How we price generative AI projects
We do not publish a price list. Two projects with the same name can differ widely in scope, and a number quoted before anyone has seen your data is a guess. This page explains what moves the cost and how you get a real estimate.
What sets the cost of a build
- How many systems it connects to. Each source system has its own format, permissions, and freshness problems.
- How ready your data is. Cleaning, structuring, and applying access rules to data takes work before any model is involved.
- What a wrong answer costs. The higher the cost, the more evaluation and human review the system needs.
- Where it must run. A hosted model API is simpler than a deployment inside your own cloud account or data center.
- How many people use it, and how fast it must answer. Volume and latency targets shape the design.
- Whether it only writes or also acts. A system that takes actions in other tools needs more safeguards than one that produces text.
What you pay after launch
A build price is one part of the cost. Model usage, hosting, monitoring, and continuing evaluation carry on after launch and grow with use. Ask for a running-cost estimate alongside any build price, from any vendor, including us. Our post on how to choose a generative AI development company lists the other pricing questions worth asking, such as what happens when the scope changes and what you own at the end.
How you get a number
- Send project details or book a call. Tell us what you want to build and where the data lives.
- Discovery follows, with a planning range of one to two weeks. It ends with a roadmap. That is where the guess becomes a plan, and the estimate rests on it.
- If building is the wrong answer, the roadmap says so, and you have spent a discovery phase instead of a build.
The process page describes each stage. Each service page lists what we need from you to start.