Lead times of nine to 12 or even 18 months. Costs rising by 35%, 45%, even 50% to 200%. More than halfway through 2026, the market for IT infrastructure that’s crucial for enterprise projects, including those involving artificial intelligence, is strapped.

Memory is at the root of the shortages. Memory prices “have risen by 50% to 200%, resulting in PC prices increasing by 35% to 45% and some server prices rising over 125%,” according to Jon Forest, VP analyst at Gartner. Network switches also need memory, albeit in lesser amounts than servers, so they are not immune, with prices and lead times likewise rising dramatically.

Industry experts agree that most of the issues stem from hyperscalers gobbling up memory capacity, which trickles down to servers, storage systems, and networking devices. But while the source of the problem may be new, supply chain disruptions are far from unprecedented.

As a result, industry insiders are not short on advice on how best to deal with the situation, with tips including making better use of what you have, considering options beyond your usual scope, and lots of planning with your vendors and internal finance teams.

State of the problem

Just how bad is the current supply chain problem? “It’s pretty bad,” says Matt Kimball, vice president and principal analyst with Moor Insights & Strategy. Companies accustomed to 30- to 45-day lead times for various infrastructure are now looking at 6, 12, or even 18 months.

“It’s real, and I’m hearing it from companies of all sizes, from the 1000-server to the 10,000-server shops,” Kimball says.

“Memory costs are expected to rise sharply well into 2027 and will reach up to 25% of network hardware expenses by the end of 2027,” according to an email Gartner’s Forest sent to Network World. The figure below shows the timeline Gartner expects for memory prices, and Forest notes that the same timing applies across networking, storage, and compute infrastructure. 

Gartner

“Enterprise network equipment pricing is projected to increase by over 20% in 2026. This upward trend is anticipated to continue with a further rise of 3% to 5% entering 2027, with no signs of price reduction until the end of 2027.”

But “reduction” will likely look more like “stabilization.”

“That’s something a lot of people don’t like to talk about. But let’s say prices went up 40%, they may come down five,” says Phillip Privett, senior vice president of vendor management with the global distributor and value-added reseller TD SYNNEX. “They’re not going to come down 40%.”

Perhaps worse, compared with past disruptions caused by issues such as fires in chip fabrication factories or the Covid pandemic, Kimball says this one is “durable” because its cause—the AI wave—is more long-lasting and just getting started.

“This AI inference wave we’re hitting is just beginning. It’s going to be longer and bigger than the training wave,” he says. “It’s impacting everything, from AI infrastructure to the traditional stuff that’s standing up your virtualization and cloud infrastructure.”

No vendors seem to be immune, not even the likes of Cisco, which makes its own Cisco Silicon One chips. Or, at least, it designs the chips; they’re actually manufactured by the Taiwan Semiconductor Manufacturing Company (TSMC), the same company that makes many of the other chips that are in such demand. And that’s only one component of many that comprise a switch.

On the other hand, the margins Cisco gets from enterprise sales are far greater than those from hyperscalers because Cisco sells mainly just hardware to hyperscalers, whereas enterprise sales generally include software and services as well. So, Cisco has incentive to keep enterprise customers happy and maintain the 66% margins it reported in Q3, its latest quarter.

Still, Cisco must deal with the same shortages as other vendors.

“I wouldn’t say any company is faring better than others,” says Neil Anderson, vice president and CTO for cloud, infrastructure, and AI solutions at World Wide Technology (WWT). “There may be nuances that some suppliers are employing to balance it to some extent, but I fail to recognize a supplier that’s not having almost the same issue.”

Cloud storage vendor Backblaze is one company that’s facing equipment cost and availability issues. “There are different types of shortages occurring in multiple places, all driven by unusual market demands, really by just a handful of very large buyers,” says James Rowell, senior vice president of operations with Backblaze.

Backblaze is constantly forecasting and monitoring demand triggers, Rowell says. That involves close alignment with the sales team to forecast client needs, as well as paying attention to historical trendlines to predict upcoming demand from new deals and growth with existing clients. But the company also looks for “unnatural market-related triggers” that would cause a spike in utilization.

With hyperscalers buying up vast amounts of capacity, “This is definitely an unnatural phase,” Rowell says. “For about for the last 12 months, I would say there’s been somewhere between a 15% and 30% uptick in costs,” especially in terms of servers and compute disks.

On the positive side, at least for Backblaze, the company is also seeing an uptick in business from an interesting source: AI companies. “We reported in the last earnings period a 70% increase in AI companies using our platform,” says Patrick Thomas, vice president of marketing at Backblaze. “That’s massive.”

