justifies it.
Frequently asked questions
Is there any AI cost even before I have users?
Very little — running cost scales with usage, so pre-launch and early on it’s usually negligible. The main spend at that stage is the one-off build. The running cost becomes something to watch as adoption grows, which is a good problem to have if you’ve engineered it sensibly.
Can I add AI to my MVP without blowing the budget?
Yes — pick one high-value AI feature, use an efficient model, and set sensible limits. You can get a quick overall estimate with our MVP cost calculator, then we can scope the AI part precisely.
The bottom line
Budget for both halves: a one-off build (roughly £3k–£15k+ depending on the feature) and an ongoing running cost that scales with usage but is highly controllable with good engineering. Get those decisions right up front and AI becomes an asset, not a runaway line item. If you want a precise build-and-run estimate for a specific AI feature, that’s exactly what we’ll give you on a free discovery call.