An enterprise team picks an AI vendor after comparing features, pricing, and compliance checkboxes. But six months into deployment, service level agreements (SLAs) begin to slip.

The software didn’t change. Instead, the vendor simply couldn’t secure enough GPU capacity to keep pace with demand.

That’s the procurement gap most buyers miss. They screen AI vendors like any other software purchase, yet they skip the infrastructure and financial health checks that actually predict who can scale with them. 

Global AI infrastructure spending will hit $497 billion in 2026, and that scale of investment is exactly why IT leaders need to check infrastructure and financial health before signing their next contract

The Infrastructure Behind the AI Tools You’re Evaluating 

Enterprise buyers already know how to vet an AI vendor on paper. They check integration documentation, compliance certifications, and pricing tiers. But none of that tells you whether the vendor can keep running its models next quarter. That answer lives in a different layer: the physical hardware and power infrastructure behind the tool. 

GPU Scarcity and What It Costs Vendors 

NVIDIA controls over 80% of the AI chip market, so most vendors chase the same limited pool of hardware. GPUs cost money at scale, and vendors without deep capital reserves compete for allocation against hyperscalers like Microsoft Azure and AWS. 

AMD’s MI300X and MI325X chips ease GPU allocation pressure for larger AI vendors competing against hyperscalers, though smaller vendors are still hitting allocation limits. 

NVIDIA’s data center revenue hit $193.7 billion in fiscal 2026, up 68% year over year, highlighting the intense competition for that supply. SLAs are usually the first thing to slip when a vendor can’t secure enough GPUs, and capitalization often decides whether that vendor can hold the line.

Why Power Is Now the Primary Scaling Constraint 

GPUs aren’t the only bottleneck. Wood Mackenzie projects $9 trillion in global AI and data infrastructure investment through 2040. Power delivery has become the primary constraint on scaling AI infrastructure, ahead of GPU supply.

That’s part of why NVIDIA, Google, and Microsoft are pushing the industry toward 800 VDC power architecture, since current systems can’t handle the density new AI workloads demand. For a CSCO, the takeaway is simple. Vendors that can’t fund next-generation power infrastructure hit capacity ceilings, and those ceilings turn into delays for enterprise clients. 

Why Vendor Financial Health Is a Procurement Variable 

None of this infrastructure math matters if the vendor can’t pay for it. Financial health belongs in procurement too, since maintaining compute access under a multi-year contract depends on capital, not just today’s product. 

Reading Private Market Signals as a Risk Tool 

Private market funding activity works as a forward-looking signal. Check how recently and how large a vendor’s last raise was, whether that money went toward infrastructure or just headcount, and what secondary market trading says about investor confidence in the roadmap.

Stability AI has raised more than $200 million across several funding rounds since its founding, and in August 2025 partnered with NVIDIA to launch the Stable Diffusion 3.5 NIM microservice for enterprise deployment. That combination signals real investment in enterprise-grade deployment rather than just research.

Researching whether a vendor has accessible investment options, including questions like “Can you buy Stability AI stock?”, shows how the market prices in that staying power. That question applies to any vendor under consideration, not just the ones making headlines.

Vendor Lock-In and the Compute Constraint Risk 

Vendor lock-in raises the stakes further. Ask whether the vendor can maintain pricing, uptime, and feature velocity over a three- to five-year horizon. Self-hosting open-source AI models hedges against vendor lock-in, though it carries a higher total cost of ownership than a managed vendor contract.

What to Check Before Committing to an AI Vendor 

Once you understand where the infrastructure risk sits, it helps to fold it into your actual vendor selection process, right alongside the criteria you already use. Here’s what deserves a spot on that list before anyone signs anything.

• Compute sourcing: Compute sourcing determines whether an AI vendor relies on shared hyperscaler capacity or owns dedicated compute infrastructure. Find out which one applies, since shared setups expose you to someone else’s capacity constraints, not just the vendor’s.
• GPU allocation transparency: GPU allocation transparency means a vendor can back its SLA commitments with documented infrastructure rather than optimistic estimates. Ask for that documentation directly. A vendor who can show you real allocation numbers is telling you something an estimate never will.
• Capital runway: Capital runway reflects how recently a vendor raised funding, how much it raised, and whether the vendor allocated that funding to infrastructure rather than headcount. A funding round that skipped compute spending won’t help them when GPU demand spikes.
• Power infrastructure readiness: Power infrastructure readiness determines whether a vendor can scale capacity without hitting a legacy power delivery ceiling. Check whether they’re investing in next-generation power delivery or still running on legacy setups.
• Contract exit provisions: Contract exit provisions define what happens to an enterprise’s service terms if a vendor’s compute access deteriorates mid-contract. Read that clause closely. It matters more than almost anything else in the agreement once infrastructure becomes the risk.

Why Infrastructure Awareness Leads to Better Vendor Decisions 

Treating AI procurement as a software-only decision means taking on infrastructure risk that never shows up on a pricing sheet. The computer supply chain faces the same pressures as any physical logistics challenge (resource scarcity, capital requirements, and capacity constraints), and each one shapes whether a vendor can actually deliver years down the line.

A complete evaluation weighs all three alongside the usual specs and certifications, because GPU access and power infrastructure are part of the product. As the AI vendor market matures, vendors with deep infrastructure and sufficient financial runway will continue scaling with their enterprise clients, while those without will consolidate or exit.