On top of that, the company is seeing an uptick in deals from enterprises that can’t get the storage capacity they need or want on-prem. “There’s a general market nervousness where we’ve got potential deals coming our way because those organizations are concerned about being able to do it themselves,” Rowell says.

While some expect new chip fabrication plants currently under construction will ease memory supply constraints, Privett doesn’t buy it. “I don’t see it getting better anytime soon,” he says. “Building a new fab is a two-year process.”

Advice: Start with the basics

Enterprises, then, must play the cards they’re dealt. For Moore Insights’ Kimball, who did stints as an IT exec with the states of Florida and Oregon, that starts with making the most of what you have.

Such a strategy is “shockingly not implemented much” across the companies he sees. “A simple capacity planning exercise can free up a lot of resources.” That includes virtualized servers running at just 20% to 30% utilization as well as extending the life of existing servers. While 15 or 20 years ago it was common to refresh every four years or so, companies can often get six or seven years out of today’s servers.

While such strategies won’t solve your AI compute challenges, they can certainly help support your ongoing operations and free up budget for AI and other modernization projects, he says.

“Sweat your assets,” agrees Privett of TD SYNNEX. “Work them as much as you can, add only what you need, get extensions on your licensing, renewals on your services agreements and things like that. Just sweat it out a little longer.”

If you have budget to spend but can’t get the hardware you’re after, buy something else, says WWT’s Anderson. “Look at things that are not tied to those components, like software projects or SaaS licensing,” he says.

Get friendly with finance teams

Numerous experts recommend regular meetings with your CFO or finance teams to keep them apprised of what you’re up against so the company can plan accordingly.

Gartner’s Forest advises using rolling 12- to 24‑month forecasts and engaging early with suppliers to identify constrained components and SKUs. Committing to quarterly or monthly buys can help you avoid long-term agreements that extend past the rapid increases we’re seeing in 2026, he says.

Also engage with the financing arm of your equipment vendors, some of which are offering financing incentives, Privett says. Compute vendors in particular are offering subsidized financing, deferred payments, and low-cost financing for the first year or so. “Those are huge opportunities to take advantage of,” he says.

By engaging with finance teams, IT groups can conduct budget allocation exercises and try to come up with ways to make the financials work. The last thing you want to do is surprise them with additional budget requests out of the blue.

Kimball recalls his days with the state of Florida, when all budget requests were examined by a technical review working group—which was designed to be hostile.

“I can’t imagine going to them and saying, ‘Oh, did I say that was a million dollars? It’s actually $2 million. I need you to write me a bigger check,’” he says. “I would walk into one of the swamps in Tallahassee and get eaten by the alligators instead of doing that.”

Work with your vendors and VARs

As you put plans together, lean on your vendors for help, including channel partners such as value-added resellers (VAR) and national resellers. “Work with them to map things out and understand what your workloads will look like,” Kimball says.

That’s what Backblaze’s Rowell regularly does with his suppliers. He lays out his forecast for the year, with commitments on what Backblaze will definitely buy, as well as scenarios that account for rapid growth, say, 2x. “And they’ll come back with, ‘Well, okay, no problem,’ or maybe they say we need to put in an allocation right away, or we won’t be able to get what we may need,” he says.

Similarly, he sits down with his CFO regularly to map out predictive models that factor in inflation, price hikes, and the like. The idea is to plan out multiple scenarios, so you don’t get blindsided.

“If you don’t do that, you’ll get caught with your pants down, on the upside-down end of spectrum,” he said – meaning not having the capacity to take advantage of market opportunities.

Acquiring the capacity you need to meet project demand may also mean being flexible in terms of your equipment choices. If you’re a Dell shop but can’t get Dell servers, maybe you go with Lenovo, Kimball says.

“You’ve got to figure out how to use all this silicon and infrastructure in a heterogenous way to serve your needs,” he says. That’s especially true when it comes to AI infrastructure. “If you think you’re going to go with 100% Nvidia for everything from RAG [retrieval augmented generation] to inferencing at the edge, you’re kind of crazy, not because of cost but because of availability.”

Look at alternatives, including AMD and cloud solutions, while staying mindful of how it all plays together. You may not be able to get Nvidia GPUs, but AWS, Azure, and Oracle Cloud have them, Kimball notes.

Be strategic, perhaps by using cloud offerings to handle certain tuning or inference workloads, then bringing them back in-house when appropriate. “Have a better understanding of what absolutely has to be on prem and what can be in the cloud,” he says.

