A production AI agent — one agent, one high-value workflow, deployed in your stack with evals and observability — costs $50,000–$150,000 and takes about 90 days. A scoped single-workflow automation runs $8,000. Ongoing operation runs $3,000–$20,000 a month depending on how many systems you keep healthy. Those are our published prices, and they track what serious mid-market engagements cost across the industry.
The wide range between $50K and $150K isn't padding — it's driven by four things. Integration surface: an agent that reads one system and writes to another is the floor; one that orchestrates five tools with human approval steps is the ceiling. Data readiness: if the knowledge the agent needs lives in clean, queryable systems, you save weeks; if it lives in inboxes and PDFs, budget for the pipeline. Risk profile: a customer-facing agent needs guardrails, evals, and escalation paths an internal tool doesn't. Operating bar: 'works in a demo' and 'runs unattended at 2am' are different products.
Where teams overspend is rarely the model bill. Inference for a mid-market workflow typically lands at $200–$2,000 a month — real, but a rounding error next to engineering time. The expensive failure modes are paying a consultancy $400 an hour for a readiness deck before anything is built, or paying twice because a demo-grade build from an automation shop collapsed under real load and had to be rebuilt.
Where teams underspend is what happens after launch. Models drift, vendors ship breaking changes, edge cases accumulate. An agent without managed operations degrades quietly until someone notices a month of bad outputs. Budget the retainer — $3K–$20K a month — as part of the cost of owning the system, the way you'd budget hosting for software.
If you're not sure the workflow justifies the spend, don't start with the build. Run the numbers first: our AI ROI calculator turns hours-per-week and loaded cost into an annual savings range and payback period per engagement. A workflow burning 20 hours a week at $75/hour costs ~$78,000 a year; at 50–70% automatable, a $90K agent pays back in well under two years — and most of what we deploy pays back faster.
The cheapest way to de-risk the whole decision is a fixed-price diagnostic. Our AI Transformation Sprint is $25,000 for two weeks: workflow mapping, ROI modeling, build-vs-buy, and a deployed pilot — and it credits toward the build it scopes. You spend a bounded amount to learn exactly what the real thing costs before committing to it.
One honest caveat: if AI is the core of your product and you can hire senior AI engineers, building in-house wins long-term — we say the same in our build vs buy vs embed guide. The forward-deployed model exists for the other case: the workflow is costing you money now, your engineers are busy shipping your product, and you want one production agent doing real work this quarter.
