NVIDIA AI Enterprise & DGX Cloud Contract Guide

NVIDIA AI Enterprise & DGX Cloud Contract Guide

Overview: CIOs and procurement leaders negotiating contracts for NVIDIA AI Enterprise (NVIDIA’s AI software suite) and NVIDIA DGX Cloud (NVIDIA’s fully managed AI infrastructure service) must carefully balance cost, flexibility, and compliance.

NVIDIA’s dominant market position means its default terms often favour the vendor, but informed negotiation and independent expertise can secure more favourable outcomes.

This guide offers a structured approach, covering licensing models, pricing tiers, key negotiation strategies, and critical legal considerations, focusing on actionable insights and real-world tactics.

NVIDIA AI Enterprise: Licensing and Deployment Models

Licensing Models: NVIDIA AI Enterprise is typically licensed per GPU, requiring a license for each GPU running the software.

Enterprises can choose from three licensing models:

• Subscription Licenses: These are available in fixed terms (e.g., 1-year, 3-year, 5-year) and must be renewed upon expiration to remain active. The subscription fee covers both the software and production-level support for the license’s term. Multi-year subscriptions often come at a discounted aggregate rate per GPU (see pricing table below).
• Consumption (Cloud Marketplace) Licenses: These licenses offer a pay-as-you-go model via cloud marketplaces (AWS, Azure, Google Cloud), charged hourly per-GPU basis. For example, NVIDIA AI Enterprise Essentials is roughly $2 per GPU per hour on-demand in the public cloud. This model includes Standard support (typically limited to a few support cases) and is ideal for bursty or trial workloads. Enterprises can negotiate custom pricing for committed usage through private offers on these marketplaces.
• Perpetual Licenses: A one-time purchase that allows indefinite use of the software on a given GPU, bundled with 5 years of support. NVIDIA requires purchasing five-year Business Standard Support with any perpetual license, after which support can be renewed annually. Perpetual licenses command a high upfront cost but may appeal to organizations planning to use hardware long-term without change.

Pricing & Support: Table 1 summarizes NVIDIA’s suggested pricing (list prices) and support options for the AI Enterprise “Essentials” edition. All subscription and consumption licenses include Business Standard support (9×5 coverage) by default, with an option to upgrade to Business Critical support (24×7 coverage) for an additional fee:

Table 1 – NVIDIA AI Enterprise Licensing Options (per GPU, NVIDIA list pricing)

License Type (Term)	List Price	Included Support	Optional Upgrade (Critical)
Subscription – 1 Year	$4,500 per GPU/year	Standard 9×5 (included)	+$1,100/year for 24×7 Critical
Subscription – 3 Year	$13,500 per GPU (3yr bundle)	Standard 9×5	+$3,000 (covers 3yr Critical)
Subscription – 5 Year	$18,000 per GPU (5yr bundle)	Standard 9×5	+$5,000 (covers 5yr Critical)
Consumption (On-Demand)	~$2 per GPU/hour	Standard 9×5 (3 support cases/year)	(Critical N/A for on-demand)
Perpetual License + 5yr Support	$22,500 per GPU (one-time)	Standard 9×5 (5 years)	+$5,000 (one-time for 5yr Critical)

Notes: These are NVIDIA’s list prices; negotiated prices can be lower. For instance, partners often quote ~$4,500 per GPU/year for AI Enterprise, but this “hefty sum” is negotiable. Business Standard Support provides phone/web support during business hours (with a 4-hour initial response), while Business Critical offers 24×7 support with a 1-hour initial response for urgent issues.

Deployment Models: NVIDIA AI Enterprise is designed to be flexible in deployment:

