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Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend.

A new survey of senior IT decision-makers, commissioned by Cloudian and conducted by Centiment, finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs, driven by tightening sovereignty requirements, cloud economics that have turned punishing at scale, and AI workloads that are exposing the limits of what cloud can cost-effectively deliver.

Notably, 76% of survey respondents already have more than half their workloads in public cloud today — making the repatriation signals all the more telling.

"This isn't a story about enterprises souring on cloud," said Jon Toor, CMO of Cloudian. "It's about organizations getting smarter about workload placement. The data makes clear that for sovereignty-sensitive, AI-intensive, and large-scale storage workloads, on-premises is often the better answer."

Sovereignty Has Become Non-Negotiable

Data sovereignty produced the survey's most striking consensus. 99% of respondents said it is at least a moderate factor in infrastructure decisions, with 82% calling it a primary or significant driver. More than half (59%) cited concerns about cloud providers accessing their data for analytics or model training, and 53% are operating under customer or partner contracts that restrict where data can reside.

Regulatory pressure is compounding the issue: 45% have experienced new cross-border data restrictions in the past two years — a reflection of accelerating data localization laws across the EU, Asia-Pacific, and elsewhere.

"The compliance landscape has gotten materially more complex, and it's moved faster than most cloud providers' infrastructure footprints," said Toor. "When your data residency requirements don't map cleanly to available cloud regions, on-premises stops being a legacy option and starts being the only viable one."
Cloud Economics Aren't What They Used to Be

The cost picture is stark — and nearly universal. 84% of respondents are running over their cloud storage budgets, with nearly one in five exceeding them by more than 30%. Just 0.5% report being under budget. The leading culprits: data egress fees (46%), costs that escalate with data volume (45%), and premium pricing for residency-compliant regions (43%).

"The math that made cloud compelling for early-stage or variable workloads doesn't hold when you're storing petabytes and accessing them regularly," Toor noted.

AI Is Accelerating the Timeline

If sovereignty and cost are the structural drivers, AI is what's making cloud data repatriation urgent. 85% of respondents said AI requirements are influencing their shift toward on-premises infrastructure. More than half (55%) said cloud cannot consistently meet AI inference latency requirements, and 52% need to keep AI training data on-premises for security or compliance reasons.

When asked to name their top infrastructure priorities for the coming year, AI and ML infrastructure ranked first (57%) — narrowly ahead of data sovereignty and security (56%) and cost predictability (54%).

"AI is the forcing function," Toor said. "Organizations that might have tolerated suboptimal cloud economics or sovereignty workarounds for conventional workloads are finding they can't accept those same tradeoffs for AI. The latency requirements are different, the data sensitivity is higher, and the cost at scale is harder to absorb."

A Rebalancing, Not a Reversal

The survey makes clear that enterprises are not walking away from cloud. Roughly 30% are still expanding their cloud presence. The more accurate picture is simultaneous expansion and cloud data repatriation — organizations optimizing workload placement rather than choosing sides.

"Hybrid isn't a compromise anymore, it's a deliberate strategy," Toor said. "The enterprises doing this well have gotten very precise about what goes where and why. Cloud for elastic, unpredictable demand. On-premises for everything where you need predictable cost, low latency, and clear data jurisdiction."

Survey Methodology: The survey was conducted by Centiment on behalf of Cloudian and fielded in February 2026. Respondents included 212 senior IT decision-makers at organizations with significant public cloud adoption. 96% identified as primary decision-makers or key influencers on infrastructure. An embedded attention-check question was included; all 212 responses passed.

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Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend.

A new survey of senior IT decision-makers, commissioned by Cloudian and conducted by Centiment, finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs, driven by tightening sovereignty requirements, cloud economics that have turned punishing at scale, and AI workloads that are exposing the limits of what cloud can cost-effectively deliver.

Notably, 76% of survey respondents already have more than half their workloads in public cloud today — making the repatriation signals all the more telling.

"This isn't a story about enterprises souring on cloud," said Jon Toor, CMO of Cloudian. "It's about organizations getting smarter about workload placement. The data makes clear that for sovereignty-sensitive, AI-intensive, and large-scale storage workloads, on-premises is often the better answer."

Sovereignty Has Become Non-Negotiable

Data sovereignty produced the survey's most striking consensus. 99% of respondents said it is at least a moderate factor in infrastructure decisions, with 82% calling it a primary or significant driver. More than half (59%) cited concerns about cloud providers accessing their data for analytics or model training, and 53% are operating under customer or partner contracts that restrict where data can reside.

Regulatory pressure is compounding the issue: 45% have experienced new cross-border data restrictions in the past two years — a reflection of accelerating data localization laws across the EU, Asia-Pacific, and elsewhere.

"The compliance landscape has gotten materially more complex, and it's moved faster than most cloud providers' infrastructure footprints," said Toor. "When your data residency requirements don't map cleanly to available cloud regions, on-premises stops being a legacy option and starts being the only viable one."
Cloud Economics Aren't What They Used to Be

The cost picture is stark — and nearly universal. 84% of respondents are running over their cloud storage budgets, with nearly one in five exceeding them by more than 30%. Just 0.5% report being under budget. The leading culprits: data egress fees (46%), costs that escalate with data volume (45%), and premium pricing for residency-compliant regions (43%).

"The math that made cloud compelling for early-stage or variable workloads doesn't hold when you're storing petabytes and accessing them regularly," Toor noted.

AI Is Accelerating the Timeline

If sovereignty and cost are the structural drivers, AI is what's making cloud data repatriation urgent. 85% of respondents said AI requirements are influencing their shift toward on-premises infrastructure. More than half (55%) said cloud cannot consistently meet AI inference latency requirements, and 52% need to keep AI training data on-premises for security or compliance reasons.

When asked to name their top infrastructure priorities for the coming year, AI and ML infrastructure ranked first (57%) — narrowly ahead of data sovereignty and security (56%) and cost predictability (54%).

"AI is the forcing function," Toor said. "Organizations that might have tolerated suboptimal cloud economics or sovereignty workarounds for conventional workloads are finding they can't accept those same tradeoffs for AI. The latency requirements are different, the data sensitivity is higher, and the cost at scale is harder to absorb."

A Rebalancing, Not a Reversal

The survey makes clear that enterprises are not walking away from cloud. Roughly 30% are still expanding their cloud presence. The more accurate picture is simultaneous expansion and cloud data repatriation — organizations optimizing workload placement rather than choosing sides.

"Hybrid isn't a compromise anymore, it's a deliberate strategy," Toor said. "The enterprises doing this well have gotten very precise about what goes where and why. Cloud for elastic, unpredictable demand. On-premises for everything where you need predictable cost, low latency, and clear data jurisdiction."

Survey Methodology: The survey was conducted by Centiment on behalf of Cloudian and fielded in February 2026. Respondents included 212 senior IT decision-makers at organizations with significant public cloud adoption. 96% identified as primary decision-makers or key influencers on infrastructure. An embedded attention-check question was included; all 212 responses passed.

Hot Topics

The Latest

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...