Before signing a multi-year AI compute contract, confirm what you are actually buying: a discount, a right to request capacity, or reserved, specified GPUs. Then test the full-term cost against realistic usage, put delivery and performance obligations in measurable terms, and negotiate what happens if demand, hardware, or your business changes. A lower hourly rate is not useful if capacity is unavailable when needed or the company must pay for compute it cannot use.
First, establish what the commitment guarantees
“Committed compute” can describe different contractual outcomes. A discount commitment may reduce the price of eligible usage without reserving GPUs in a particular location. A capacity reservation is a separate promise about access to defined resources. A right to request capacity is different again: it may provide a process for seeking resources without guaranteeing that they will be available on the required date.
| Contract outcome | What it generally establishes | What to verify in the order form |
|---|---|---|
| Discount or spend commitment | A price or spending arrangement for eligible usage; it does not necessarily reserve capacity. | Eligible services, GPU families, regions, accounts or projects; whether fees continue when resources are idle; and how unused commitment is treated. |
| Capacity reservation | A reservation of specified resources, subject to the contract’s scope and conditions. | GPU type and count, location, delivery date, reservation duration, release rules, and remedies if capacity is not delivered. |
| Right to request capacity | A defined means of requesting resources, which may not itself guarantee supply. | Response deadlines, allocation priority, availability conditions, and what happens if the request cannot be fulfilled. |
These distinctions appear in provider documentation. Google Cloud says a resource-based commitment provides a one- or three-year discounted price agreement but does not reserve capacity in a specific zone; its documentation also says resource-based GPU commitments require attached reservations, while flexible commitments for certain GPU families do not themselves assure capacity. Check the terms for the exact product and GPU family rather than inferring a reservation from the word “commitment.”
OpenAI’s Guaranteed Capacity offer page, accessed October 3, 2026, describes one- to three-year commitments, guaranteed access based on spend levels, and drawdown across supported OpenAI products, cloud providers, and model families. Those are offer-page descriptions, not a substitute for confirming eligibility, scope, and obligations in the negotiated contract.
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Size the commitment against credible demand
Estimate usage across the entire term, including training runs, inference throughput, seasonality, expected deployment ramp, and the teams or products allowed to draw against the commitment. Model low, expected, and high demand cases. Include the possibility that improved model efficiency reduces compute needs as well as the possibility that product growth increases them.
- Identify whether the commitment is tied to spend, named resources, GPU types, a region, an account or project, or a wider portfolio of services.
- Ask whether eligible usage can be pooled or reassigned across teams, workloads, regions, or products—and what approval is required.
- Get the treatment of unused allocation in writing: rollover, pooling, reassignment, or forfeiture.
- Compare the commitment with on-demand use, shorter reservations, and flexible purchasing—not just with another long-term offer.
Google Cloud’s resource-based commitment documentation says fees remain due through the term whether or not the resources are used, and that the fee and discounted prices stay the same until the end date even if on-demand prices change. The live documentation accessed October 3, 2026, advertises discounts of up to 55% off on-demand prices for most GPU types. That is a Google Cloud-specific maximum, not a typical saving or a market-wide benchmark; confirm which resources and terms qualify.
For each demand case, compare the total unavoidable commitment cost with the value of the compute you expect to use. Include storage, networking, data transfer, support, software, managed services, taxes, and fees. Also check billing cadence, credits, currency and tax treatment, renewal pricing, and any price-adjustment or price-protection clauses. A headline GPU-hour rate cannot show whether the overall deal works if usage is lower than forecast.
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Specify the capacity and delivery you need
Describe the usable system, not just a GPU family name. State the accelerator model and generation, count, memory, interconnect and topology, host CPU and RAM, storage, network bandwidth, cluster size, region or zone, and the date the environment must be ready for use. Define whether “delivery” means hardware allocated, access enabled, or a workload that has passed agreed readiness checks.
