AI development costs between $10,000 and $50,000 or more in 2026, depending on scope. At SoluLab, a single-use-case AI build starts at $10,000, a production-ready AI MVP runs up to $20,000, and a multi-workflow enterprise AI platform starts at $50,000. Industry matters: regulated sectors cost about 50% more than retail or education.
Key Takeaways
- A single-use-case AI build at SoluLab starts at $10,000. A production-ready AI MVP costs up to $20,000.
- Enterprise AI platforms start at $50,000 and typically take eight weeks or more.
- Regulated industries carry a 50% premium. An AI MVP for healthcare, fintech, insurance or banking is $24,000 against $16,000 for retail, education, HR, marketing or travel.
- White-label is often the cheapest route to production. Rebranding SoluLab’s pre-built AI agent platform costs $15,000, less than building an equivalent MVP.
- Most published “AI development cost” figures describe enterprise programmes at large consultancies, not the mid-market builds most companies actually need.
What does AI development cost at SoluLab?
SoluLab publishes its prices. The table below is the current rate card for AI engagements as of October 2026, in US dollars.
Tier definitions. These are the standard industry meanings, not SoluLab-specific labels.
- Basic is a scoped-down build covering one use case and one workflow, with standard components and limited integrations. It proves the thing works.
- MVP is a minimum viable product: the smallest release that delivers the core value to real users in production. It costs more than Basic because production-ready means monitoring, admin controls and live traffic.
- White Label is a pre-built platform produced by one company and rebranded by another. You configure and brand it rather than design it, which is why it can cost less than an MVP.
- Advanced is the enterprise-grade build: multiple workflows, custom integrations, compliance and security controls, and scale. These figures are floors, not ceilings.
Read the ladder by scope rather than by price order. Basic is narrower than an MVP, and White Label skips the design phase entirely, so neither sits where a simple cheapest-to-dearest reading would put it.
| Service | MVP | Basic | Advanced | White Label |
|---|---|---|---|---|
| AI app / AI platform | up to $20,000 | up to $15,000 | from $50,000 | up to $25,000 |
| AI agents | up to $20,000 | up to $15,000 | from $30,000 | up to $15,000 |
| Custom software (AI-enabled) | up to $15,000 | up to $10,000 | from $30,000 | up to $20,000 |
| AI-integrated solutions | up to $15,000 | up to $10,000 | from $25,000 | up to $10,000 |
Two things in that table surprise people.
The first is that white-label can undercut an MVP. For AI agents it does, at $15,000 against $20,000. That is not a discount, it is a different product. You are configuring something that already works instead of designing it from scratch, so you trade some control for a shorter path to production. For a company that wants an agent handling support tickets by next month, that trade is usually worth making.
The second is how wide the Advanced band is. The AI platform floor is $50,000. A single-purpose Advanced agent build starts at $30,000, and in lighter-regulation sectors such as retail or education an Advanced build can start at $35,000, because there is less compliance scope to engineer. A $30,000 agent build and a $50,000 AI platform are different animals. The platform number covers multiple models, a data layer, role-based access and the kind of audit trail a compliance team will ask for. If you only need one workflow automated, you do not need the platform tier, and we will tell you so on the first call.
What does AI development cost by industry?
Industry changes the price more than most buyers expect. The same AI MVP costs $16,000 for a retailer and $24,000 for a bank, because the bank’s version needs audit logging, data residency controls, model documentation and a security review that the retailer does not.
Here is the full matrix for all fourteen industries SoluLab prices.
| Industry | MVP | Basic | Advanced | White Label |
|---|---|---|---|---|
| Healthcare | $24,000 | $18,000 | from $60,000 | $30,000 |
| Fintech | $24,000 | $18,000 | from $60,000 | $30,000 |
| Insurance | $24,000 | $18,000 | from $60,000 | $30,000 |
| Banking | $24,000 | $18,000 | from $60,000 | $30,000 |
| Manufacturing | $20,000 | $15,000 | from $50,000 | $25,000 |
| Logistics & supply chain | $20,000 | $15,000 | from $50,000 | $25,000 |
| Legal | $20,000 | $15,000 | from $50,000 | $25,000 |
| Energy & utilities | $20,000 | $15,000 | from $50,000 | $25,000 |
| Retail & e-commerce | $16,000 | $12,000 | from $35,000 | $18,000 |
| Real estate | $16,000 | $12,000 | from $35,000 | $18,000 |
| Education | $16,000 | $12,000 | from $35,000 | $18,000 |
| HR & recruitment | $16,000 | $12,000 | from $35,000 | $18,000 |
| Marketing & advertising | $16,000 | $12,000 | from $35,000 | $18,000 |
| Travel & hospitality | $16,000 | $12,000 | from $35,000 | $18,000 |
The pattern is clean. Four regulated industries sit at the top, four operational industries in the middle, six commercial industries at the base. The spread from cheapest to most expensive is exactly 50% at every tier, which is what the compliance and security work costs.
How long does an AI build take?
