Global Compute Refresh in 2026 – A Multi-Billion Opportunity for Channel Partners

For the last decade, the dominant narrative in enterprise IT was simple: move to the cloud. It promised agility, faster deployment, a friendlier OpEx model, and the ability to escape the constraints of legacy data centers. But by 2025, a growing number of organizations quietly started running into a hard reality cloud wasn’t always cheaper, and it definitely wasn’t always predictable. At the same time, generative AI exploded into the enterprise stack, bringing with it an unprecedented hunger for compute. These two forces together are now setting up what looks like a global compute refresh boom in 2026, and channel partners—GSIs, MSPs, and VARs are right in the blast zone of opportunity.

Many enterprises have discovered that cloud bills are not just large, they are volatile. A significant percentage of organizations exceeded their cloud budgets in the last couple of years, driven by underutilized resources, premium managed services, complex pricing, and expensive data egress. Several high-profile cases have emerged where companies moved substantial workloads out of the public cloud and onto their own infrastructure, cutting millions from annual spend. One firm publicly shared that by shifting core workloads off public cloud and back on-prem, it reduced yearly infrastructure costs from the low millions to nearly a third of that number in under a year. Another large digital business reported savings of tens of millions of dollars over a two-year period by building its own data centers for storage and compute instead of relying exclusively on hyperscale providers. These are not isolated anecdotes—they reflect a broader rethinking of which workloads genuinely benefit from cloud, and which are simply renting compute forever at a premium.

The economics are fairly straightforward. For bursty, elastic, short-lived workloads, cloud remains compelling. But for steady-state workloads that run 24/7, especially those that generate or process large volumes of data, the meter never stops. At a certain scale, the total cost of operating in public cloud begins to exceed the cost of owning and operating equivalent on-premises infrastructure amortized over several years. Analysts often describe a tipping point: once cloud spend for a given workload crosses a certain ratio relative to a comparable owned environment, it makes more financial sense to invest capex in infrastructure. Generative AI has pushed many enterprises closer to that tipping point faster than expected. AI is not a normal workload. Training and serving large language models, running personalized recommendation engines, real-time fraud detection, autonomous systems, and high-resolution analytics all demand dense clusters of GPUs or AI accelerators, high-bandwidth networking, and low-latency access to large datasets. Running this entirely in the public cloud is convenient at the prototype stage, but once organizations move into continuous training, large-scale fine-tuning, and real-time inference across millions of users or records, the monthly AI bill can become eye-watering. There are already organizations that have spoken openly about AI-related cloud bills in the tens of millions per year, and they are now exploring private AI infrastructure as a way to regain control.

That is why the center of gravity for many AI initiatives is shifting toward hybrid and on-prem environments. A large and growing share of AI-related infrastructure spending is now going into on-premises systems rather than cloud-only solutions. Recent market data on high-performance computing and AI infrastructure shows that on-prem “HPC-AI” systems now account for several times the spend seen in cloud-based HPC and AI services. Organizations are building their own AI clusters with GPU servers, specialized accelerators, fast storage, and high-speed interconnects, often mimicking hyperscaler-style architectures inside their own facilities. The logic is simple: if you know you will be running AI workloads intensely for years, it can be cheaper and more controllable to own the compute rather than rent it indefinitely. This shift is not purely about cost. Data gravity, regulation, security, and latency all play a role. Certain industries such as financial services, healthcare, and public sector operate under strict rules around data residency, sovereignty, and privacy. Keeping data on-premises and bringing compute to that data, instead of moving sensitive information to the cloud, simplifies compliance and reduces risk. Latency-sensitive applications like factory automation, connected vehicles, or real-time video analytics benefit from compute being closer to where data is generated, often at the edge or in local data centers. There’s also a resilience and control argument: organizations burned by major cloud outages and policy changes have begun to ask if it is wise to outsource too much of their core infrastructure.

All of this is driving a new appetite for modern, AI-ready data centers. That appetite is very visible in how major OEMs have repositioned their portfolios. Large infrastructure vendors now talk openly about AI driving the largest infrastructure refresh cycle in decades. They are rolling out reference architectures for “AI factories,” turnkey stacks that include GPU-accelerated servers, high-performance storage, fast networking fabrics, and software for managing AI workloads on-premises. Some vendors highlight internal analyses showing that running certain AI workloads on their on-prem solutions can be significantly more cost-effective over time than running the same workloads on general-purpose public cloud services. Others have launched private AI cloud offerings that deliver a cloud-like experience on dedicated gear in customer data centers.

Networking vendors are seeing similar tailwinds. AI workloads are incredibly demanding on east–west traffic within the data center, and many existing network fabrics simply are not designed for the kind of bandwidth and latency AI clusters need. As a result, there is strong demand for next-generation data center switches, advanced fabrics, AI-optimized Ethernet, and integrated security at line rate. Some vendors, traditionally known for networking, now report multi-billion-dollar AI data center order backlogs, signaling just how fast enterprises are refreshing their infrastructure for an AI-centric future. Beyond servers and switches, power and cooling have suddenly become board-level topics. Dense AI and GPU racks draw enormous power and generate significant heat. Many data centers are at or near their power and cooling limits. This is pushing organizations to consider direct liquid cooling, immersion cooling, and more efficient designs to support AI at scale without doubling energy bills. Energy is effectively becoming a strategic resource in AI-era infrastructure planning, and any expansion of compute now comes with a sustainability and efficiency lens.

