This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers.
My company recently commissioned a survey of enterprise leaders to better understand what they need from their IT investments. The Rise Of Alternative Cloud Providers, a 2026 commissioned study conducted by Forrester Consulting on our behalf, found that nearly half of all respondents showed levels of satisfaction ranging between "not at all satisfied" and "somewhat satisfied" with their current cloud providers, for both conventional and AI/ML workloads. Legacy single-cloud models cannot sufficiently support the demands of everyday digital operations, let alone the latest advancements in AI.
For today's enterprises, modernized infrastructure is an existential priority. With the right foundation, businesses can open new revenue streams through AI tools and services, while simultaneously optimizing their existing processes to be more efficient. But with wrong-sized infrastructure, they're losing money, losing opportunity and losing ground to the competition.
What's Causing Dissatisfaction
As organizations expand their cloud services usage to accommodate heavier AI workloads, they're uncovering infrastructural weaknesses that become increasingly harder to manage at scale.
The aforementioned survey identified security and risk as the top challenge enterprises face with their current cloud providers. This high ranking is partly symptomatic of an increasingly unpredictable threat environment: the same AI innovations that have helped businesses to transform their operations are also accelerating cyberattacks.
But leaders are also skeptical of their provider's native defenses. Legacy IT infrastructure was built for a different time; enterprise leaders are understandably worried that it could be too inflexible to keep up with the pace of evolving threats.
The digital sovereignty push has also ramped up this year; data residency and encryption requirements are becoming increasingly strict, pushing businesses to seek out local cloud alternatives to remain compliant.
Behind security, the next big infrastructure challenge enterprises cited was, unsurprisingly, high costs. Between the memory shortage and stalled data center buildouts, an AI services crisis is looming. Hyperscalers are racing to expand their AI compute availability, but customers are already (unfairly) footing the bill.
As if that weren't enough, hyperscalers are already notorious for spiking costs with feature bloat and forced overprovisioning. Hyperscalers are designed for the biggest companies and governments, but not every enterprise requires that heft of hardware. Organizations end up paying for extra features simply because they come with the contract. Think of it like adding items to your shopping cart to get a discount on shipping — at the end of the day, you're still paying a lot more.
Unpredictable workloads also contribute to high computing costs. When businesses race to bring a new AI model into their processes, or have teams working with different AI tools in silos, cloud costs can spiral upward before enterprises are able to get a handle on them. Strategies like protocol tagging help, but they'll only go so far if the actual infrastructure is virtually designed to encourage overuse.
Other factors signaled organizational and design limitations. 52% of respondents cited a shortage of AI talent. 48% noted a lack of visibility into cloud infrastructure, and 46% said they were unable to scale in line with demand.
All of these factors prevent organizations from reaching the transformational stage of AI maturity — thereby preventing them from solving the business inefficiencies that are holding them back from growth.
It's Not Just the Cloud
To support next-generation AI workloads, many enterprises are considering a full-scale infrastructure rebuild that also includes edge and on-prem compute. A hybrid, multi-cloud approach would allow them to control costs through more strategic workload management across their entire IT infrastructure.
In practice, this looks like:
- Running smaller AI models on-device to reduce latency and maximize the impact of highly specialized use cases.
- Distributing inference across multiple GPU and CPU frameworks for improved tokenomics — a must as token-based pricing continues to rattle IT budgets.
- Auditing for unused features to inform budget reallocation, hyperscaler contract amendments, and alternative cloud adoption.
- Promoting more efficient prompting throughout the organization to reduce costly redundant inference.
The Solutions: Alternative Clouds, Open Source Support, and Hyperscaler Refreshes
Flexibility, scalability and security are the formula for resilient infrastructure. Most hyperscalers, with their deeply integrated stacks and sprawling feature sets, cannot offer all three. Even so, as our survey found, 54% of enterprises still plan to expand their footprint with their current hyperscalers to solve their infrastructure challenges. Uncertainty and skepticism hold them back from pursuing alternatives, but so do more practical considerations, like data migration burdens and IT team training.
The ideal solution is not to simply pick an alternative cloud and hope for the best, or to sign another hyperscaler contract with gritted teeth. Wary organizations don't have to abandon their hyperscaler outright to take advantage of alternative cloud and on-device options. Multi-cloud strategies enable workload-specific deployments; compute power becomes proportional to compute intensity, bringing down costs without sacrificing performance.
Also, infrastructure is more than just hardware. Software-supported AI compute adds robust capabilities and further promotes hardware optimization. Infrastructure with open-source compatibility allows enterprises to truly customize their stacks, whether they need additional storage, networking support, or deployment assistance. With open-source software, enterprises can get pre-built, pre-tested foundations for a fraction of the cost and the backing of highly skilled developers.
Ultimately, how enterprises solve their cloud dissatisfaction comes down to solving their biggest pain points while advancing their growth goals. Plenty will stay with their current providers, but there are too many options for unhappy customers to leave migration off the table.