Alternative clouds have emerged amid major cloud providers such as AWS, Google Cloud, and Microsoft Azure, and often specialize in niche areas like AI-native computing, and sovereign and edge deployments, while professing to provide cost reductions and best-of-breed performance.
“Many organizations are rebalancing away from all-in hyperscaler strategies to avoid lock-in and concentration risk,” says Kyle Campos, CTPO at CloudBolt, a cloud management platform. “At the same time, alternative clouds are often purpose-built for specific advantages.” To him, benefits include lower-cost object storage at scale, better edge performance for latency-sensitive apps, and faster access to dedicated GPUs for AI workloads. Data sovereignty is also pushing adoption of localized providers.
But as alternative clouds, also called neoclouds, make headlines and amass considerable funding rounds, their use in enterprise environments remains relatively nascent. While enterprise executives see the potential in using alternative clouds, especially for GPU workloads, many are skeptical, taking a balanced approach to assessing maturity, costs, and whether it’s worth sacrificing continuity with existing clouds.
Smartsheet CIO and CISO Ravi Soin is one of these executives taking a measured approach. “We’ll grow our use of alternative clouds, but within a clearly defined perimeter,” he says. “That discipline is what separates considered adoption from sprawl.” For Soin, many neoclouds still fail early in the evaluation process, especially for regulated workloads where a mature compliance posture is non-negotiable.
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In order to fully evaluate alternative clouds, the platforms must be treated with the same vigilance as any other IT acquisition. “This remains consistent across any tech stack: fit, form, and function,” says Nigel Gibbons, director and senior advisor at the NCC Group, a cybersecurity consulting firm. “This means alignment of the cloud to workload performance, security, and sovereignty needs.”
Overall, CIOs aren’t rushing into alternative clouds. It’ll take a serious proof spanning regulatory impact, auditability, resilience, and total cost planning in order to convince large organizations to migrate away from well-established cloud infrastructure. More realistically, such clouds will be evaluated on a per-workload basis.
Compliance and transparency
When evaluating neoclouds, compliance is top of mind for enterprise executives. “Compliance posture such as SOC 2, ISO 27001, and sovereign-specific certifications like FedRAMP or IRAP is the first filter and, for most neoclouds, an early exit,” says Soin.
Smartsheet runs AI on enterprise customer data and therefore requires strict compliance and sovereignty frameworks. “That defines a hard boundary where regulated workloads stay with certified providers,” he adds. “Workload identity doesn’t federate natively across clouds, so every trust relationship must be hand-built and audited. For a platform where data sensitivity varies enormously, that governance gap isn’t theoretical.”
Gibbons adds that trust validation is becoming a pressing requirement from regulators and the overall market. “The uncomfortable truth is that most organizations have operated on deferred risk captured in registers, softened in board narratives, and tolerated due to limited visibility,” he says. The pattern of tumultuous changes in the AI market, he adds, is encouraging more of a resilience mindset among technical executives assessing new technologies.
Operational transparency is another pressing area to evaluate. For Soin, the infrastructure that supports AI workloads requires granular auditability. “Can the provider give you end-to-end observability across the inference layer: tamper-evident logs, model versioning, the ability to trace what happened to customer data from input to output?” he asks.
Smartsheet’s enterprise customers also ask how AI is applied to their data and what controls exist, and that answer has to be defensible. “A neocloud that can’t support it isn’t just an operations risk, it’s a risk to the customer trust our business is built on,” he says.
Per-workload evaluation
Although neoclouds are often framed as a replacement to hyperscalers, they’re more often complementary infrastructure, purpose-built for individual use cases. “A neocloud isn’t a type of cloud, but more a design principle,” says Gibbons. “We can think of it as infrastructure optimized for a specific workload, trust model or control requirement, rather than general-purpose scale.”
Their usage hinges on the project at hand, which typically involves AI/ML projects. “We work with a mix of alternative providers depending on workload fit,” says CloudBolt’s Campos. “We’ve used alternative cloud providers for VMware as part of a cloud migration strategy.” Their customers use alternative clouds for object storage, edge and low-latency needs, AI/ML GPU capacity, and legacy virtualization workloads that don’t map cleanly to hyperscaler-native services.
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Executives must determine which environment makes sense for the workload at hand, and act accordingly. “CIOs should begin by clarifying the specific workload opportunity an alternative cloud is meant to accelerate, then evaluate providers against developer and operator experience inclusive of ability to scale with business need,” adds Campos. “From there, the priorities are whether the platform can be governed and operated alongside existing clouds, with strong visibility into usage and unit economics. Security, compliance posture, and credible SLAs for reliability and performance should be evaluated at the same bar as hyperscalers. The goal is to add flexibility without creating a new operational silo.”
Not all workloads are a fit for neoclouds or alternative providers, however. While these platforms benefit numerous workload-specific options, they’re not viewed as wholesale replacements for hyperscalers. For general-purpose enterprise functions, hyperscalers may still retain advantages around integrated services, discounts, operational maturity, and the availability of relevant job skills.