The sectors driving the earliest and largest waves of this refresh are those with both heavy data use and strong AI ambitions. Financial services firms are modernizing infrastructure to support advanced risk modeling, fraud detection using AI, and hyper-personalized customer analytics, while staying compliant with regulatory demands around data handling. Healthcare and life sciences organizations are investing in infrastructure for AI-assisted diagnostics, medical imaging, genomics, and drug discovery, where data sensitivity and performance both argue for strong on-prem or sovereign environments. Manufacturers and automotive companies are upgrading their environments for smart factories, digital twins, generative design, and autonomous systems, all of which depend on reliable, low-latency compute close to production lines and development environments. Public sector and defense agencies are upgrading as well, driven by national security, sovereign AI strategies, and the need for secure, often air-gapped compute environments.

Analysts consistently describe this moment as a convergence: unprecedented AI demand, accumulated technical debt in legacy infrastructure, and mounting cloud cost pressure are colliding to trigger a broad modernization wave. Global IT spending projections for the next year show some of the fastest growth rates in decades, with data center systems and AI-related infrastructure among the fastest-growing segments. Spending on AI infrastructure is expected to grow aggressively year-on-year for the rest of the decade, and a growing share of that spend is earmarked for hybrid and on-prem projects rather than pure public cloud. There is also a recurring theme in surveys of CIOs and IT leaders: most organizations see the potential of generative AI, but a large majority believe their current infrastructure is not ready and that a foundational overhaul is needed if AI is to deliver meaningful impact. For channel partners, this is not a “nice to have” trend—it is a window. GSIs, MSPs, and VARs sit in exactly the right place in the ecosystem to translate this refresh into revenue and long-term strategic relevance. But doing so requires adaptation.

First, there is enormous opportunity around cloud cost optimization and workload placement strategy. Many enterprises now want partners who can analyze their current cloud spend, model total cost of ownership across cloud and on-prem options, and recommend which workloads should stay in the cloud, which should move to a private cloud or colocation environment, and which should run in customer-owned facilities. That advisory motion—combined with execution of migrations and modernization—can become a significant consulting and integration business on its own. It also sets up the partner as a long-term trusted advisor. Second, channel partners need to build deep competence in AI infrastructure. It is no longer sufficient to know generic server and storage sizing; partners are expected to understand GPU and accelerator options, AI networking designs, storage architectures for large datasets, and the software ecosystem around MLOps, observability, and security for AI workflows. Many vendors are offering training, labs, and certifications around AI infrastructure specifically because customers are demanding credible expertise. Partners that invest in upskilling now can position themselves as the default choice for on-prem and hybrid AI builds. Those that don’t risk being sidelined by more specialized firms.

Third, partners should embrace verticalization. The refresh is not abstract; it is anchored in concrete business problems. A healthcare provider has very different constraints and goals than an automotive manufacturer or a national ministry. Channel partners that package solutions and services around industry-specific outcomes—faster claims decisions, reduced fraud losses, improved factory uptime, faster clinical trial analytics—will stand out from those merely quoting hardware. This often means assembling repeatable blueprints: a reference design, a bill of materials, pre-integrated software, and a services playbook tailored to a given vertical.

Fourth, there is a massive opening in managed and “cloud-adjacent” services for on-prem infrastructure. Many enterprises do not want to go back to the old model of managing everything themselves. They still want the operational simplicity of the cloud. This is where MSPs and VARs can offer managed private cloud services, managed AI clusters, hybrid management platforms, and edge infrastructure management. Operating and optimizing this new wave of infrastructure over its lifecycle creates recurring revenue and strengthens the partner’s position at the center of the customer’s IT strategy.

Finally, addressing power, cooling, and sustainability is becoming part of the partner value proposition. As organizations deploy more compute for AI, they cannot simply double energy consumption without facing internal and external scrutiny. Channel partners that can advise on, design, and deliver more efficient data center solutions whether through modern hardware with better performance per watt, liquid cooling, or smarter placement of workloads will have an advantage in large deals. Some customers will explicitly prefer partners who can demonstrate how their proposed architecture supports both AI growth and ESG goals.

Put simply, 2026 is shaping up not just as another refresh cycle, but as a structural reset of how enterprises think about compute. The story is no longer “move everything to the cloud and be done.” The new storyline is “place each workload where it delivers the best combination of cost, performance, control, and compliance.” That naturally leads to hybrid architectures and a renewed role for on-prem and edge infrastructure. Channel partners who understand this shift, build the right skills, align with the right OEM programs, and design services around AI and hybrid infrastructure will find themselves in the middle of a multi-year growth wave. Those who treat AI as just another buzzword and ignore the infra implications risk watching the opportunity pass them by.  Channel partners should be preparing now for a compute refresh boom powered by AI, disciplined by economics, and executed through the hybrid enterprise.