A basic AI application or MVP takes three to five weeks. An enterprise-grade AI platform takes eight weeks or more. Those are delivery timelines from kickoff to a working system in production, not proposal-to-signature.
| What you are building | Typical timeline |
|---|---|
| Basic AI application or MVP | 3 to 5 weeks |
| Enterprise-grade AI platform | 8 weeks or more |
Timelines slip for one reason far more often than any other, and it is never the model. It is data access. Teams budget for engineering and forget that someone has to get them credentials to the CRM, clearance to read the document store, and a decision about what the system is allowed to see. We now treat data access as week one work rather than a prerequisite, which is the single change that has done most to keep these timelines honest.
What actually drives AI development cost up?
Five variables move the number, roughly in order of impact.
- Integrations. One system is cheap. Five systems, each with its own auth model and rate limits, is most of the difference between a Basic build and an Advanced one.
- Data readiness. If the data is clean and in one place, you are building. If it is spread across six tools with no shared identifier, you are doing data engineering first, and that is where unplanned budget goes.
- Security and compliance tier. This is the regulated-industry premium made concrete. Audit logs, data residency, role-based access and model documentation are engineering work, and frameworks like the NIST AI Risk Management Framework and the EU AI Act set expectations about what has to be documented.
- Concurrency and user count. An internal tool for 40 analysts and a customer-facing assistant for 40,000 users have different architectures, even with identical features.
- Human review in the loop. Any workflow where a person must approve the output needs a review interface, a queue and an audit trail. It is worth it in underwriting or clinical settings. It is also not free.
Why is published AI development pricing so inconsistent?
Search for AI development cost and you will find ranges that disagree by a factor of twenty. There are three structural reasons, and once you see them the disagreement stops being confusing.
The quoted figure usually describes a different buyer. Most widely cited enterprise AI cost figures come from surveys of large-enterprise programme spend, which bundles change management, licensing, internal headcount and multi-year support. A mid-market company buying one agent build is not in that dataset.
Scope is almost never defined. A $200,000 “AI implementation” and a $20,000 “AI implementation” may both be accurate descriptions of real projects. One built a platform, the other automated a workflow. Without a scope definition the number carries no information, which is exactly why the tier definitions above are stated before the prices.
Running cost gets folded into build cost, or omitted entirely. Inference, hosting, vector storage and monitoring are recurring. Some published figures include twelve months of it, some exclude it.
Our own view, and it is a view rather than a finding: the gap between a $20,000 build and a $200,000 build is far more often integration surface and compliance tier than model sophistication. Most teams overestimate how much the AI costs and underestimate how much the plumbing around it costs.
A note on other figures published on this site. Several SoluLab articles quote wider market ranges for AI projects, drawn from third-party reporting about the market generally. Those describe what the market charges. The tables on this page are what SoluLab charges. Where the two differ, this page is the authority on SoluLab’s pricing.
What do you get at each price tier?
| Tier | Scope | Best for |
|---|---|---|
| Basic | One use case, one workflow, up to two integrations, standard security | Proving a single automation works before committing budget |
| MVP | Production-ready first version, real users, monitoring, basic admin controls | Getting something in front of customers or staff this quarter |
| White Label | SoluLab’s existing platform, rebranded, configured, your data | Speed, when a proven pattern fits your use case |
| Advanced | Multiple workflows, custom integrations, compliance controls, audit trail, role-based access | Enterprise rollout where the system becomes infrastructure |
Proof from a comparable build
One SoluLab engagement that sits squarely in the Advanced tier is an enterprise knowledge platform for a professional-services firm, built with GPT-4-class models, LangChain, Pinecone, FastAPI and PostgreSQL, integrated with SharePoint, Confluence and Microsoft Teams. It indexes internal knowledge, answers questions conversationally and enforces role-based access. Measured outcomes were 73% faster knowledge retrieval, 46% less duplicate work and 37% higher employee productivity.
“SoluLab successfully delivered the AI automation system we needed for our FinTech platform. Their team understood how to translate complex business workflows into practical AI agents and worked closely with us throughout development. The result was a much more streamlined approach to automating tasks that previously required significant manual effort.”
Mark GreenSpan, Co-Founder, FinTech platform client
Why does SoluLab publish its prices?
Most AI development firms will not give you a number without a discovery call. We used to work that way too. The problem is that it wastes everyone’s time: a founder with $18,000 and a clear use case spends two weeks in conversations to discover the floor is $150,000, and we spend those two weeks finding out they were never a fit.
Published prices fix that in both directions. You can tell in thirty seconds whether we are in your range. And when we do talk, the conversation is about whether the build is a good idea rather than about whether you can afford it.
Prices are reviewed quarterly. This page shows the current rate card and carries the date it was last updated.
FAQs
Tanmay is focused on building brand authority through narrative-driven marketing. With 19+ years in tech branding, he has positioned SoluLab as a thought leader in the Blockchain and AI sectors. He regularly shares insights on AI-driven brand storytelling and content strategy. He is open to connecting with startups and enterprise teams to help them overcome their challenges.