• On-Premises (Bare Metal or Virtualized): The software can run in your data center on NVIDIA-Certified servers (with NVIDIA GPUs). Each GPU in the server requires a license. Notably, if a server has no NVIDIA GPU (CPU-only environment), NVIDIA still requires one AI Enterprise license per server or instance, regardless of CPU count. This ensures even CPU-only usage of the AI Enterprise suite is licensed.
• Included with Hardware Purchases: NVIDIA often bundles AI Enterprise licenses with hardware. For example, each new NVIDIA DGX system includes AI Enterprise as part of its software bundle. Similarly, high-end GPUs (like NVIDIA H100, A800, and H200) ship with an embedded multi-year AI Enterprise subscription (e.g., 3 to 5 years) at no extra cost. These bundled licenses are tied to the specific GPU’s serial number and cannot be transferred to other hardware. Negotiation tip: If you’re purchasing NVIDIA hardware in volume, leverage these included licenses and even ask to extend or upgrade support as part of the deal.
• Public Cloud (Service or BYOL): NVIDIA AI Enterprise is available in major cloud marketplaces (AWS, Azure, GCP) for on-demand use. Enterprises can also Bring Your Own License (BYOL) to the cloud – i.e., purchase an annual GPU subscription via an NVIDIA partner and deploy the software on cloud instances you manage. In BYOL mode, one license is required per GPU in the cloud VM; if the cloud instance lacks a GPU, one license per instance is required (similar to the on-prem CPU-only case). Cloud deployment gives flexibility to scale, but be mindful of cloud vendor infrastructure costs on top of NVIDIA’s license fees.

Usage Considerations and Limits: AI Enterprise is a broad suite (AI model training, data science libraries, inference servers, etc.), and your contract should clarify the scope of usage. Standard licenses cover production use on the specified hardware but may not cover unlimited usage for certain components.

For instance, specific AI Enterprise components like NVIDIA Riva (speech AI) have their own usage terms – Riva’s free tier allows up to 1,000 hours/day of use before requiring a paid license.

While the core AI Enterprise license generally doesn’t impose a processing-hour cap (aside from the on-demand hourly billing), any promotional or trial licenses might include usage limits. Always review the product-specific terms for usage restrictions (e.g., limits on query throughput, number of concurrent users, etc.).

Ensure the contract or accompanying terms explicitly state what usage is permitted so you can avoid compliance issues or surprise costs later.

NVIDIA DGX Cloud: Pricing, Consumption, and Support Models

What is DGX Cloud? NVIDIA DGX Cloud is a fully managed “AI-as-a-Service” offering that provides dedicated NVIDIA infrastructure on leading cloud platforms.

Each DGX Cloud instance (often called a “node”) is essentially a virtual DGX supercomputer: it comes with eight high-end NVIDIA GPUs (A100 or H100) with high-speed interconnects, attached high-performance storage, and NVIDIA’s full software stack (including NVIDIA Base Command Platform and AI Enterprise software) pre-configured.

DGX Cloud is aimed at organizations that need ready-to-use multi-GPU clusters for AI development and training without procuring and managing physical hardware.

The service includes premium support and expert guidance built into the subscription.

Pricing Structure: DGX Cloud is sold on a subscription per node (per cluster) basis. Pricing is tiered based on commitment length and configuration.

For example, as of 2024, 12-month term pricing starts at $19,699 per monthly node for a base configuration (likely an 8×A100 GPU node).

Shorter commitments (e.g., month-to-month) are available but come at higher monthly rates, while longer commitments (12, 24, or 36 months) can secure better pricing.

NVIDIA does not publicly list all prices, but a longer-term or larger volume commitment generally reduces the effective monthly rate.

Always request detailed quotes for different term options.

• Tip: Align contract length with project needs. If you have an AI initiative that will run for 6 months, avoid overcommitting to 12+ months of DGX Cloud unless significant discounts justify it. NVIDIA may offer trial or PoC programs – for example, a short-term (e.g., 1-month) evaluation of DGX Cloud at a reduced cost or using cloud credits – to entice new customers. Leverage these trial periods to validate performance and requirements before locking into a long subscription.

Consumption and Flexibility:

DGX Cloud’s standard model is the monthly rental of nodes (customers rent dedicated GPU nodes by the month, which can scale up or down between billing periods). Unlike typical cloud VMs, you cannot spin DGX Cloud nodes up or down for just a few hours – you commit to at least a month for each node.

However, scalability is a key feature: you can often add additional nodes (if capacity is available) in the middle of a term or the next month, which is useful as AI workloads grow. To improve flexibility, negotiate for a ramp-up schedule: for example, start with two nodes in the first quarter, then expand to 4 nodes later at the same negotiated rate.

This ensures you’re not paying full price for capacity before you need it, but you lock in unit pricing for when you scale. Additionally, clarify what happens if you need to pause or downscale. Some contracts might allow reducing nodes when given notice or after a minimum period, especially if you’ve met certain spending commitments.

Infrastructure Commitments:

When budgeting DGX Cloud, consider the total infrastructure commitment. Each “node” is a powerful cluster (8 GPUs, networking, storage). At ~$20k per node/month, a 12-month contract for even a single node is roughly $240k.

