You Got a Quote and It Felt Either Way Too High or Suspiciously Low
Both reactions are usually correct.
Getting meaningful pricing for an AI product is genuinely hard. The range of quotes you'll receive for what sounds like the same project is enormous. One agency might say $50k. Another might say $400k. A freelancer might say $15k. They all sound confident.
Here's what's actually happening: they're pricing different things. And without understanding what drives cost, you can't tell which quote represents good value.
This guide breaks it down honestly.
The Four Things That Drive Cost
1. The AI Component Itself
Most AI products in 2026 do not require custom models. They call existing APIs (OpenAI, Anthropic, Google Vertex AI) and build logic around the response. API costs for inference are low: typically fractions of a cent per call for most tasks.
Where cost goes up is in the architecture around the AI:
- Document processing pipelines that handle real-world file formats reliably
- Retrieval-augmented generation (RAG) systems that search your proprietary data accurately
- Multi-agent orchestration where multiple AI systems coordinate
- Validation and retry logic to ensure consistent accuracy
A team that has built these systems before will do it faster and for less than a team learning on your project.
2. The Product Itself (Everything Around the AI)
The AI feature is rarely the expensive part. The expensive part is the product that surrounds it:
- User authentication and account management
- Subscription billing and payment processing
- Multi-tenant architecture (so different clients can't see each other's data)
- Admin dashboards and reporting
- File upload and storage
- Role-based permissions
- Error handling and monitoring
This is the invisible work. It doesn't make the demo look impressive, but it's what makes a product usable by real businesses.
3. Design
A product that works but feels confusing or dated loses users. Good product design costs money, and it creates significant value. The difference between a product that users figure out in three minutes versus thirty is often the quality of the UX work.
Founders who skip design to save money usually spend more later rebuilding things that users couldn't figure out the first time.
4. Who Is Doing the Work
This is the biggest cost variable of all.
Large agency: $150k to $500k+ for a full AI product. Long processes, many stakeholders, slow feedback loops. Justified if you need enterprise-grade compliance or have genuinely complex requirements.
Boutique product studio: $30k to $150k for a production-ready AI product. Smaller team, faster cycles, direct access to the people doing the work. Better value for most founders at this stage.
Individual freelancers: $15k to $80k, but you're paying for parts, not a system. You manage the integration yourself, which is a real cost in time even if not in dollars.
In-house team: $300k to $600k per year in salaries. Best for long-term, but not the right vehicle for a first build.
Real Numbers From Real Projects
Document Processing AI: Medium-Complexity Build
Artheon Medical needed to automate the processing of Explanation of Benefits (EOB) documents. These documents arrived in inconsistent formats from different insurance providers. Their team was spending hundreds of hours per month extracting billing data manually into spreadsheets.
FeatherFlow built PureClaim for them: an AI-powered SaaS platform that automatically extracts, validates, and normalizes EOB data. The system uses Google Vertex AI for document understanding, FastAPI for the backend, and Next.js for the frontend, with multi-tenant architecture, admin dashboards, real-time job tracking, and CSV and Excel export.
The result was an 80 to 90% reduction in manual processing time.
Now consider the math. If Artheon's team spent 200 hours per month on this work at $50 per hour, that's $120,000 per year in labor. Cutting that by 85% saves more than $100,000 annually. A product that delivers that ROI pays for itself within months.
Education AI Prototype: Fast Scope, Fast Timeline
EduSync, an AI-powered coding education platform, was built as a prototype in 35 days. The platform included interactive coding games, a student progression system with gamification, an AI chatbot for hints, and a RAG-powered teacher assistant. The founder raised approximately $100k in pre-seed funding using that prototype.
A prototype at that scope, built by an experienced team, sits in the $25k to $60k range depending on the studio. The fundraising it enabled made that investment return many times over.
Full SaaS Platform with Branding: End-to-End Build
NTREE needed more than just code. They needed strategy, product design, brand identity, a marketing website, and the platform itself. A single link that shows different content based on time, platform, and location, manageable by non-technical restaurant owners and event managers.
FeatherFlow handled all of it: product strategy, user flows, visual design, brand identity, marketing website in Framer, and full platform development. The kind of project that would require a product manager, a designer, and two developers working in coordination, all delivered under one roof.
End-to-end builds like this sit in the $60k to $150k range depending on complexity.
