How much does an MVP cost in Canada? An honest breakdown
Rough price bands for Canada, the five decisions that move the number most, and why answering the expensive questions in discovery is the biggest discount available.
Founders ask this question expecting a number, and anyone who gives them one without asking questions back is guessing. But "it depends" is not an answer either. So here is what I can say from running a product studio in Vancouver: the ranges I see quoted in Canada, and the handful of decisions that move the price more than anything else.
The ranges, with the caveats attached
These are orientation, and anyone who quotes you without asking questions is guessing. A clickable prototype or a no-code MVP, built to test whether anyone wants the thing, usually lands in the low five figures in Canadian dollars. A coded MVP from a freelancer or a small studio, with real accounts and one core workflow done properly, is usually in the tens of thousands. A full agency build with a dedicated team, design system, integrations, and a launch plan runs well into six figures, and it is not unusual for it to keep climbing after launch.
Those bands overlap because the label "MVP" covers everything from a landing page to a regulated fintech product. The number you are quoted says more about which of those you described than about the market rate. So the more useful question is what is driving the number.
The five decisions that move the price
The first is how many user types the product serves. A single-sided tool has one set of screens, one onboarding flow, and one set of edge cases. A two-sided marketplace like Dwell, with renters, landlords, and property managers, roughly triples all of that before anyone talks about the matching logic in the middle.
The second is whether the product touches money or regulated personal data. The moment it does, the cost stops being about screens and starts being about verification and audit trails. Dwell's bank-verified rent history through Flinks is the whole point of the product, and it is also the single most expensive line in the build.
The third is AI. It is now cheap to add a model call and expensive to make it trustworthy. ShiftFlow generates rosters with an LLM, but the compliance layer around union rules and guaranteed hours is deterministic code, because a silent AI mistake there is a contract breach, not a bug. That split is where the engineering time goes. A demo that "uses AI" takes a weekend. A product where the AI can be wrong without hurting anyone takes months.
The fourth is hardware. I have never seen a hardware estimate that was off by a percentage. They are off by multiples. PavIQ's sensor box has a bill of materials around CAD $580 per unit and GridSentinel's node is closer to $95, but the unit cost is the smallest part. Enclosures, certification, field testing, and the firmware that has to survive a 72-hour LTE outage without losing data are where the budget actually lives.
The fifth is how much is still unknown when the build starts, and this one outweighs the other four. Every open question that gets answered mid-build gets answered by an engineer, at engineering rates, with code already written the wrong way. That costs several times what it would have cost to answer in discovery.
How discovery lowers the bill
When Squint started, it was framed as a developer QA tool. Discovery with agencies revealed that the thing they would pay for was the persuasive, competitor-benchmarked report, not the diagnostics. That reframing cut an entire category of features from the build and turned a commodity into something with a $79 to $249 per month willingness to pay attached. The product got cheaper to build and more valuable at the same time. That happens more often than you'd expect when positioning gets fixed before code.
The GridSentinel re-scope is the same story in hardware. Finding out during technical discovery that theft detection couldn't work on a medium-voltage line saved an entire field-trial cycle that would have proven it the expensive way.
What to ask anyone who quotes you
Ask what they assumed about user types and integrations, and whether those assumptions are written down. Ask what happens to the price when one of them turns out to be wrong, because one of them will. Ask whether discovery is included or skipped. And ask to see a case study that documents what went wrong, not only what launched. If every project on their site is a highlight reel, the estimate probably is too.
We publish our case studies as specs with the unresolved parts left in. Partly that is because it is more useful to read. Partly it is because it keeps our own estimates honest. Our own work with clients stops at the point where the expensive questions are answered: a discovery sprint, a prototype, and a spec your engineers can build from. If you are trying to size a build and want a second opinion on the assumptions behind a quote, that is a conversation I am happy to have before any money changes hands.
Sources and further reading
- PavIQ case study. Full bill of materials and the 20-week plan behind the hardware numbers.
- ShiftFlow case study. Where the deterministic-versus-AI split is documented.
- Squint case study. The repositioning that cut the build.