That’s good advice, says Backblaze’s Thomas. When it comes to AI, think about performance tiers and the range of use cases you have. They don’t all need top-tier performance.

“People get wrapped around axle of needing the top end. There’s a lot of flexibility in the edges, innovation in different hardware and software,” Thomas says.

Gartner likewise advises companies to increase configuration flexibility and expand sourcing paths. That may include buying from secondary markets and lease-return programs to preserve continuity with existing infrastructure until the shortages pass, Forest says.

Get started somewhere

Even if you can’t acquire or have to wait for the infrastructure you need, don’t let that keep you from getting started with AI or other modernization projects.

Options include public cloud and neocloud providers, Anderson says. WWT also provides capacity in its own lab so customers can get started with proof-of-concept projects. “Don’t just throw your hands up. We can help you find access to capacity,” Anderson says. “Production-scale AI may be delayed, but don’t let that derail your strategy.”

Colocation providers may likewise be an option, especially if enterprises are struggling to acquire high-end networking equipment. Networking is a key value proposition for colocation providers, in that they have built-in connections to various cloud providers and other ecosystem players.

Equinix, for example, has 280 data centers in 77 metropolitan areas, says Phil Read, senior director, colocation product management for the company. If you have the compute infrastructure, Equinix can help you with the high-end connectivity required both intra- data center and at edge facilities.

It also has partnerships with the likes of Cisco and Nvidia for “ready-to-go AI connectivity,” Read says. That means Equinix offers the right infrastructure to meet the requirements of high-end compute solutions in terms of power density and cooling. Such power densities are significant, requiring 120k VA per rack and up. “There’s plenty of talk about a megawatt rack,” he says.

Power is a significant issue in this entire discussion, Privett says. Older installed computing infrastructure likely consumes far more power than newer systems, which is an argument for upgrading as soon as possible.

“If you modernize today, you could substantially reduce the number of servers needed to support the same applications at a much lower power consumption rate,” Privett says. He advises sitting down with folks from the OT side of the house to make sure power is available for whatever you want to do. In many areas, power is at a premium.

If your plans include installing GPU environments in your own data center, WWT advises you not to delay. “We’re telling customers, you need to talk with us and get that designed, get that ordered, because it will take quite a bit of time until it actually ships and we’re able to install it,” Anderson says.

Moor Insights’ Kimball agrees. “You have to order these parts today if you want to see them hitting your dock, your warehouse, or your office 12 months from now.”

Dell Technologies is acknowledging that infrastructure and storage supply still can’t keep up with agentic AI’s insatiable appetite for resources.

The company this week reported a “record” AI backlog, with $95 billion in orders waiting to be filled. This dovetails with quarterly earnings reflecting a more than 50% year-over-year increase in AI demand.

On an earnings call, Dell COO Jeff Clarke acknowledged that supply constraints start with servers and storage, and span the stack to “just about every product going through a leading node.”

“We are doing everything we can to get more supply,” he said. “In today’s environment, that’s a very difficult task.”

A glimpse of infrastructure demands ahead

Dell reported that, in its financial quarter ending July 31, its revenue was $47 billion, reflecting 58% year-over-year growth. Moreover, revenue in its Dell Infrastructure Solutions Group (ISG) increased 89% to a record $31.8 billion.

Much of this growth is in servers, notably traditional central processing unit (CPU)-based servers that are increasingly supporting agentic AI workloads. Demand is “exceptionally strong” in this area, with earnings up 122% year-over-year.

Perhaps most tellingly when it comes to the ongoing demand, the company booked nearly $61 billion in AI server orders in the three months ending July 31; all told, over the last 12 months, it has inked more than $130 billion in AI server orders.

Clarke reported that Dell converted $131.7 billion of demand into orders over the last year, and that demand is broadening across enterprise customers, neoclouds, and sovereign cloud providers. To illustrate his point, he noted that the number of customers using Dell AI Factory, the company’s platform built to support AI workflows, has surpassed 6,500, and of those, 3,300 signed on in the last three quarters. Clarke pointed out that, by contrast, it took the company two years to sign on the first 3,200 after debuting Dell AI Factory in May 2024.

“Agentic demand is reshaping the data center,” Clarke said. Inference is “pure demand in our industry.” In fact, Dell anticipates that 3,600 quadrillion tokens will be in use by 2030, representing an 87x increase from today. Further, over that same period, training demand is predicted to grow to 850 zettaflops, a 5x jump.