How to Budget Realistically
The MVP Budget: $25k to $75k
This gets you a working product with one core AI feature, basic auth, a usable interface, and real user feedback. The goal is validation, not perfection.
At this budget, you're buying proof that the idea works and that users want it. If they do, you fund the next stage. If they don't, you've learned something valuable at a cost you can absorb.
The Production Budget: $75k to $200k
This gets you a product you can sell. Multi-tenant, billing, polished UI, proper error handling, monitoring, and the infrastructure to support real customers.
This is the right scope once the MVP has validated that the market is there.
The Scale Budget: $200k+
This is when you're investing in significant infrastructure: custom models, high-volume processing, enterprise compliance, advanced analytics, and integrations with complex third-party systems.
Most founders don't start here.
The Hidden Costs Nobody Talks About
Ongoing AI inference costs: Every time a user interacts with your AI feature, you pay for the model call. At low volume, this is negligible. At high volume, it becomes a line item. Model your unit economics before launch.
Iteration after feedback: Your first version will need changes after real users touch it. Budget 20 to 30% of the build cost for post-launch iteration in the first three months.
Maintenance and hosting: Production apps require monitoring, updates, and occasional infrastructure work. Budget $500 to $2,000 per month depending on complexity.
Your time: If you're spending 20 hours per week in meetings with a development team, your time has a value. Studios that communicate clearly and require less hand-holding from you are worth paying a premium for.
How to Evaluate Whether a Quote Is Fair
When you receive a quote, ask these questions:
What does the scope include? Get a list of features, not just a price. Two quotes that look the same number may represent completely different scopes.
Have you built something similar before? Ask for case studies with the same class of AI component. A team that has built document processing pipelines before will price them accurately. A team quoting for the first time is guessing.
What happens if the scope changes? Understand whether the quote is fixed-scope or time-and-materials. Fixed-scope protects you from cost overruns. Time-and-materials gives more flexibility. Know which you're getting.
What is the payment structure? Legitimate studios work with milestone-based payments tied to deliverables, not large upfront payments before anything is built. If someone asks for 80% upfront, that's a red flag.
What do you hand over at the end? You should own the codebase, the cloud accounts, the domain, and the data. If there's any ambiguity about this, clarify it before signing.
The False Economy of Cheap
The cheapest option is almost never the best value.
A $15k freelancer who disappears after six weeks, leaving half-finished code that a new developer has to untangle, costs more than a $60k studio that delivered a clean, documented product on time. The hidden cost is in the rebuild and the time lost.
The founders who get stuck in the "$15k to $15k to $30k to finally paying $80k to fix it all" cycle would have saved money and six months of their life by budgeting correctly from the start.
Budget for what you actually want, not for the number that feels comfortable.
Frequently Asked Questions
Can I build an AI product for under $10k?
A very narrow proof of concept with no real UI, no auth, and no production infrastructure: yes. Something you can show to real users and collect feedback on: probably not. At that budget you're likely getting a demo, not a product.
Is it cheaper to use a boilerplate?
Yes, significantly. Starting from an AI-ready SaaS boilerplate eliminates weeks of infrastructure work. Teams that use good starting points pass those savings on in lower quotes. Ask any studio you're evaluating what their starting infrastructure looks like.
How do I know if the ROI makes sense?
Calculate what the problem is currently costing you. If your team spends 200 hours per month on a manual task at any reasonable cost per hour, and the AI product eliminates 80% of that, the math usually justifies a significant build budget. Model it out before you start.
Should I pay for discovery before committing to the full build?
Yes. A paid discovery phase of $3k to $8k where the studio maps your requirements, evaluates technical feasibility, and defines scope is one of the best investments you can make. It surfaces ambiguity early, gives you a realistic scope to price against, and tells you whether the team actually understands your problem before you're six figures deep.
The Bottom Line
A meaningful AI MVP costs $25k to $75k. A production-ready SaaS product costs $75k to $200k. Anything significantly lower than these ranges is either a very narrow scope or a risk you're taking on.
The right budget is the one that gets you to a real answer. Does your AI idea work? Do users want it? Those are the questions version one should answer. Everything else is version two.
The founders who look back and say the build cost too much are almost always the ones who either underfunded version one or overfunded the wrong scope. Getting this number right from the start is worth the research time.