Often, enterprises use multiple nodes (for multi-node training or separate environments for different teams), quickly reaching multi-million dollar commitments. Key negotiation points include:

• Volume Discounts: If contracting for multiple nodes or years, push for a discount tier. For instance, committing to 4 nodes for 12 months might secure a lower per-node price than a single node.
• Tech Refresh or Scale-Out: Ensure the contract allows you to upgrade to newer GPU nodes (e.g., switch from A100 to H100) or add more nodes at a pre-agreed price. Given NVIDIA’s rapid GPU release cycle, a clause to swap to newer hardware in a long-term deal can be valuable.
• Cloud Provider Fees: Understand that DGX Cloud on a given provider (Azure, Oracle, etc.) might bundle cloud networking or storage costs, but any additional services (data egress, etc.) could incur cloud provider charges. Clarify which costs are included in NVIDIA’s fee. NVIDIA’s materials claim no added charges for software or data movement in DGX Cloud. However, verify if, for example, large data egress out of the cloud is truly free or if the underlying cloud provider’s fees apply. Negotiate for credits or waivers on data egress fees to avoid lock-in (below) if not included.

Support Levels:

A DGX Cloud subscription comes with NVIDIA’s highest level of enterprise support built in. Unlike on-prem software, where Standard vs. Critical support is a paid choice, DGX Cloud pricing includes 24×7 “business-critical” support, direct access to NVIDIA AI experts, and even a dedicated Technical Account Manager (TAM) and customer success manager for your account.

This premium support is part of the value proposition (and cost) of DGX Cloud. Ensure your contract details the support entitlements:

• Confirm the SLA (Service Level Agreement) targets for uptime and capacity. NVIDIA DGX Cloud’s SLA targets ~99% service availability and 95% monthly capacity availability. That means NVIDIA commits that the platform will be up and usable 99% of the time and that 95% of your contracted GPU hours will be delivered (i.e., minimal downtime or shortfalls in available GPU time). If they fail to meet these, you are entitled to service credits as compensation. For example, verified SLA breaches can yield credits equal to the downtime (applied to future DGX Cloud orders). Negotiate for stronger remedies if possible – e.g., if uptime drops below a certain threshold for consecutive months, perhaps the right to terminate early or receive a larger credit.
• The support should cover software updates and compatibility: NVIDIA regularly updates the AI Enterprise software stack and Base Command. Verify that your subscription includes all such updates and that NVIDIA will maintain compatibility with the underlying cloud. Any feature promises or performance benchmarks (specific training speed-ups vs. DIY infrastructure) should ideally be captured in writing or at least referenced in the contract or SOW.

Example: One large enterprise negotiated a DGX Cloud deal in which the first two months were treated as a pilot phase, allowing cancellation with minimal penalty if performance or adoption goals were unmet.

NVIDIA provided this concession in exchange for the customer committing to a multi-node 1-year term if the pilot was successful. This kind of phased commitment can reduce risk – consider asking for a “step-in/step-out” clause if you are unsure of long-term consumption.

Key Negotiation Strategies with NVIDIA

Given its strong position in the AI market, negotiating with NVIDIA requires diligence and sometimes creative tactics.

Below are key strategies to improve cost efficiency, ensure technical flexibility, support scalability, and minimize lock-in in your NVIDIA contracts:

