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.

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For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT Dominates Enterprise Architecture, with Colocation Anchoring Critical Workloads

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AI is Moving Fast, and Enterprise File Data Is Not Ready

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

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Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

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I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

AI is Breaking Enterprise Log Management

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

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Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...