What an AI integration project actually costs in 2026.
Honest pricing for adding AI to a product that already has users. The bands a senior shop will quote, the variables that move the price, and the parts founders consistently underestimate.
TLDR
A real AI integration into a shipped product runs €25K–€80K, six to twelve weeks. Below €15K it's a Streamlit demo or LLM wrapper. Above €100K you're paying for an org chart, not the integration. Most of the price is in evaluation, not in the model call.
Founders ask "how much will it cost to add AI to my product" the same way they used to ask "how much will it cost to add a mobile app." The honest answer in both cases is "what kind, and what state is your data in." The honest answer in 2026 is bounded enough to put numbers on, which is what this post does.
What follows is the pricing reality at a senior shop, not a junior contracting rate. The numbers reflect production-grade work: a feature that ships, has evaluations behind it, has cost controls, and does not embarrass you when it is wrong on a customer call. The bands below are what a senior boutique typically quotes across the engagement shapes I have scoped or shipped in the last 18 months.
Where AI integrations actually land.
Most projects sort into one of four shapes. The price band is set by the shape, the data state, and how much of the existing product needs to change.
Retrieval over your own data
The model answers user questions by retrieving relevant context first, then generating against it. Price moves on data quality. Tidy markdown corpus = lower band. Five sources nobody touched in two years = upper band. The expensive part is retrieval and re-ranking, not the LLM call.
Agentic workflows
Agent takes user intent, executes multi-step workflows. Support triage, ops automation, sales follow-up with judgment. Price moves on action surface. Each action needs an evaluation, error handling, an audit trail, and a rollback story.
Evaluation harness
Often the highest-leverage AI work a company can pay for. Turns a black box into something measurable. Test set, scoring rubric, CI hook, and dashboard. Tighter band because the deliverable is concrete.
Model migration off vendor lock
Existing AI feature pinned to one provider. Pricing or SLA pressure forces migration. Benchmark, parity test, prompt portability, fallback strategy, cost rebalance. Successful migrations commonly cut inference cost by 35–60 percent.
Six factors, signed.
These move the band more than anything else. Down arrow = lower band; up arrow = upper band.
Anatomy of a shipped AI feature.
Across a hundred quotes, the same four cost categories get under-priced when teams plan their own builds.
- ImplementationThe core build. The model call, the integration, the UI. ~45%
- EvaluationTest set, regression harness, human review cycle. Skipped = ships, degrades in 3 weeks. ~25%
- Failure pathsWhat happens when LLM down, returns malformed output, or generates content the user shouldn't see. ~13%
- Cost controlsToken budgets, caching, batching, model routing. Pays back in Q1. ~10%
- Vendor lock decisionChoosing a provider is a 6-month commitment. The audit before the choice matters more than the integration. ~7%
The honest price corridor.
Below the floor or above the ceiling, the quote is hiding something specific.
Below €15K is hiding
A wrapper around the OpenAI API with a prompt in it, an open-source RAG template configured for your data with no evaluation, or a Streamlit demo that will never make production. Any of these can be the right move if you know that's what you're buying. None is an AI integration into a shipped product.
Above €100K is hiding
An org chart. There's no AI feature category that needs twelve months of senior engineering at a four-engineer team rate to ship. Walk.
Two mechanisms keep the price honest.
Built into every QuantaLynk SOW. Both work elsewhere too.
Fixed scope, written change orders.
Time and materials encourages exploration that does not ship. Pin the scope. Write down what done means. Pay against milestones, not hours. Material new scope is a written change order with a price delta agreed before work resumes.
20% milestone holdback at acceptance.
Released only after agreed evaluation criteria are met in production, not in staging. This single mechanism eliminates the "demo works, production does not" failure mode more reliably than any other contracting choice.
Have an AI integration to scope.
I run an AI integration practice through QuantaLynk. From €25K, fixed scope, four to ten weeks. The studio sits at the lower end of the bands above because it operates from Ahmedabad with a senior solo + AI delivery shape. Discovery call is 30 minutes. We'll walk through the four shapes and figure out which one your project is.
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