Budgeting an On-Prem AI Project: A Line-Item Guide
Most on-prem AI budgets fail for the same reason: they price the GPUs carefully and guess at everything else. Hardware is the line item everyone remembers because it is concrete and quotable, but on a typical mid-size on-prem AI project it is rarely more than a third of total cost. Integration engineering, evaluation and testing, change management, and ongoing operations routinely add up to more than the server itself, and they are the line items that get discovered mid-project rather than budgeted up front. This guide breaks a realistic 2026 on-prem AI budget into its actual components with real ranges, so the number you bring to finance survives contact with the project.
Hardware: The Line Item Everyone Estimates First
GPU pricing in 2026 runs roughly $25,000 to $32,000 per H100 80GB card, $32,000 to $40,000 per H200, and $45,000 to $60,000 per B200, before server chassis, networking, storage, and rack infrastructure. A practical pilot server with four to eight H100s lands in the $150,000 to $350,000 range fully built, while a production cluster for multiple concurrent use cases can run $500,000 to $1.5 million. Add 15 to 25 percent for networking, storage, and power and cooling infrastructure that is easy to forget until the facilities team asks about it.
- H100 80GB: roughly $25,000 to $32,000 per card, current generation workhorse for 7B to 70B model serving
- H200: roughly $32,000 to $40,000 per card, larger memory footprint for longer context and larger models
- B200: roughly $45,000 to $60,000 per card, next-generation for the largest deployments and training
- Add 15 to 25 percent on top of GPU cost for networking, storage, rack, power, and cooling
Software Licensing and Model Costs
The serving stack itself is usually free: vLLM, SGLang, and Ollama are open source with no license fee. The cost hides elsewhere, in MLOps and observability tooling, vector database licensing if you choose a commercial option over an open-source one, and any commercially licensed model rather than an open-weight one under a permissive community license. Budget $20,000 to $80,000 annually for tooling and observability depending on scale, and confirm the license terms of any base model you fine-tune, since some open-weight licenses restrict commercial redistribution of derivative weights above certain usage thresholds.
Integration Engineering: The Line Item That Blows Budgets
Connecting a model to your actual systems of record, SyteLine, Infor LN, a document repository, or a shop floor data source, is where projects most commonly overrun. Integration engineering typically consumes 30 to 50 percent of total project labor cost, covering API or IDO integration, data pipeline construction, authentication and least-privilege service accounts, and the evaluation harness that proves the integration actually works against real records rather than a clean sample. Projects that budget integration as an afterthought at 10 percent of labor almost always renegotiate scope mid-build.
- ERP or system-of-record integration: API or IDO work, service account setup, data mapping
- Data pipeline engineering: ingestion, cleaning, and ongoing sync for RAG or fine-tuning data
- Evaluation harness: golden test set construction and automated scoring, often underestimated
- Security hardening: least-privilege scoping, logging, and audit trail wiring
Change Management and Training
Budget 10 to 15 percent of total project cost for change management: job aids, shift-change training, a communication plan for affected teams, and time for a floor-level champion to be involved throughout the build rather than only at rollout. This is the line item most often cut when a budget gets squeezed, and it is directly correlated with whether the finished system actually gets used. A technically flawless agent that nobody adopts delivers zero of the ROI the budget was built to justify.
A Sample Line-Item Budget for a Mid-Size Pilot
A representative eight-week pilot for a single use case: $60,000 to $120,000 in rented cloud GPU time or a small on-prem server lease instead of a capital purchase, $50,000 to $90,000 in integration and evaluation engineering labor, $10,000 to $15,000 in change management and training, and $10,000 to $20,000 in project management and security review support. Total: roughly $130,000 to $245,000 for a scoped pilot proving out one use case before committing to a full production build.
How Netray Builds Budget Estimates
Netray scopes budgets with the same line items above, itemized rather than bundled into a single number, so finance can see exactly what they are approving. We separate hardware, integration, evaluation, change management, and first-year operations explicitly, and we flag which numbers are firm quotes versus estimates that firm up after a discovery phase. For clients unsure whether to buy hardware or start on rented cloud GPUs, we model both paths side by side before recommending a capital purchase.
Frequently Asked Questions
What percentage of an on-prem AI budget should go to hardware?
For a typical mid-size project, GPU hardware and supporting infrastructure runs 25 to 40 percent of total budget, with the remainder split across integration engineering, evaluation, change management, and project management. Larger multi-use-case programs can see hardware drop below 25 percent of the total as fixed engineering and governance costs amortize across more use cases running on the same cluster.
Do I need to budget separately for maintenance after go-live?
Yes. Ongoing operations, model updates, monitoring, and sampled human review of output are a recurring cost, not a one-time build item. Budget 15 to 25 percent of the original build cost annually for maintenance and evaluation reruns, plus GPU hardware amortization if you purchased rather than leased. Skipping this line item is one of the most common reasons a technically successful pilot quietly degrades after the consultant leaves.
How much does integration engineering typically cost on an on-prem AI project?
Integration engineering, meaning the work to connect a model to your ERP, document systems, or shop floor data, typically consumes 30 to 50 percent of total project labor cost. It includes API or IDO work, service account setup, data pipeline construction, and the evaluation harness needed to prove the integration works against real production records rather than a clean sample dataset.
Key Takeaways
- 1Hardware: The Line Item Everyone Estimates First: GPU pricing in 2026 runs roughly $25,000 to $32,000 per H100 80GB card, $32,000 to $40,000 per H200, and $45,000 to $60,000 per B200, before server chassis, networking, storage, and rack infrastructure. A practical pilot server with four to eight H100s lands in the $150,000 to $350,000 range fully built, while a production cluster for multiple concurrent use cases can run $500,000 to $1.5 million.
- 2Software Licensing and Model Costs: The serving stack itself is usually free: vLLM, SGLang, and Ollama are open source with no license fee. The cost hides elsewhere, in MLOps and observability tooling, vector database licensing if you choose a commercial option over an open-source one, and any commercially licensed model rather than an open-weight one under a permissive community license.
- 3Integration Engineering: The Line Item That Blows Budgets: Connecting a model to your actual systems of record, SyteLine, Infor LN, a document repository, or a shop floor data source, is where projects most commonly overrun. Integration engineering typically consumes 30 to 50 percent of total project labor cost, covering API or IDO integration, data pipeline construction, authentication and least-privilege service accounts, and the evaluation harness that proves the integration actually works against real records rather than a clean sample.
Put this into numbers
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Terms used in this article
Building the business case for an on-prem AI budget? Netray will itemize a realistic line-item estimate for your specific use case before you take a number to finance.
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