Improving Cost Efficiency

• Leverage Competition and Alternatives: NVIDIA knows it has the best GPUs, but remind them you have options. Use alternative suppliers or services as bargaining chips. For example, if NVIDIA’s quote is high, mention you are evaluating GPU cloud alternatives like CoreWeave or AWS’s native GPU instances as a stopgap. Even if not equivalent, demonstrating a Plan B (e.g., temporarily using another cloud or delaying purchase) gives you negotiating power. NVIDIA reps often use supply scarcity (“grab this deal now or wait 6+ months”) as a pressure counter by showing you won’t be stranded if you walk away.
• Volume and Term Discounts: NVIDIA’s pricing (for both hardware and cloud) has room for negotiation, especially on large deals. Aim for multi-GPU or multi-year discounts. For instance, if the list price is $4,500/GPU/year for AI Enterprise, in a deal for 100+ GPUs, you might push for a significant percentage off. It’s noted that volume commitments or end-of-quarter timing can “shave off a significant percentage” of hardware list prices, and the same logic applies to subscriptions. Ask for bundled pricing – e.g., “What discount if we commit to 3 years upfront?” or “If we also buy 2 DGX systems, can you include AI Enterprise at no extra cost?”.
• Bundle Deals Across Product Lines: NVIDIA would prefer to sell you the full stack (hardware, software, cloud). Use that to your advantage by bundling strategically. For example, if purchasing on-prem DGX servers and considering DGX Cloud for burst capacity, negotiate a credit on DGX Cloud usage or a reduced rate as part of the hardware purchase. If committing to DGX Cloud, request training credits or discounts on AI Enterprise licenses for any on-prem environment you maintain. NVIDIA has some flexibility on pricing when it’s incentivized to land a larger total deal.
• Pilot Periods and Proof-of-Concepts: Secure a trial period whenever possible. NVIDIA often agrees to short pilot access for AI Enterprise or DGX Cloud to prove value. You might get the first 30–60 days free or deeply discounted for AI Enterprise. For DGX Cloud, as mentioned, consider a month-to-month trial node before the annual contract kicks in. Ensure these terms are documented in the contract or a Proof-of-Concept addendum (verbal promises are insufficient). A well-structured PoC can save money if the solution underperforms, and it keeps NVIDIA motivated to support your success early on.

Ensuring Technical Flexibility

• Avoid Unnecessary Bundled Software: NVIDIA’s sales reps may bundle software like NVIDIA Base Command Manager or other tools with hardware and cloud offers, insisting they’re essential. In reality, you may have alternatives (open-source or third-party). Insist on line-item quotes and evaluate each component. For instance, if AI Enterprise or Base Command adds significant cost, but you have an in-house MLops platform or prefer Kubernetes, request to remove or opt out of those components for a price reduction. NVIDIA often charges ~$4.5K per GPU/year for management software, which you might not need if you have a capable team. By showing a willingness to unbundle, NVIDIA may drop the price or even include software for free to keep the overall deal.
• License Portability: Negotiate rights to reassign licenses to new hardware or cloud instances. If you retire servers or upgrade GPUs, you don’t want AI Enterprise licenses stranded on old machines. In enterprise agreements, request a clause allowing you to transfer AI Enterprise licenses to equivalent new GPUs (e.g., moving from an older GPU to a newer model in the same organization) without additional fees. This protects you from having to re-buy licenses during hardware refresh cycles. Similarly, for DGX Cloud, clarify if you can switch your deployment to a different cloud region or provider (e.g., move from Azure to Oracle Cloud) if needed – perhaps not freely, but ensure it’s discussed.
• Technical Compatibility and Exit Flexibility: Ensure the contract does not overly restrict integration with other tools. For example, using DGX Cloud shouldn’t mean you can only use NVIDIA’s software; you should be free to install your preferred ML frameworks or devOps agents. Verify that administrative access or APIs will allow you to use your tools in conjunction. Additionally, there is forward-looking flexibility: if, in the future, you choose a different AI platform, you can export your models and data (see Exit section) and possibly convert some of your investments. One idea is negotiating a conversion option: if you decide to bring workloads back on-prem, perhaps a portion of your DGX Cloud spend can be credited toward the purchase of on-prem DGX servers or vice versa. While NVIDIA may not readily agree, raising the point underscores that you value agility.

Supporting Scalability and Growth

• Guaranteed Capacity & Burst Options: If AI adoption is expected to grow, negotiate scalability provisions. For DGX Cloud, this might mean NVIDIA guarantees the availability of up to X additional nodes when you need them (perhaps with some notice). This prevents a scenario where you commit to a small deployment but later can’t scale because capacity runs out. In one contract, a client secured the right to burst to double their committed DGX Cloud capacity for short periods at the same per-node rate, provided they gave 30 days’ notice and used it within the same region. This clause ensures NVIDIA’s other customers’ demands don’t stymie your growth.
• Locked-In Rates for Expansion: Relatedly, ensure any additional purchases within the contract term are at equal or better rates. If you sign a 1-year deal for two nodes and later add a 3rd node, the 3rd should be at no higher monthly cost than the originals. Otherwise, you risk “rack rate” pricing on expansions. Ideally, negotiate a pre-priced option for extra capacity – e.g., “Customer may add up to 2 more nodes at $ X/ X/month each, coterminous with the original term.”
• Support for New Use Cases: As you scale, you may explore new AI use cases (e.g., edge deployment, inference hosting, etc.). Even if not needed on day one, discuss how those might fit in with NVIDIA. For instance, NVIDIA offers an inference service (NVIDIA NIM) and other cloud services – perhaps get contractual access to a few instances or a discount if you expand into those. At a minimum, keep renewal options open: avoid any clause that locks your spending to current services. If your needs change (from training to more inference), you want the flexibility to shift your contract value accordingly.

