Costs vary enormously by scope, but as of 2026 a narrowly scoped pilot built on hosted model APIs typically runs in the tens of thousands of dollars, a production deployment with custom integration, private hosting or fine-tuning commonly reaches the low hundreds of thousands, and a full on-premise GPU infrastructure build can run into the high hundreds of thousands or more; always verify current pricing directly with vendors rather than relying on published industry averages. Cost drivers include integration count, since each connected system such as SAP, Salesforce or ServiceNow adds engineering time, whether the model runs on private infrastructure or a hosted API, data preparation effort, and ongoing costs such as GPU hosting or per-token fees. Hosted-API pilots keep upfront hardware spend at zero but carry ongoing usage fees that scale with volume, while on-premise deployments front-load hardware and setup cost but can lower per-inference cost at high, steady volume over time. Consulting and engineering labor typically costs more than the model or infrastructure itself for most mid-sized deployments. Request a written, itemized quote broken out by discovery, build, infrastructure and ongoing support rather than accepting a single bundled number. Nanobase AI provides an itemized, scope-based quote after a short discovery call rather than a flat rate card, since GPU, integration and data needs differ project to project.
Reading a quote by its cost drivers, not its total
A single total number on a vendor quote hides more than it reveals, since two projects with the same headline price can have completely different risk profiles depending on what is driving the cost. Breaking a quote into its underlying drivers, integration count, hosting model, data preparation effort and ongoing operational cost, makes it possible to compare vendors meaningfully and to spot where a low bid is cutting corners rather than being genuinely efficient.
As of 2026, always request an itemized quote broken out by discovery, build, infrastructure and ongoing support, and verify current pricing directly with vendors rather than relying on published industry averages, which age quickly in a fast-moving market.
Cost structure by scope
| Scope | Typical cost range (2026, verify with vendors) | What drives it |
|---|---|---|
| Narrow pilot on hosted model API | Tens of thousands of dollars | Discovery time, prompt or light fine-tuning work, no hardware spend |
| Production deployment, custom integration | Low hundreds of thousands | Integration count, security review, monitoring build-out |
| Private hosting or custom fine-tuning added | Adds meaningfully to the above | GPU hosting or procurement, ongoing inference cost, MLOps setup |
| Full on-premise GPU infrastructure build | High hundreds of thousands or more | Hardware procurement, data center or colocation setup, networking, ongoing power and cooling |
Where the money actually goes
For most mid-sized deployments, consulting and engineering labor makes up a larger share of total project cost than the model or infrastructure itself. This surprises budget holders who assume GPU hardware or API fees dominate the bill. Labor cost concentrates in three areas: data preparation and cleanup, which is almost always underestimated at the proposal stage; integration engineering, since each connected system such as SAP, Salesforce or ServiceNow requires its own authentication, testing and error handling; and evaluation and monitoring setup, which is easy to skip in a quote but expensive to retrofit later.
Upfront versus ongoing cost trade-offs
Hosted-API pilots keep upfront hardware spend at zero but carry ongoing usage fees that scale with query volume, which can become the larger cost over time for high-volume use cases. On-premise deployments front-load hardware and setup cost but can lower per-inference cost at high, steady volume, since the marginal cost of an additional query on owned infrastructure is far lower than a per-token API fee once utilization is high. The own GPUs versus cloud API cost comparison walks through the volume threshold where this trade-off tends to flip in more detail.
Requesting a quote that can actually be compared
- Ask every vendor to break the quote into discovery, build, infrastructure and ongoing support as separate line items.
- Confirm whether pricing is fixed, time and materials, or usage-based, and ask for a worst-case scenario in writing, not only the pitch's best case.
- Ask what happens to cost if the integration count or data volume turns out larger than initially scoped.
- Compare quotes against the same defined scope document, not against each vendor's own interpretation of the requirements.
- Ask specifically what ongoing cost, hosting, support, monitoring, continues after the initial build is complete.
Frequently asked questions
Is a lower quote always a red flag?
Not automatically, but it warrants specific questions. A lower quote can reflect genuine efficiency, a leaner scope, or a team cutting corners on testing, monitoring or security review. Ask what is explicitly excluded from the lower quote compared to higher ones before assuming it represents better value.
Does the choice of underlying model significantly affect total project cost?
Model choice affects ongoing per-query cost more than upfront build cost in most cases, since the engineering work of integration, data preparation and evaluation is largely independent of which model sits behind the system. Switching models later is usually easier than switching the surrounding infrastructure and integrations.
Should we budget separately for ongoing maintenance after launch?
Yes, and this is one of the most commonly underbudgeted items. Plan for ongoing cost covering monitoring, model updates, and periodic re-evaluation as data patterns shift, typically as a smaller but recurring line item rather than a one-time cost folded into the original build.
How can we estimate cost before getting a vendor quote?
Estimate roughly by counting the number of systems the use case needs to integrate with, checking whether the data requires significant cleanup, and deciding whether hosted APIs or private infrastructure fits the volume and compliance needs. This rough framing helps set an internal budget range before vendor conversations begin, even without exact figures.
How Nanobase AI helps
Nanobase AI, an NVIDIA Inception Program member, provides an itemized, scope-based quote after a short discovery call rather than a flat rate card, since GPU, integration and data needs differ meaningfully from one project to the next. The team breaks out infrastructure, build and ongoing support costs separately so budget holders can see exactly what drives the total.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.