"What will this cost?" is the first question we get, and the honest answer is that it depends — on scope, on the state of your data, and on how deeply the solution has to plug into what you already run. That said, "it depends" is a cop-out without a framework, so here is how AI consulting is actually priced, what drives the number, and the budget ranges we see most often.
The three ways AI consulting is priced
Almost every engagement maps to one of three pricing models. Good consultants will tell you which one fits your work and why, rather than forcing everything into a single structure.
| Model | How it works | Best for |
|---|---|---|
| Fixed project fee | One price for a defined scope and deliverable | A clear, bounded build with known requirements |
| Monthly retainer | Set fee for ongoing work and support | Continuous AI development and iteration |
| Day rate / advisory | Billed per day for strategy and guidance | Roadmaps, audits, and decision support |
What drives the price up or down
Two AI projects that sound similar in a sentence can differ by an order of magnitude in cost. Five factors explain most of the spread:
Scope and use-case complexity. A single, well-defined workflow — say, an assistant over one document set — is far cheaper than a multi-step agent system touching several parts of the business.
The state of your data. If your data is clean and accessible, work starts immediately. If it's scattered across systems and spreadsheets, the data preparation can be a larger line item than the AI itself.
Integration depth. A standalone tool is cheaper than one wired into your existing systems, security model, and user workflows — and integration is usually where the durable value is.
Security and compliance. Investment firms have real requirements around data privacy and confidentiality. Architectures that keep your data private and auditable cost more to build but are non-negotiable.
Build vs. ongoing support. A one-time build is a single number; production systems that you depend on need maintenance, monitoring, and iteration, which is what retainers cover.
Budget ranges we see most often
With the caveat that every engagement is scoped individually, these are the ranges we encounter in the market in 2026. Treat them as orientation, not a quote:
A scoped pilot — one use case, taken to a working system — commonly lands in the low-to-mid five figures. A production-grade integration, wired into your systems with the security and reliability a firm actually needs, frequently runs from the mid-five figures into six figures depending on depth. Ongoing retainers for continued development and support range from a few thousand to tens of thousands per month based on intensity. Advisory and roadmap work is typically billed at a day rate.
How to budget so the project ships
The most expensive AI project is the one that never reaches production. The way to avoid that is to scope to a single concrete use case with a measurable outcome, fund a small paid pilot, prove the value, and then expand — rather than committing a large budget to an open-ended "AI transformation" before anything works. Tie the spend to a success metric from the start, and the cost question largely answers itself: you're buying a result, not a science project.