A production AI feature costs $25,000 to $120,000 to build in the US, plus ongoing model spend that depends entirely on usage. A retrieval-based assistant over your own documents typically runs $30,000 to $70,000 including evaluation. An agent that takes actions in your systems starts higher because permissions, logging, and failure handling are most of the work. Prototypes cost a fraction of these numbers, which is exactly why they mislead teams about timelines.
Prototype
$5,000 – $20,000
A working demo on sample data. Useful for a decision, not for customers.
Production feature
$30,000 – $70,000
Retrieval over your data, evaluation harness, guardrails, monitoring, and cost controls.
Agent or multi-system workflow
$70,000 – $200,000+
Tool integrations, permission scoping, human handoff, audit logging, and failure-mode testing.
The cheaper option
Before building, check whether a configured product already does this. A large share of AI requests are met by a SaaS tool at a fraction of the cost, and a good partner will tell you when that is the case.
Want a number for your actual scope? Tell us what you need built and get a matched team in 48 hours, $0 until you hire.
Because it skips evaluation, guardrails, monitoring, and the edge cases that appear with real users. That gap is where AI budgets are lost.
Anywhere from tens to thousands of dollars depending on volume and model choice. It should be modelled before build, not discovered after launch.
Yes, with standard architecture choices: no training on your data, processing inside your cloud account, and full audit logging. Ask for those terms explicitly.
Tell us the scope. You get matched engineers and a real number in 48 hours, $0 until you hire.
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