A realistic look at maintenance budgets so you're not surprised a year after launch.
Key takeaways
As a starting point, many custom AI tools cost somewhere in the range of fifteen to twenty-five percent of their original build cost per year to maintain properly. Simpler tools sit lower, complex agentic ones sit higher.
This figure should be treated as a planning input, not a guarantee, since actual cost depends heavily on how much your underlying data and processes change.
Maintenance budgets often blur two very different costs: the infrastructure the tool runs on, and the developer time spent improving or fixing it. Keeping these separate makes it easier to spot when one is creeping up unreasonably.
Infrastructure costs tend to be predictable; development time is where scope creep usually hides.
Beyond bug fixes, a healthy maintenance plan includes time for someone to periodically check the tool's outputs against real cases and flag any drift, not just respond when something visibly breaks.
This proactive check is usually a small line item, but it's the one that catches slow, silent degradation.
A tool used lightly by one team can outgrow its original scope quickly once adoption spreads across the business. Revisiting the maintenance agreement as usage scales avoids a mismatch between support and actual load.
Doing this proactively, on a schedule, tends to go far more smoothly than doing it reactively after a slowdown or outage.
A 30-minute call is enough to tell you whether AI pays for itself here.
