AI implementation
How much does AI implementation cost?
What moves the cost of AI implementation, and how we price a $5,000 sprint, a $20,000+ MVP, or a larger scale-up.
Updated / 3 min read
Short answer
The price follows the production boundary. At Galactus, a sprint is $5,000, an MVP starts at $20,000, and larger scale-up work usually costs $75,000 to $250,000. Integrations, risk, data, and rollout decide where you land.
First decide: proof or production
A small proof and a production system are different purchases. A proof answers one technical question. A production system also has to handle integrations, permissions, the user interface, testing, monitoring, failures, and ownership.
Our prices: a one-week sprint is $5,000. A focused end-to-end MVP starts at $20,000 and takes four to eight weeks. Work across several systems, teams, or higher-risk operations usually costs $75,000 to $250,000 and runs two months or more. We agree the price before work starts. These are our ranges, not a market rate.
Integrations, risk, and rollout move the price
The model is one part of the budget. What moves the number is how wide the workflow is, how many systems it touches and what shape they're in, the state of the data, the cost of a wrong action, and how many people have to change how they work.
- Scope: one path, or several connected workflows
- Systems: standard APIs, or brittle legacy access
- Risk: low-stakes help, or regulated decisions
- Data: ready examples, or heavy cleanup and migration
- Rollout: one team, or several locations and user groups
Budget for the year after launch
After launch you pay for model and hosting usage, monitoring, incident response, test updates, vendor changes, and improvements. A low-volume workflow can have a tiny model bill and still need a named technical owner.
Ask for an operating cost estimate at your expected volume. Ask how the design keeps that cost down: smaller models for simple tasks, caching safe results, batching work, and keeping fixed rules out of the model.
Measure today's process first
Measure the current process before you approve the project. Count staff hours, delays, errors, missed opportunities, outside fees, and management time. Then set a conservative target for the first release.
Time saved shouldn't be the only number. A system can also speed up replies, add capacity, make output consistent, give you visibility, or lift conversion. Say where the benefit will show up and who will measure it.
Buy it in stages
Use a short first stage to map the workflow and test the riskiest technical assumption. Approve the MVP only once the owner, data, integrations, controls, and acceptance criteria are clear.
Keep changes visible. Every new workflow, user group, or integration is a scope decision. Leadership then chooses the trade-off between speed, features, and cost early, instead of finding it late.
Key points
- A technical proof and a production system are different purchases.
- Integrations, controls, data, and rollout often cost more than the model.
- Put ongoing ownership and running costs in the business case.
- Use stages and acceptance criteria to keep scope in check.
Common questions
How much does an AI implementation cost?
At Galactus, a one-week discovery or proof sprint is $5,000. A focused MVP starts at $20,000. Larger scale-up work usually costs $75,000 to $250,000. Scope and risk set the exact number, and we agree it before work starts.
What makes an AI project more expensive?
More systems, hard-to-reach data, high-risk decisions, custom interfaces, several user groups, strict compliance, and unclear ownership. Each one adds work.
Are model fees the largest cost?
Usually not, for a focused business workflow. Discovery, engineering, integrations, testing, helping people change how they work, and support tend to cost more. Model fees matter more at high volume.
How can we control the budget?
Pick one workflow. Prove the riskiest integration first. Write down acceptance criteria. Treat every change as an explicit decision. Expand only after users show the first release is worth it.