Business & LegalSome revenue models are traps: know why you refuse a success fee
Ex-Microsoft engineer reading every public tender in Romania
Customers keep offering Mihai a seductive model: charge nothing up front, take a percentage when the tender is won. He refuses, and his reasoning is worth copying. The contract values are public money, and a private platform skimming a percentage of state contracts invites legal scrutiny, reputational damage, and misaligned incentives to push clients toward the biggest tenders rather than the right ones. If he ever prices on outcomes it will be a fixed fee per successful assisted tender bundled into a subscription, never a percentage. The general discipline is to stress-test a revenue model against the source of the money and the behavior it rewards, not just against revenue potential, and to be able to articulate exactly why you turned down the model everyone suggests. A pricing structure is a position you have to defend for years.
Related advice
Mihai NegreaPrice against the manual workflow you replace, not the software you wrote
Mihai's first serious customer was a security firm whose tender discovery process was a bookmark folder organized by county and an employee who opened town hall websites every single day. They happily paid 2,000 lei per month for complete automated coverage, a hundred times his 19 lei list price at the time, because the alternative was a salary. The lesson is that software is not priced against its build cost or against other software, it is priced against the manual grind it replaces. Find the customer who is already paying a human to do the thing badly, and the budget already exists; you are not creating a line item, you are shrinking one. Before setting a price, ask what the current workflow costs in hours and salaries, and anchor there.
Mihai NegreaCloud egress, not compute, is what eats a data-heavy product's margin
Mihai's Azure bill was not dominated by the compute he had planned for, but by data movement he had never priced: documents flowing from the procurement system into processing, into storage, back out to jobs and users, culminating in a 700 EUR ingress charge in a single month. Hyperscaler pricing makes exactly this traffic expensive while keeping headline compute prices attractive, which is why his migration to Hetzner dedicated servers and S3-compatible storage, plus a self-hosted OCR pipeline on a local GPU, cut a 2,500 to 3,000 EUR monthly burn to a fraction. If your product's core loop is moving files around, model the traffic before choosing infrastructure, because the cloud's convenience premium applies to every gigabyte, every direction, forever. Data-heavy products are precisely the ones where owning the pipes pays.
Mihai NegreaPublish your prices in a market that hides behind contact forms
Every plan on DataDriven has a public price, topping out at 290 lei per month, in a category where the incumbent reflex is "contact us for pricing." For small firms deciding whether to try a tool, the hidden price is a wall: it signals enterprise sales calls, negotiation, and wasted time, exactly what a two-person catering company will not endure. Transparent pricing lets the product sell itself to the long tail the incumbents ignore, and it filters out nobody who was actually going to pay. Mihai treats it as strategy, not cosmetics: his revenue is built from hundreds of small subscriptions rather than a handful of negotiated contracts, and that model only works when the price is on the page. If your competitors hide their prices, publishing yours is free positioning.
Mihai NegreaA gaming GPU on solar power can replace a cloud AI service
DataDriven needs industrial OCR, every tender document in Romania, scans included, and cloud pricing made that a luxury: Azure Document Intelligence billed per page, and GPU instances rent at rates designed for venture budgets. Mihai's replacement is a single RTX 3090 in his office running Tesseract plus his own quality-validation classifiers, processing about 1,500 pages per hour, powered by the solar panels on the roof, so the marginal cost per page is approximately zero. A consumer GPU is not a data center, but a solo founder's workload rarely needs one; it needs sustained, predictable throughput on a task that tolerates batch processing. When an AI API is your biggest variable cost, price out the unfashionable alternative of one good machine you own, because the payback period is often measured in weeks.