"How much does it cost to add AI to our software?" is a question I get almost weekly now. The honest answer has two halves — the build cost, and the ongoing cost that scales with usage — and most estimates only mention the first, which is how companies get surprised by their AI bill three months after launch.
We build AI into real products (Mebag runs on OpenAI), so here's the honest breakdown, both halves.
The short answer — build cost
| What you're adding | Realistic cost | Timeline |
|---|---|---|
| Simple AI feature (a single capability via an API — summarise, classify, generate) | $8K–$25K | 4–10 weeks |
| Meaningful AI integration (AI woven into core workflows, some custom logic) | $25K–$70K | 10–20 weeks |
| AI agent / complex capability (autonomous tasks, tool use, validation layers) | $40K–$120K | 16–32 weeks |
| Custom/fine-tuned models or heavy AI product | $90K–$250K+ | 24+ weeks |
Build cost only. The ongoing API cost is separate and covered below — don't skip it.
What drives the build cost
How deeply the AI is integrated. Bolting a "summarise" button onto an existing app is cheap. Weaving AI through your core workflows — where it reads your data, makes decisions, and feeds results back into the product — is a real engineering project. Depth of integration is the biggest build-cost driver.
Off-the-shelf model vs custom. Most business AI today uses existing models (OpenAI, Anthropic, others) via API — far cheaper and faster than training your own. Custom or fine-tuned models cost dramatically more and are only worth it when off-the-shelf genuinely can't do the job. For most businesses, using a frontier model via API is the right and affordable choice. Don't let anyone sell you a custom model you don't need.
Validation and guardrails. Because AI can be confidently wrong, the engineering that checks its output — sanity checks, confidence thresholds, human-in-the-loop, fallbacks — is a real part of the cost. For anything where errors matter, this isn't optional, and skimping on it is how AI features embarrass you in production.
Data readiness. AI is only as good as the data you feed it. If your data is messy, scattered, or unstructured, getting it AI-ready is often a bigger job than the AI integration itself. This is the hidden cost that surprises people most.
The "boring foundation" tax. If your existing software has weak architecture, adding AI on top exposes it. Sometimes the honest answer is that the foundation needs shoring up first — the AI sits on top of solid software, and weak foundations can't hold it.
The half nobody tells you about: ongoing API cost
Here's what catches companies out. Every time your AI does something, it's an API call, and API calls cost money that scales directly with usage. The more your product is used, the bigger the bill. Success costs you more.
A single AI operation costs a fraction of a cent — which feels free. Multiply it by every user, every action, running continuously across your whole user base, and it becomes a real monthly line item that grows as you grow. We've watched founders build a cheap AI feature and get blindsided by the running cost.
The disciplines that control it — and that a good team builds in from day one:
- Caching — don't pay to compute the same thing twice
- Batching — group operations where timing allows
- Right-sizing the model — don't use your most expensive model for a job a cheaper one handles fine
- Real-time monitoring — see a cost spike the day it happens, not at month-end
AI cost management is an engineering discipline. Factor the ongoing cost into your model from the start, because a cheap build with an expensive running cost is not a cheap product.
Where companies waste money adding AI
Adding AI because it's trendy. The most expensive AI is the AI you didn't need. If a few business rules would do the job more reliably and cheaply, use the rules. Not every problem wants a probabilistic answer.
Building custom models unnecessarily. Frontier models via API cover the vast majority of business needs at a fraction of the cost. Custom models are rarely the right first move.
Ignoring the running cost until it hurts. Covered above — it's the number one surprise. Model it upfront.
Skipping validation. An AI feature with no guardrails will eventually produce a confidently wrong output in front of a customer. The validation layer isn't where you cut corners.
How to get your real number
Define the specific job. Not "add AI" — what exact task, on what data, producing what? Precision here is most of the estimate.
Check your data readiness. Is the data the AI needs clean, structured, and accessible? If not, that's part of the cost.
Decide how deep. A bolt-on feature or AI woven through the product? This swings the number most.
Estimate the usage. How often will the AI run, across how many users? That drives your ongoing cost, which matters as much as the build.
Then a good team can give you both numbers — build and running — which is the only complete answer.
Muhammad Nabeel is the co-founder of Teamseven. We build AI into real products — including Mebag, on OpenAI. Book a free consultation and we'll give you an honest number for both the build and the running cost.