AI implementation
How much does AI implementation cost?
Understand the cost of AI implementation, the main cost drivers, and realistic budget ranges for a sprint, MVP, or wider scale-up.
Updated August 11, 2026 / 7 min read
Quick answer
A useful budget depends on the production scope. At Galactus, a sprint is $5,000, an MVP starts at $20,000, and wider scale-up work is commonly $75,000 to $250,000. Integrations, risk, data, and rollout drive the range.
Use ranges, not one number
A small proof and a production system are different purchases. A proof answers one technical question. A production implementation also handles integrations, permissions, user experience, evaluation, monitoring, failure, and ownership.
For Galactus work, a one-week sprint is $5,000. A focused end-to-end MVP starts at $20,000. Work across several systems, teams, or higher-risk operations is commonly $75,000 to $250,000. These are service ranges, not a universal market price.
The main cost drivers
The model is only one part of the budget. The larger variables are the workflow boundary, the number and quality of integrations, the condition of the data, the risk of a wrong action, and the number of people who must change how they work.
- Scope: one path or several connected workflows
- Systems: standard APIs or brittle legacy access
- Risk: low-impact assistance or regulated decisions
- Data: ready examples or major cleaning and migration
- Rollout: one team or several locations and user groups
Plan for ongoing costs
After launch, budget for model and infrastructure use, monitoring, incident response, evaluation updates, vendor changes, and product improvements. A workflow with low volume can have a small model bill but still need a named technical owner.
Ask for an operating-cost estimate at the expected volume. Also ask how the architecture can reduce cost, such as using smaller models for simple tasks, caching safe results, batching work, and keeping fixed rules outside the model.
Build the business case around the workflow
Measure the current process before you approve the project. Count staff time, delays, errors, missed opportunities, external fees, and management effort. Then set a conservative target for the first release.
Do not use time saved as the only result. A system can also improve response speed, capacity, consistency, visibility, or revenue conversion. Name how the benefit will appear in the business and who will measure it.
Control the budget with stages
Use a short first stage to map the workflow and test the risky technical assumption. Approve the MVP only when the owner, data, integrations, controls, and acceptance criteria are clear.
Keep changes visible. New workflows, user groups, and integrations should be explicit scope decisions. This lets leadership choose between launch speed, features, and cost instead of discovering the trade-off late.
Main points
- Separate a technical proof from a production implementation.
- Integrations, controls, data, and rollout often cost more than model use.
- Include ongoing ownership and operating costs in the business case.
- Use clear stages and acceptance criteria to control scope.
Questions people ask
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, and wider scale-up work is commonly $75,000 to $250,000. The exact cost depends on scope and risk.
What makes an AI project more expensive?
More systems, poor data access, high-risk decisions, custom interfaces, several user groups, strict compliance needs, and unclear ownership all add work and cost.
Are model fees the largest cost?
Usually not for a focused business workflow. Discovery, engineering, integrations, evaluation, change management, and support are often larger cost areas. High-volume workloads can make model cost more important.
How can we control the budget?
Choose one workflow, prove the riskiest integration early, set acceptance criteria, use an explicit change process, and expand only after users show that the first release creates value.