AI budgets go wrong in predictable ways — usually by counting only the software and getting surprised by everything else. Budgeting well means seeing the full picture and favoring costs you can forecast. Here’s how, and how dgm makes it predictable. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)
What to budget for
The full AI project, not just the license:
- Assessment — the upfront work to scope and prioritize.
- Implementation and integration — often the largest line.
- Model / usage costs — the per-use cost of the models.
- Data preparation — getting your data ready.
- Training and change management — driving adoption.
- Ongoing operation — maintenance and improvement after launch.
(See AI total cost of ownership.)
Avoid the surprises
The budget-busters are usually per-seat pricing (rises with adoption) and usage-based pricing (rises with volume) — both can balloon past your initial figure. Ask what’s included vs billed separately, and favor fixed pricing you can forecast. A scoped first use case also keeps the initial budget small and controlled.
Don’t forget the savings side
For a true net figure, offset the budget with expected savings — consolidated SaaS subscriptions and recovered staff time. A project that replaces several tools can have a far lower net cost than its gross budget (see how to measure AI ROI).
Start small
Keep the first budget small by starting with one scoped, high-ROI use case rather than a sprawling program — then fund expansion from proven results (see how to pick the right AI use case first).
How dgm helps
dgm uses fixed, public pricing — a $399 one-time assessment and $3,999/month implementation, no per-seat fees — so you budget with certainty rather than facing opaque quotes or per-seat surprises, and consolidation often lowers the net figure. If you’d rather explore the platform yourself first, go straight to osFoundry; if you want a predictable AI budget, that’s where dgm comes in.