Bait-and-Bill is when the number that won the deal was never the price — it was the entry fee for a meter you can't turn off. A discovery phase comes in suspiciously cheap. The build is quoted as an estimate. Nine months later you've spent three times the figure that anchored the decision, and every increment was individually reasonable. Nobody lied to you. The pricing model did exactly what it was designed to do.
This is worth naming because it's the mechanism behind a lot of the AI money that vanishes. 95% of enterprise generative-AI pilots deliver no measurable P&L impact (MIT Project NANDA, 2025), and Gartner expects more than 40% of agentic-AI projects to be canceled by the end of 2027, largely on escalating cost and unclear value (Gartner, 2025). Those cancellations aren't mostly technical failures. They're budgets that ran out before the thing shipped.
How the trap is built
It isn't fraud, and it usually isn't even deliberate. It's the natural consequence of three ordinary choices stacked together.
The cheap front door. Discovery is priced low — sometimes free — because it isn't the product. It's qualification, and it's where the switching cost gets manufactured. By the time you have a findings document, the vendor knows your systems better than any competitor could, and starting over means paying for that context twice.
The estimate that isn't a quote. The build is scoped as a range, with the range anchored on the happy path. AI work is genuinely uncertain, so this sounds like honesty — and in a fixed-fee model, that uncertainty is the vendor's problem to price. In time-and-materials, it becomes yours to fund. Same uncertainty, opposite incentive.
The change order treadmill. Every discovery that expands scope becomes billable, and AI projects discover things constantly: the data is messier than the demo suggested, an integration needs an enterprise tier, an edge case needs a human review step. Each change order is defensible on its own. In aggregate they're the actual price.
Five clauses that tell you which model you're in
| What you see | What it sounds like | What it means |
|---|---|---|
| "Estimated" hours or range | Honest about uncertainty | You absorb the overrun, not them |
| Discovery priced far below build | A low-risk way to start | The switching cost is the product |
| No named deliverable for phase one | Agile, adaptive | Nothing to hold anyone to |
| Rates but no total | Transparent pricing | Transparency about the meter, not the bill |
| Change orders billed at full rate | Standard practice | Scope discovery is a revenue line |
None of these is disqualifying on its own — a serious firm can run time-and-materials honestly, and some genuinely exploratory work should be. The signal is the *combination*: a cheap entry point, an uncapped middle, and no single number anyone will commit to in writing.
Why almost nobody publishes prices
Because unpublished pricing is worth money. It lets the number be set after the vendor has seen your budget, your urgency, and how many alternatives you have. That's not a conspiracy; it's just how professional services have always worked, and it's why *request a quote* is the industry default.
It's also why a published price is a genuine signal. A firm that puts real numbers on a public page has given up the ability to charge you more than the next buyer, and has to make the fixed fee work — which means the uncertainty gets priced into their model instead of billed to yours. Our prices are on the site: a Gigabit Agents build is a flat fee from $8,000 per agent, the AI Transformation Sprint is $25,000, and managed operations run $3,000–$20,000 a month. You can compare those to anything else you're considering without booking a call.
The related tell is that the token bill everyone worries about is the cheapest line in the budget — inference is cents per transaction and falling fast. If a proposal spends its pricing section on model costs and stays vague about engineering hours, it's drawing your attention to the rounding error.
What to ask for instead
- A named price for a named deliverable. Not a range for an outcome — a number for a thing, with a date.
- A fixed-price first phase that ends in something deployed. If phase one produces a document, you've bought a document.
- The total, not the rate. A rate card describes the meter. Ask what the whole engagement costs if nothing goes wrong, and what it costs if the two most likely surprises happen.
- Who eats the overrun, in writing. This single question separates the models faster than anything else on the list.
- What the exit looks like. Whether you own the code, the prompts, the evals, and the infrastructure — and how long a handover takes.
If a vendor won't answer those in writing before contract, that is the answer. The nine questions to ask any AI agency covers the rest of the diligence, and our comparison against traditional consultancies lays out where the money actually goes in each model.