“Enterprise agentic AI is expected to be the single largest workload by 2028,” Clarke said, and by 2030 will account for 75% of all data center demand.

Enterprises clamor for traditional servers

Dell is seeing a growing trend of customers requiring “meaningful CPU compute capacity” to support AI and agentic workflows. As evidence of this demand, in just its last two financial quarters, it has generated nearly as much revenue from traditional servers and networking as it has in any prior full year in company history.

Most of this growth comes from existing customers accelerating their investments in traditional IT environments to refresh, modernize, and bolster performance, efficiency, and resiliency. Dell anticipates “significant and durable” refreshes ahead, and heightened security and resiliency requirements are also increasing demand.

“AI requires modern, disaggregated architectures that keep data accessible and in motion across compute, storage, and networking,” Clarke noted. It is much more than assembling and delivering components; AI deployments require significant engineering, design, and deployment expertise. Some customer engagements, in fact, require upwards of 50 unique designs as enterprises optimize for workload performance, power, cooling and the data center environment, he claimed.

Enterprises want new servers with more cores, more dynamic random-access memory (DRAM), and more storage. However, the constraints remain the same: “DRAM, DRAM, DRAM, followed by NAND, NAND, NAND [flash memory],” Clarke said. There are “spotty” CPU and disk drive shortages, and constraints all the way down the supply chain, from microcontrollers to drives to transistors.

Large enterprises and multinational corporations across the globe “would prefer to have products now if we had the supply,” he said. “We are supply constrained in the sense of what we can build in any given quarter.”

This has led Dell to plan accordingly and optimize configurations with what “bits and bytes” they do have coming in to maximize outputs, with a focus on “getting it out the door,” Clarke said. There are associated lead times that the company is working through, but they’ve been able to “realize greater shipments.”

“We’ll continue to focus on trying to get more supply, and take the supply we have and optimize the output,” he said.

Reflecting increased need for storage as enterprises prep, manage, and protect huge volumes of data, Dell has also seen strong growth across its PowerFlex, PowerStore, PowerProtect, and PowerVault products.

“Demand remains broad based; enterprises continue to modernize their storage environments as data growth increases the importance of keeping data available and secure,” Clarke said.

How customers respond to shortages

Clarke acknowledged that modernization is driving higher core counts, more DRAM, and more storage. Those configurations “cost more than they did last quarter, and the quarter before, and the quarter before.”

Customers are adjusting to these price increases, he noted, deferring purchases because they are unable to sufficiently flex existing budget dollars. In other cases, enterprises are placing orders further in advance to ensure they have access to constrained supplies. “Large, sophisticated customers are acting, first and foremost,” Clarke said. Some are collaboratively planning with Dell to gain a view of their needs further into the future.

“That is a new phenomenon,” he said. “We are working through this demand environment that’s well ahead of supply, helping customers manage.”

This article originally appeared on Network World.

On top of July’s 30% price hikes across almost all of its product lines, Nvidia is reportedly preparing to raise prices of servers, including those powered by Vera Rubin and Grace Blackwell chips, by 15%, due to skyrocketing memory prices.

That 15% increase for systems being delivered in early 2027, reported by Bloomberg and other business media, is seen as part of a series of expected price hikes throughout AI deployments.

Scott Bickley, advisory fellow at Info-Tech Research Group, said that he sees Nvidia’s move as one that is only passing along its own rapidly increasing costs. But rather than price gouging because of its close-to-monopoly market control, Bickley’s calculations suggest that Nvidia is likely eating some of its costs, and is only passing along a fraction of them to its largest customers.

But not all AI-related costs are increasing; per-token prices appear to be dropping, he said. That gives CIOs a potential strategy to manage costs by pushing approaches that will reduce the reliance on memory.

“The workload cost is going down per token while the underlying hardware and infrastructure costs are going up,” Bickley said. “If you are directly building out your own clusters, this is an automatic uplift to an already egregiously expensive solution. If you are buying your own hardware, you’re going to have to suck it up. You are not going to negotiate your way out of this.”

But, he added, CIOs should also be able to get more mileage out of the clusters they are currently running, via techniques such as model routing, compression, and batch processing.  

Gaurav Gupta, VP analyst at Gartner, noted that the Nvidia price hikes are reflective of the many pricing increases throughout the AI environment. 

“Memory prices are going up, especially HBM and LPDDR5, but there are other aspects, like leading-edge foundry wafers, advanced packaging, and other component shortages,” Gupta said, adding that those issues generate “longer lead-times, which typically translates to higher prices.” And he does not expect the situation to improve any time soon. 