For this reason, many technology leaders are still in an exploratory phase. “Our infrastructure today runs on hyperscalers, and that’s been the right choice for where we’ve been,” says Smit Shanker, global CIO of Xebia, a global IT consulting and software engineering company. Still, he encourages CIOs to begin actively evaluating alternatives. “We’re now in that evaluation phase, looking at where specialized providers, sovereign options, or hybrid approaches make more sense for specific workloads.”
Shared responsibility model
CIOs require a high-degree of enterprise-grade trust that must be codified beyond empty promises and proven under real-world stress tests. As such, before diving headfirst into a newfangled cloud, Smartsheet’s Soin advocates verifying its shared responsibility model, which stipulates commitments the cloud provider has to uphold, and where responsibility lies with consumers.
For Soin, it’s especially critical to understand how a cloud provider acts when incidents occur. “Request post-incident reviews before signing anything, not templated RCAs but the real communication timeline,” he says. “Shared responsibility models are well-documented until they’re tested. Find out what happened the last time they were tested.”
Ultimately, alternative clouds need to meet the same enterprise maturity bar as established cloud providers. “Assurances must include auditability, compliance, resilience, transparent SLAs, interoperability, portability, and independently validated trust, not vendor assertions,” says NCC Group’s Gibbons.
Total cost analysis
Understanding the economic impact is necessary to determine whether an alternative cloud is worth it for the business. This is an area where alternative clouds shouldshine, with reports estimating savings of up to 70% compared with hyperscaler GPU instances. Traditional cloud computing pricing models are often opaque, and charges related to add-ons, upsells, and hidden fees around egress and ingress are notoriously known to surprise cloud computing consumers.
For certain workload categories, the economics can favor alternative clouds. “Traditional cloud providers often impose substantial data egress fees that inflate the total cost of ownership for data-heavy architectures,” says Dave McCarthy, VP of cloud and infrastructure services at global market intelligence and advisory firm IDC. “Alternative providers frequently minimize or eliminate these bandwidth penalties entirely, offering a more transparent cost structure,” he says.
Still, a full cost audit is necessary before going all-in on an alternative cloud. “What I’d recommend to anyone is do the full-cost model before you commit,” says Shanker. He suggests conducting a comprehensive analysis of cloud compute, inference, API volumes, and maintenance costs for things like infrastructure upgrades projected against the actual demand curve. When viewed together, he says, they create compounding costs that most C-suites haven’t modeled.
“Most enterprises are still modeling cloud economics on implementation costs alone, and that’s incomplete,” he adds. “The organizations getting AI right are the ones treating infrastructure economics as a first-class design decision, not a footnote.”
As cloud infrastructure choices can produce vendor lock-in, CIOs should use all the criteria at their disposal to plan and inform a neutral decision-making process. “Whatever path you choose — hyperscaler, alternative, sovereign, or some combination — the discipline that matters is vendor-agnostic, framework-led design,” adds Shanker. “Don’t let infrastructure choices constrain what’s possible.”
Where the market is heading
Neoclouds have made a mark for themselves by offering direct access to latest-generation hardware, which is important for AI-focused workloads requiring high performance. There’s also the argument that opting for alternative clouds aids a distributed multi-cloud strategy, which can benefit resilience and meet certain regional sovereign cloud mandates.
Nearly 90% of IT leaders say an organization should never commit to one cloud provider for their entire architecture, according to a HostingAdvice survey from last year. The same study found 73% of leaders use two or more alternative cloud providers, underscoring a clear interest in alternative clouds.
Still, while some industry reports are favorable, enterprise executives express more of a cautious reality of selective evaluation, not wholesale migration. For Soin, a top concern is the compliance posture and observability capabilities for alternative clouds, especially when hosting workloads that run AI inference. “The question isn’t just where inference happens but whether we can see what happened, explain it, and stand behind it,” he says. “That obligation shapes which providers are even in the conversation.”
While state-of-the-art cheap GPU compute is alluring, CIOs should keep an eye on the full governance picture when evaluating alternative clouds. “The neoclouds that earn more of our workload won’t just be the ones with the best GPUs,” says Soin. “They’ll be the ones that have seriously invested in model governance infrastructure and can demonstrate it. That’s a higher bar than most currently clear, but it’s where the market needs to go.”
A focus on inference
IDC forecasts that by next year, the computational demands of AI will force 80% of organizations to modernize legacy cloud environments by switching to platforms designed for AI workloads, with half of this being processed locally on endpoints or edge nodes by 2030.
“Enterprises are increasingly looking beyond major hyperscalers to avoid vendor lock-in and optimize specialized workloads,” says IDC’s McCarthy. “Rather than relying on a single, centralized infrastructure, organizations are adopting these alternatives into a broader portfolio.”
Much of this new computing paradigm will center on inference, or the post-training execution of AI models in production, using live customer or enterprise data. As inference moves into more business-critical workflows, decisions about where those workloads run, and what criteria guide those decisions, will have long-term implications.
Alt and neo clouds will likely have their place in inference, as well as in future sovereign and decentralized cloud models, and it’s not a decision CIOs should take lightly or without an informed evaluation framework. “Where workloads run becomes one of the most consequential decisions a leader makes,” says Shanker.