The honest answer to what a SaaS MVP costs is between $40,000 and $400,000, which is useless without knowing what drives the range. It is not primarily the feature count. It is the number of integrations, whether you need multi-tenancy on day one, the compliance surface, and whether anyone has decided what the product actually does. Here is how the number gets built and where it gets out of control.
01Realistic ranges by product shape
These assume a competent senior team, production-quality code with tests and CI, and a product that real customers will use rather than a demo. They exclude your own staff costs and ongoing infrastructure.
| Product shape | Offshore/hybrid | Onshore | Typical duration |
|---|---|---|---|
| Single-tenant internal tool | $35k - $70k | $120k - $220k | 2 - 3 months |
| Standard B2B SaaS, light integrations | $60k - $120k | $200k - $400k | 3 - 5 months |
| Multi-tenant SaaS with billing + SSO | $100k - $180k | $320k - $600k | 4 - 7 months |
| Regulated (health, fintech) | $150k - $300k | $450k - $900k | 6 - 10 months |
| AI-heavy product with data pipeline | $130k - $280k | $400k - $800k | 5 - 9 months |
02What actually drives the cost
Founders usually estimate by counting features, which correlates weakly with effort. The cost drivers below are what an experienced estimator actually looks at, and most of them are architectural rather than functional.
- Integrations: each meaningful third-party integration is 1-3 weeks, more if the API is poor.
- Multi-tenancy: retrofitting is expensive, so decide before the first schema.
- Auth complexity: email and password is cheap, enterprise SSO and SCIM provisioning is not.
- Billing: usage-based metering costs several times what flat subscription billing costs.
- Compliance: SOC 2, HIPAA or PCI scope adds meaningful engineering and process work.
- Data volume: anything requiring real pipeline engineering rather than a database.
03Where budgets go wrong
The most common failure is not underestimating the build. It is spending the entire budget on the build and having nothing left for the six months afterwards, when you actually learn what the product should have been.
An MVP that ships and then cannot be iterated is worse than a smaller MVP with runway behind it. Budget the build at roughly 50-60% of what you can spend, and hold the rest for the iteration cycle that follows first contact with users. That is where product-market fit gets found, and it is never in the original specification.
- Reserve 40-50% of total budget for post-launch iteration.
- Budget infrastructure separately - typically $200-$2,000/month at MVP scale.
- Expect 15-20% of engineering cost as your own management overhead.
- Assume the first version of the pricing model is wrong.
04Scope decisions that genuinely save money
Cutting features is the obvious lever and usually the least effective one, because the expensive parts are rarely the visible ones. These decisions save real money without making the product worse.
Buy rather than build anything that is not your differentiator. Authentication, billing, email delivery, file storage, search, analytics and error tracking are all solved commodities. Building them yourself is a common way to spend a third of an MVP budget on undifferentiated infrastructure.
| Capability | Recommendation | Rough saving |
|---|---|---|
| Authentication and SSO | Buy | 3 - 6 weeks |
| Subscription billing | Buy | 4 - 8 weeks |
| Transactional email | Buy | 1 - 2 weeks |
| Search | Buy unless core | 2 - 5 weeks |
| Admin panel | Generate or buy | 2 - 4 weeks |
| Your core domain logic | Build | - |
05How to read a quote
A quote without stated assumptions is not an estimate. When comparing proposals, the range and the assumptions tell you more than the headline figure - two vendors quoting wildly different numbers are usually solving differently sized problems, not offering different value for the same work.
Ask each vendor what they would descope to hit 70% of the quoted number. The answer reveals whether they understand which parts carry the value, and it gives you a genuine option rather than a binary decision.
- Are assumptions stated, and are they realistic?
- Is testing, CI and documentation included or extra?
- What is the change-request rate relative to the base rate?
- What is explicitly out of scope?
- What would they cut to reach 70% of this number?
Topics
Priya Iyer
Staff Engineer · SyncTrix
Writes about the engineering decisions behind production systems - architecture, delivery and the trade-offs that only show up at scale.
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