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Include maintenance and interruption behavior in the capacity plan. Google Cloud documentation states that Compute Engine instances with attached GPUs are stopped during host maintenance. Confirm how that behavior applies to the chosen service and what restart, scheduling, or continuity arrangements are available.
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Make performance, availability, and remedies measurable
An SLA should say what is measured and how a shortfall is handled. Separate compute capacity from control-plane access, storage, network, and support response where those components have different effects on the workload. Define the measurement window, exclusions, maintenance treatment, evidence and reporting process, claim deadline, credit caps, and whether credits expire or can only be used on future orders.
NVIDIA’s DGX Cloud SLA, last modified November 5, 2025, states monthly targets of 99% service availability and 95% capacity availability, measured over a calendar month. Its capacity calculation tracks 60-minute intervals and excludes gaps shorter than 60 minutes; validated claims receive service credits as the stated remedy. These are DGX Cloud terms, not a universal benchmark for AI compute. Check the SLA that applies to the specific service and order, and assess whether credits compensate meaningfully for the business impact.
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Address failures that ordinary uptime credits may not cover: missed delivery dates, partial deployment, prolonged outages, or failure to meet an agreed performance level. Negotiate suitable remedies such as make-good capacity, fee reductions, termination rights, or other agreed relief, and check for language making credits the sole remedy.
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Set the term, ramp, and exit consequences before signing
Write down the effective date, delivery milestones, ramp period, payment start date, renewal and extension mechanics, and dependencies on power, networking, hardware delivery, or customer readiness. Define consequences for deployment delay, loss of a required GPU family, material provider changes, extended outage, or a sharp fall in business demand. Review termination for convenience and cause, cure periods, suspension, insolvency, regulatory change, and force majeure alongside any fees payable after termination.
Do not assume a commitment can be canceled because the workload changes. Google Cloud says resource-based commitments cannot be canceled or deleted after purchase and remain active through the specified end date, with fees payable regardless of use. NVIDIA’s DGX Cloud service-specific terms say early termination does not affect the obligation to pay fees for the full subscription period. These examples are product-specific; the order form and governing terms for the proposed service control.
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Before committing, establish who owns the data and artifacts, where they may be processed, who can access them, how they are encrypted and logged, which subcontractors are involved, and what incident reporting, audit evidence, retention, backup, and deletion obligations apply.
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List what must be exportable: datasets, checkpoints, model weights, container images, logs, configurations, and outputs. Specify usable formats, provider assistance, retrieval access after termination, egress pricing, the transfer window, and whether service continues while a migration is underway. Require a clear deletion process, including confirmation where appropriate. These terms can affect the practical cost and timing of leaving even when the compute rate looks attractive.
Google Cloud’s archived service terms dated February 18, 2026 include switching and export provisions; for certain covered exports, they say data-export egress charges may pass through incurred egress costs but cannot exceed those costs. Confirm that the archived provision applies to the current product and contract. AWS’s general customer agreement, last updated August 14, 2026, describes a 30-day post-termination content retrieval period in specified circumstances, conditioned on payment of amounts due. That general agreement does not establish the exit terms for every AWS compute commitment.
Compare offers on the same assumptions
Normalize competing proposals before comparing their headline discounts. For each offer, record the same workload assumptions and use the same definition of usable capacity.
- GPU generation and model, quantity, topology, region, and ready-for-use date.
- Whether the deal guarantees usable capacity or only provides a discount or financial commitment.
- Benchmark workload, utilization assumptions, storage and network configuration, and support level.
- All-in cost over the full term, including underuse and growth scenarios.
- SLA definitions, exclusions, claim process, remedies, and limits.
- Refresh and substitution terms, termination exposure, export provisions, and egress costs.
A March 2026 Clifford Chance briefing identifies guaranteed capacity and performance, refresh and upgrade mechanics, deployment and installation delay risk, termination and portability, and security and auditability as negotiation issues in long-term AI compute offtake. It is legal-market commentary, not a binding standard or evidence that providers offer identical terms.
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