Minimizing Vendor Lock-In

• Exit Clauses and Termination Rights: Lock-in risk is high when dealing with unique tech like DGX. Always negotiate how you can exit the contract. A standard termination for breach (if NVIDIA fails to meet obligations) should be there, but also consider a termination for convenience clause – even if it comes with a penalty. For example, you might secure the right to terminate a multi-year DGX Cloud contract early by giving a few months’ notice and paying a scaled termination fee (like 3 months of charges). This at least caps your downside if business priorities shift. If NVIDIA doesn’t allow that, try to negotiate a short initial term or milestone (e.g., 6 months in, you can opt out of the second half of the contract). If you are worried about performance, include a “failure to perform” clause: e.g., if SLA uptime falls below 95% for two consecutive quarters, you can exit without penalty. Explicit exit terms force NVIDIA to satisfy you or face losing the account.
• Data Portability: To avoid lock-in, ensure you can retrieve all your data, models, and results from NVIDIA’s cloud. The contract should affirm that your data belongs to you and that NVIDIA will assist in exporting data if you leave. In practice, DGX Cloud uses NVIDIA’s NGC storage and container registry. Include language that you can use to download your stored data from the cloud upon termination, and NVIDIA will delete residual copies upon request. NVIDIA’s terms state that they will permanently delete your content from their cloud registry (with exceptions like system logs) if you ask. Use this to enforce a clean exit, with certificates of data destruction if needed for compliance.
• Avoiding Proprietary Traps: Be cautious of any proprietary formats or dependencies. NVIDIA AI Enterprise largely builds on open-source frameworks (TensorFlow, PyTorch, etc.), which is good. However, if you use NVIDIA’s proprietary pre-trained models or SDKs, clarify your rights. Can you take a model trained on DGX Cloud and deploy it elsewhere without restrictions? Ensure the answer is yes. Also, watch for license compliance audits (common in software deals, including NVIDIA’s). NVIDIA’s standard license gives them the right to audit your usage for up to 3 years after the term. To minimize disruption, negotiate audit provisions: e.g., at most one audit per year, 30 days’ notice, and use of a neutral third-party auditor. Also, insist that any audit will respect data privacy (no access to your sensitive data, only usage logs). Tightening the audit clause protects you from fishing expeditions and ensures any license true-up is based on facts.
• Legal Safeguards: NVIDIA’s standard contracts often limit liability and warranties extensively. While changing those boilerplates is hard, you should review them with counsel. Check if there are carve-outs for data privacy or gross negligence. If you operate in a regulated industry, you may need NVIDIA to commit to certain compliance standards (e.g., GDPR data processing agreements, which NVIDIA does provide, or FedRAMP compliance if U.S. government-related). Ensure that the governing law and venue are acceptable (NVIDIA often uses Delaware law or something similar in the U.S.). Pushing back on these legal points may not always succeed, but at least involve your legal team or an independent expert to spot any unusually risky terms. Remember that independent advisors like Redress Compliance can be engaged to review and benchmark NVIDIA’s contractual terms against industry norms, which is valuable to avoid one-sided commitments.

Legal and Compliance Considerations

When finalizing the contract, consider clauses that affect compliance, data management, and ongoing risk.

Key areas include:

Data Residency and Sovereignty: If your organization has requirements on where data is stored (due to GDPR, HIPAA, or other regulations), ensure the contract explicitly addresses data location. NVIDIA DGX Cloud is deployed on “leading clouds” (currently regions in North America, Europe, and Asia via Azure, Oracle, etc.), and customers can often choose the region for their DGX Cloud deployment. Specify in the contract or order form which region your data and computation will reside in (e.g., “EU-West data centre” or “OCI London region”). The NVIDIA Data Processing Addendum (DPA) will govern how NVIDIA handles personal data in the service, but you may need additional terms to ensure data stays in certain jurisdictions. If needed, leverage NVIDIA’s partnerships for sovereign cloud solutions – for example, NVIDIA and Oracle offer DGX Cloud in Oracle’s Dedicated Region for on-premises sovereign setups. Such options allow full control over location at a potentially higher cost. The bottom line: get commitments that NVIDIA will not move your data out of agreed regions without consent, and that they comply with relevant data protection laws.

