A proof of concept with Dignep takes 4–8 weeks to build, and its cost depends on three things: how many systems it has to touch, whether it needs AI and real data, and how clearly you can state what “it works” means before we start. A narrow PoC with one integration costs far less than one that tries to prove several things across several systems.
On this page
- What is a proof of concept, and what isn’t it?
- How much does a PoC cost?
- What drives the cost of a PoC?
- Where does the money go during a PoC?
- How can you cut PoC cost without weakening the result?
- Should you build a PoC in-house or outsource it?
- What mistakes make PoCs expensive?
- When don’t you need a PoC?
- Frequently asked questions
A PoC is the cheapest way to find out whether an idea is technically feasible before you fund a full build. This post explains what drives the price, where the money goes, how to cut cost without cutting the answer you need, and when you don’t need a PoC at all.
What is a proof of concept, and what isn’t it?
A PoC is a small, focused build that answers one question: will this idea, algorithm or architecture work under real conditions? It isn’t a product, a demo for marketing or an MVP. It has narrow scope, limited polish, clear success criteria, and code you may partly throw away.
- Narrow scope: the one or two riskiest technical assumptions, nothing else.
- Clear success criteria: for example, a latency target, an accuracy threshold on your data, or an integration that completes end to end.
- Limited UX: enough interface to test the idea, no more.
- Reuse agreed up front: decide which parts should survive into production and build only those carefully.
For AI work, a PoC usually means getting access to data, building a baseline, and measuring it against business-specific metrics, rather than full integration and deployment.
How much does a PoC cost?
Less than an MVP, and far less than a failed full build. The price depends on how many systems the PoC must touch, whether it needs AI and real data, and how clear the success criteria are. We scope every PoC in a short discovery and send a written proposal with the price within two working days of a free 30-minute call.
| Type of PoC | Relative cost | What pushes it up |
|---|---|---|
| Web or internal tool, one integration | Lowest | Unclear requirements, extra integrations |
| SaaS or platform feature, a few integrations | Medium | Multi-tenancy, security or compliance requirements |
| AI or data-heavy PoC | Highest | Data cleaning and labelling, custom models, evaluation work |
If your scope won’t fit in a 4–8 week build, it’s usually an MVP or several PoCs bundled together. We’ll say so and scope it as a project instead, starting with a 1–3 week discovery.
What drives the cost of a PoC?
Scope clarity drives cost more than anything else, followed by technical complexity, data work and non-functional requirements. Vague goals force everyone to add buffers. A PoC that has to prove one thing with one dataset costs a fraction of one that tries to prove five things across three systems.
- Scope and clarity: vague requirements bring change requests and risk buffers.
- Complexity: real-time processing, multi-tenant designs and custom AI models need more senior hours.
- Technology: niche tools mean specialist engineers at higher rates.
- Data: cleaning, labelling and access approvals for AI work are often the surprise cost.
- Non-functional requirements: security, compliance and performance targets all add effort.
- Your decision speed: a PoC waiting a week for data access or feedback still costs money.
Where does the money go during a PoC?
Into five phases: discovery and scoping, architecture, building, testing against the success criteria, and a handover with recommendations. For a simple cloud-hosted PoC, that’s roughly one to two weeks of discovery and design, three to four of building and one of testing. AI PoCs spend more of the time on data and evaluation.
Discovery and scoping
Agree the question the PoC must answer, the success criteria and what’s out of scope.
Architecture and design
Choose the simplest design that tests the idea, and decide which parts are meant to survive.
Build and integrate
Write the code, connect the minimum set of systems and wire up measurement.
Test and validate
Run it against the agreed criteria with real or realistic data.
Handover
A written result: did it work, what we learned, what an MVP would need and what it would cost.
How can you cut PoC cost without weakening the result?
Cut waste, not rigour. Fix the success criteria early, limit integrations to the minimum that proves feasibility, use managed services and pre-trained models instead of building from scratch, and decide in advance which code you’ll keep. Have your data ready on day one.
- Freeze success criteria before the build starts.
- Test the single riskiest assumption first.
- Use managed cloud services and existing APIs where they’re good enough.
- For AI, start with a pre-trained model or a hosted LLM before considering custom training.
- Pick a stack the team can move fast in, even if production will differ.
- Prepare data access and test accounts before kickoff.
Should you build a PoC in-house or outsource it?
In-house gives you the most control but ties up your team and, in the US or Western Europe, costs the most. Outsourcing to a specialist gets you a small squad that has done this before, for a price agreed in writing up front. A common split is to keep product ownership in-house and outsource the build.
If you outsource to us, be aware of time zones. Nepal is UTC+5:45. We share most of the working day with India, part of it with Europe and Australia, and none with the US by default. For US clients we agree a fixed daily call window before kickoff.
What mistakes make PoCs expensive?
Building for production too early, skipping discovery, choosing a partner on price alone, and starting without success criteria. The last one is the worst: without agreed criteria nobody can say whether the PoC worked, and it drifts into an unplanned MVP.
When don’t you need a PoC?
When the technology is well proven and the real risk is whether customers want the product. In that case, spend the money on a small MVP or user research instead. A PoC answers “can it be built?”, not “will people pay for it?”. Our AI MVP development service covers the second question.
Frequently asked questions
Why do AI PoCs cost more than ordinary web PoCs?
They need extra work on data access, cleaning and labelling, plus evaluation against realistic test sets, and the engineers who do it are more specialised. If labelling is a big part of the job, see our data labelling services.
How long does a PoC take?
Our builds take 4–8 weeks. How fast you can provide data, access and feedback affects that more than team size does.
Can we reuse the PoC code?
Often, partly. If you expect to reuse it, tell us at the start so those parts are built to production standards and the throwaway parts stay cheap.
How do we get a quote?
Book a free 30-minute call. We’ll send a scoped proposal within two working days. More detail is on our PoC development page.
Related reading: GenAI MVP development in 4–8 weeks and what an MVP costs.
