For the past year, I’ve been monitoring global supply chain dynamics as GPUs, memory, storage, and networking hardware experience a simultaneous crunch. Through our extensive relationships across various data center locations, vendor relationships, customers, and more, we’ve found that the upcoming period will likely be one of the most constrained eras for hardware, specifically for AI and data center purposes.

This problem isn’t exclusive to GPUs. While overall it remains easier to source CPU-based servers, companies are still feeling a squeeze there due to memory shortages and giants like Apple stockpiling inventory, among other constraints. 

Providers and enterprises that aren’t up to date with the latest data on hardware costs, availability, and supply are going to struggle if they don’t adapt in the coming months. For the small players, the keys to weathering the storm start with procuring hardware well ahead of future growth plans, expanding planning horizons, and performing robust evaluations of infrastructure partners.

What’s driving the shortage

At its core, the ongoing supply shocks are a ripple effect caused by the AI boom. For example, one area that’s acutely affected is the DRAM production sector. As recently as six months ago, we used to buy a whole CPU compute system for around $15,000. Today, the cost is around $70,000 for the memory alone.

What’s contributing to this is the large-scale reallocation of global semiconductor manufacturing capacity. Leaders like Samsung, SK Hynix, and Micron have shifted the fabrication line toward high-bandwidth memory for AI accelerators, which means supply for standard, enterprise-grade DRAM is shortened. The result is that all memory production leaders have indicated their NAND capacity is sold out for the rest of 2026. 

Server CPUs are another problem area. While lead times are roughly the same as six months ago, the prices are dramatically different, increasing between 5 and 7 percent month over month. Part of the reason is manufacturers are naturally emphasizing production of GPUs over CPUs. But even that shift faces hurdles.

The capital driving this demand isn’t a secret. The largest hyperscalers are on track to spend upwards of $700 billion combined in 2026 on hardware, with some projections reaching $7 trillion in data center investment by 2030. As those with the most capital throw more and more money at their goals, those without deep coffers are most often left behind.

Where independent cloud providers get stuck

Due to the top hyperscalers controlling the majority of server capacity, those who are not hyperscalers themselves are often stuck in a difficult position, forced to compete for the same components that others can buy in volume, at prices that have increased since their current contracts were set.

Operating a cloud company has become way more capital-intensive than it was just six months ago. Prices are higher and you have to buy way, way sooner than you used to. Waiting even 30 days to place an order means you’ll face an entirely different sourcing environment.

Independent providers are playing a guessing game between their customers and the market. If they raise contract rates too high, customers will flock to competitors who can provide the same service at a more reasonable price. However, if they lower prices too much, they’re betting that future hardware prices will slow down and they can properly expand or upgrade their capacity to stay competitive. As such, many providers are already feeling the squeeze as they made bets that are cutting heavily into revenue from one side or the other.

What providers can expect in the future

Because the AI trend is going to be a long-term reality rather than a temporary disruption, capacity planning is way more important than it was six months ago. AI is growing exponentially and only a small fraction of companies have actually implemented it so far. There’s much more growth ahead.

Since negotiating with customers against hardware pricing is key for providers, it’s imperative to have a strong understanding of how things will progress in Q4 and beyond. Conventional DRAM contract prices rose over 90% in Q1 of 2026 and server CPU prices have continued to rise throughout 2026, with experts projecting that hardware prices will continue to grow exponentially. 

Availability has become less predictable for specific components across various regions. Organizations that make the assumption of on-demand cloud capacity should revisit that notion. Lead times are now longer, and OEM quote validity windows are shortened. Components are being priced on timeframes measured in days, on some occasions. Decisions delayed by internal bureaucracy can result in massively different costs by the time procurement is closed.

Planning ahead is what keeps business afloat

During COVID, providers faced a similar situation with supply heavily constrained, demand outpacing production levels, and outcomes that differed based on when customers started their planning. Customers who reached out early and secured infrastructure ahead of growth events had what they needed. However, those who waited were faced with a tough, constrained allocation queue. Now, in 2026, we’re facing a similar structure, with the added factor of some producers prioritizing higher-margin components.

The most immediate priority should be the expansion of planning horizons. CIOs have to become adept at projecting earlier how their business is going to grow, in terms of what infrastructure capacity they’ll need in 12 or even 24 months. 

Infrastructure decisions that previously operated on a quarterly basis must speed up. This means starting procurement conversations ahead of growth events, rather than reacting when the need appears. Budget assumptions need to be updated in real-time as well. Infrastructure costs should be modeled using current pricing with escalation assumptions automatically built in.

Source capacity, not just components

Finally, when evaluating infrastructure partners, the most important question is no longer just the headline pricing. A provider with existing facilities, power, and space can bring capacity online without negotiating every agreement from scratch. It’s also extremely helpful if they have vendor contracts that predate the shortage, ensuring a steady flow of business.

CIOs, along with everyone else, are interested in how soon you can put GPUs online. Because data centers are being pushed to the limit, we’ve seen some outages across the U.S. So you have to ask about certifications and infrastructure reliability, including things like power and cooling. That has to get equal consideration to how soon they can get compute online.

Customers understand what’s happening

This is not a behind-the-scenes development. Cloud providers across the spectrum, from the smallest bootstrapped startup to hyperscalers, have had to adapt to scarce availability. Customers have already seen prices rising on new deployments and they are aware the whole market is going up, from hardware to power costs.

The constraints being put on the manufacture and distribution of components by the AI boom are going to have a profound impact on how you grow your business. In fact, the plans you have in place for 2027 may already need revisiting. 

But you can adapt by maintaining real-time updates on supplier inventory, pricing, and delivery dates; contacting manufacturers and vendors well in advance of growth events; and shopping for capacity alongside components. That will help you head off any surprises so you can continue to meet customer demand in the future.