“In the current environment of strong demand and limited supply, we expect this situation to continue in the near to mid-term,” he said. “This means higher costs for those deploying these servers/systems and for those renting compute in the cloud, including software vendors/model builders, and others.”

Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, agreed.

“This is a supply and demand problem, and it’s likely to reach a plateau and eventually improve once memory production ramps up to meet the current demand due to AI, but I don’t think this will happen in the next few months,” Villanustre said. “For now, CIOs will need to contend with the current market conditions.”

But he also agreed with Bickley’s suggestion that CIOs try to squeeze more value from the RAM they already have.

“Some AI model vendors are adapting their models to run better in memory constrained environments,” Villanustre noted. “For example, Gemma E4B and similar models by Google now use a hierarchical tiered model that allows them to pull only the necessary parts of the model into memory instead of holding the entire model in RAM. These models have a much larger effective number of parameters than the memory that they require.”

And, added Mike Wilkes, enterprise CISO at Aikido Security, this means that the Nvidia price hikes may do some good if they convince enterprises to adopt a more thoughtful approach to AI deployments. 

“Enterprises have spent the last few years treating frontier-model tokens almost as an infinitely elastic utility, sending workloads to the biggest model whether or not the task required frontier-level reasoning,” he said. “Higher infrastructure and token costs should force much better workload discrimination.”

He observed that the right enterprise AI architecture is increasingly hybrid: reserve 10% for frontier model consumption for problems that genuinely require it, while pushing classification, extraction, summarization, routine agent actions, and other bounded workloads toward small language models (SLMs) and open-weight models running on infrastructure that the enterprise controls.

“That gives CIOs leverage against price gouging or unilateral price setting,” he pointed out.

This article originally appeared on NetworkWorld.

OVH is increasing the prices of its servers, some by as much as 87%, for both new and existing customers, blaming AI’s insatiable demand driving the rising cost of the RAM and storage it uses in its data centers.

The European cloud operator specializes in low-cost bare metal and public cloud offerings.

CIOs will be familiar with the balancing act OVH has had to perform over the last year. In a Monday post explaining the upcoming increases, OVH chairman Octave Klaba wrote on X,  “We have to place the right volume of orders, month by month, over 12 months, with no guarantee of the purchase price and without knowing what will be the real demand from our customers.”

Still, he added, “even though our prices are increasing, we remain the cheapest on the market for bare metal and public cloud; where before we could be 3x cheaper, we will be 2x cheaper (if our competitors don’t increase their prices).”

The increases will hurt hard-core gamers hardest, with the cost of the company’s most recent gaming servers rising 87%. (Older gaming instances are unaffected.)

High Grade, high price

But enterprises will also feel the pain from climbing component costs: OVH’s latest High Grade bare metal servers, with up to 2 x 96 cores of AMD Epyc 9005 series processors, 36 hard disks per server, and high-density cooling systems, will go up in price by 59%; older models built to the 2024 spec will go up 26%.

Lower-performance servers will also see increases of 40%-49% for the most recent models, and 26%-37% for older models.

The new prices take effect from Sept. 1 for new orders, and from Oct. 1 for renewals.

It’s not just baseline server prices that are increasing; optional additional memory and storage are going up in price too. OVH already increased the cost of these extras for new server orders as of July 1, with RAM prices rising 127% and disks 89%. From Oct. 1, renewals will be affected too, with the price of additional RAM in the latest servers rising by 40%, and that of larger disks by 15%. For servers built to 2024 specs, the increases will be 20% and 10% respectively.

Existing customers can lock in current prices for servers already in production for up to four years if they pay in advance by Oct. 1, Klaba wrote. Existing commitments will not be affected by the increases until they are due for renewal.

Small instances, big increases

The price rises are more nuanced when it comes to public cloud systems. In future, OVH will break out storage and IP address rental costs separately, and will allow customers to mix and match storage capacity and compute.

“In appearance, hourly compute cost won’t change,” Klaba wrote. “On the other hand, low-latency Block Storage and IPv4 addresses, previously included in our Gen3 instances (B3, C3, R3) will appear as two separately billed line items on Oct. 1.”

The result is price increases of as little as 1.4% for the most powerful instances, or as much as 21.9% for smaller instances, he said.

OVH will continue to offer a 15% discount for a commitment of one year, or 30% for three years, he said, but will no longer offer discounts for shorter terms.

This article originally appeared on NetworkWorld.