What Does an AI Pilot Program Cost?

Why pilot pricing is so hard to compare

Most vendors quote a pilot without defining what's inside one. Some call a 30-minute demo a pilot. Others quote a full production build as a pilot. The result is a price range that spans from free to six figures depending on who you ask, which makes comparison shopping nearly useless without a shared definition.

A real pilot has a narrow scope, a working system (not a mockup), real data, and a measurable success criterion. It should tell you whether the approach works before you commit to full deployment. That's the frame we use when we scope a pilot, and it's the frame you should use when you evaluate any quote.

What actually drives the cost of an AI pilot

The biggest cost drivers are integration complexity and compliance requirements. A pilot that reads from a CSV and answers questions through a chat interface is cheap to build. A pilot that connects to Epic, pulls patient records, and routes conversations based on intake data is not. The underlying model matters less than the plumbing around it.

For most SMBs, a single-workflow pilot sits between $5,000 and $10,000. That covers discovery, a working prototype on private infrastructure (not a public API wrapper), basic testing, and a handoff document. Timeline is typically 4 to 6 weeks. If you're in a regulated industry, add a compliance layer. HIPAA-covered work requires a signed BAA, a private LLM deployment, and audit logging. That adds $5,000 to $10,000 in setup cost depending on your existing infrastructure. Finance and logistics pilots with SOC 2 Type II requirements follow a similar pattern.

Multi-agent pilots, where two or more AI systems hand off tasks between each other, run closer to $20,000 to $35,000 and take 8 to 12 weeks. Those aren't typical first pilots. We usually recommend starting with one well-scoped workflow and expanding after you've validated the approach.

When the price goes up or down

The price drops when you already have clean, accessible data and a clear success metric defined before kickoff. Pilots stall, and get expensive, when the client is still figuring out what problem they're solving during the build. If you can hand us a documented workflow and sample data on day one, we can move faster and charge less.

The price goes up when your stack is fragmented, your data lives in three systems with no API access, or your legal team needs to review every vendor agreement. HIPAA work adds cost because private LLM deployment (we use Llama 3.1 on your own infrastructure, not OpenAI's shared endpoints) costs more to stand up than a simple API call. That cost is worth it if you're handling protected health information. It's not necessary if you're automating a scheduling workflow for a retail chain.