Service Level Agreements (SLAs): We touched on SLAs in support, but from a legal perspective, ensure the SLA document is attached or referenced in your contract. Key SLA elements for DGX Cloud (99% uptime, 95% capacity) should be clearly understood. Verify the remedy: NVIDIA’s SLA remedy typically provides service credits for future use, not direct refunds. Those credits may expire if not used. Negotiate the credit terms – for example, if you’re near the end of a contract, a credit does little good; you might ask for the option to instead extend your term or get a payment. Consider an SLA on support response time (especially if you didn’t opt for Business Critical support on software). For instance, ensure Severity-1 issues get a 1-hour response if you paid for that. If continuous AI operations are mission-critical, you might need a custom SLA with higher uptime or faster recovery,

Exit and Transition Assistance:

A strong contract will include an exit plan. Besides discussing termination rights, NVIDIA must ensure that it will provide reasonable assistance during a transition. This might include data export (which should be automated via tools, but specify a format and time frame), keeping services running for a short overlap period if you’re migrating off (maybe you want a month of overlap to the new infrastructure), and not charging excessive fees for such assistance. If you have a perpetual license component (AI Enterprise on-prem) and you discontinue support or cloud use, clarify that you can continue using the last version obtained.

An exit clause should also handle the return or destruction of any NVIDIA-provided appliances or keys, as well as the revocation of your access to the cloud service. Aim for a clear exit clause, with no ambiguity on what fees, if any, apply if you choose not to renew. Avoid auto-renewal without notice – if NVIDIA insists on auto-renewal for subscriptions, demand a long notice period (60-90 days) before renewal and the ability to opt out at that point.

Audit Rights and Compliance:

As mentioned, NVIDIA’s contracts give them audit rights to ensure you’re not overusing licenses. While compliance is important, you should insert fairness: require reasonable notice (e.g., 30 days), audits to occur at most once per year, and any audits to be conducted in a way that minimizes disruption to your business. If you’re found non-compliant, typically, you must pay true-up fees and possibly back support – try to negotiate a waiver of any penalties if you promptly rectify the shortfall.

On your side, consider adding a clause that NVIDIA will comply with your company’s security policies during audits (especially if auditors come on-site). If you operate in a highly regulated space, consider your right to audit NVIDIA – you likely can’t audit their internal operations. Still, you might request the right to review their security and compliance reports (SOC 2, ISO27001 certifications, penetration test results) annually to ensure their service meets your standards.

Liability and Indemnification:

Carefully review liability clauses. NVIDIA will cap its liability (often to the fees paid) and exclude indirect damages – standard practice. Ensure there is at least mutuality (you also want to cap your liability).

Pay attention to any indemnification: if NVIDIA software infringes someone’s IP, NVIDIA should defend you – check for an intellectual property indemnification from NVIDIA, which is common in enterprise software contracts but must be explicitly stated. Conversely, avoid overly broad indemnities on your side.

For example, you shouldn’t have to indemnify NVIDIA for using their products as intended, except perhaps if you violate license terms. If such language exists, negotiate it down.

Finally, independent experts should always be consulted for such negotiations. Firms like Redress Compliance specialize in software and cloud contract optimization and can provide benchmarks (e.g., typical discounts others achieved on DGX deals) and help spot hidden risks.

Unlike resellers who might have incentives aligned with NVIDIA, they operate solely on the client’s side, ensuring that strategies like bundling, true-down rights, and audit defence are crafted in your favour. Engaging an expert can help validate that the contract terms meet your cost efficiency, flexibility, scalability, and minimal lock-in goals.

Real-World Takeaway:

Negotiating with NVIDIA is more about leveraging your position and foresight than the line-by-line contract terms. One CIO who recently secured a major AI deal with NVIDIA noted that bringing detailed usage forecasts and alternative scenarios to the table changed the tone of the negotiation – NVIDIA realized the customer had done their homework and was prepared to walk away or scale back if terms weren’t favourable.

In the end, NVIDIA conceded to a ~20% price reduction on AI Enterprise subscriptions and granted a more lenient transfer right to new hardware in exchange for the customer committing to NVIDIA’s solution over a competitor.

The lesson: be informed, stay firm on critical needs, and use independent counsel to navigate the complexities. With that approach, even in a seller’s market for AI technology, you can secure a contract that meets your organization’s financial and technical objectives.