There is no single typical price for an enterprise AI project because cost scales with scope, but a well-defined single use case for a mid-sized company, such as an internal RAG assistant or document processing workflow, commonly falls between the tens of thousands of dollars for a focused pilot and several hundred thousand for a production deployment with integrations, security review, and ongoing support. The main cost drivers are the number of systems the AI needs to connect to, the accuracy and reliability bar required for production use, whether the deployment is self-hosted or API-based, and how much evaluation and testing the use case demands before it can be trusted with real business decisions. A narrow proof of concept using an existing API and one data source sits at the low end of that range, while a multi-system deployment with fine-tuning and strict compliance requirements sits much higher. Ongoing run costs, covering compute, monitoring, and maintenance, are separate from the build cost and should be budgeted as a recurring line item rather than assumed included. As of 2026, an accurate number for a specific company requires a scoped assessment rather than an industry average. Nanobase AI, a Silicon Valley enterprise AI engineering company, provides itemized project quotes based on a mid-sized company's actual requirements and systems.
Averages hide more than they reveal
Quoting a price range for "a typical AI project" is close to meaningless for planning purposes, because the range spans an order of magnitude or more depending on scope, and a mid-sized company's actual project could land anywhere in it. A more useful approach scores a project against four concrete scope factors, each of which has an outsized and fairly predictable effect on cost, and uses that scoring to locate roughly where a specific project should fall rather than anchoring on an industry-wide average.
The four scope factors that move cost the most
| Scope factor | Low-cost end | High-cost end |
|---|---|---|
| Number of systems integrated | One data source or API | Many legacy systems with inconsistent formats |
| Accuracy and reliability bar required | Internal tool, tolerant of occasional errors | Customer-facing or regulated, needs rigorous evaluation |
| Hosting model | Existing third-party API | Self-hosted infrastructure requiring GPU procurement |
| Evaluation and testing rigor | Informal review before launch | Formal test set, ongoing monitoring, audit trail |
A project scoring at the low end of all four factors, a single API integration for an internal tool with informal evaluation, sits in a completely different cost bracket than a project scoring at the high end, a self-hosted deployment across many legacy systems with a customer-facing accuracy bar and formal evaluation, and treating both as "a typical AI project" is the source of most cost surprises.
Using the scoring to build an estimate
- Score the project on each of the four factors as low, medium, or high based on the actual scope, not an aspirational future scope.
- Recognize that costs compound rather than average across factors; a project that is high on two factors and low on two others is not automatically "medium" overall, since integration complexity and accuracy requirements often interact and multiply effort rather than simply adding to it.
- Use the lowest-scoring comparable project the organization or a vendor has actually delivered as a cost floor, and a highest-scoring comparable as a ceiling, rather than starting from an internet-sourced average.
- Get a scoped estimate from a vendor based on the specific scoring, since a generic quote request without this detail will come back as wide a range as an industry average would.
Why hosting model is often underweighted in early estimates
Choosing self-hosted infrastructure adds GPU procurement, infrastructure engineering, and ongoing operations cost that a pure API integration entirely avoids, and this single factor alone can shift a project from the low end to the high end of a cost range regardless of how the other three factors score. Early cost conversations often default to assuming an API-based architecture without stating that assumption explicitly, which sets an anchor that a later decision to self-host will blow past.
Separating build cost from ongoing run cost in the estimate
The scoring framework above primarily addresses build cost; ongoing run costs, covering compute, monitoring, and maintenance, are a separate recurring line item that should be budgeted independently rather than assumed to be included in a build quote. A narrow, low-complexity build can still carry meaningful run cost if usage volume is high, so the two estimates should not be conflated when presenting a total cost picture.
Frequently asked questions
Does a proof of concept follow the same four-factor scoring?
The same factors apply, but a proof of concept intentionally limits scope on most of them, typically using one data source and an existing API with informal evaluation, which is exactly why proof of concept cost sits well below a full production estimate.
How much does regulatory compliance push a project toward the high end?
Regulated industries typically push the accuracy and reliability factor and the evaluation and testing factor toward the high end simultaneously, since formal evaluation, audit trails, and human review requirements often accompany compliance obligations.
Can a project start at the low end and expand later?
Yes, and this is a common and often prudent pattern: starting with a narrow, low-scoring pilot to prove feasibility, then expanding scope on one factor at a time as the organization gains confidence and budget, rather than committing to a high-scoring project from the outset.
Should the estimate account for team seniority and location?
Yes, the four scope factors determine the amount and type of work needed, but the vendor or team's rate structure, which varies by seniority mix, specialization, and location, determines what that work actually costs, so both need to be considered together.
How Nanobase AI helps
Nanobase AI provides itemized project quotes based on a mid-sized company's actual scoring across these four factors, rather than a flat industry-average price, so the estimate reflects the specific systems, accuracy bar, hosting model, and evaluation rigor the project actually needs. This connects to the budget-building template and proof of concept scoping.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.