For Gulf businesses handling sensitive, always-on workloads, the choice is not simply “cloud or on-premises.” Cloudera CEO Charles Sansbury argues that hybrid AI—running applications across public cloud, sovereign infrastructure and privately operated systems—is becoming a durable model. Its enabling capability, he says, is workload portability: being able to move and operate the same workloads across those environments without rebuilding the operating model each time.
What does workload portability mean?
Workload portability is the ability to deploy and run the same data and AI applications in different computing environments, such as a public cloud, a sovereign cloud, a private data center or an isolated air-gapped network. Cloudera says its aim is to give customers a consistent way to manage those workloads across locations.
That does not necessarily mean moving data or applications frequently. A business might keep a particular workload in a private environment for years, while retaining the option to deploy it elsewhere if its requirements change. Portability is the option to choose a suitable environment without having to start over with a different application and operating model.
Why might Gulf enterprises choose hybrid AI?
Finance, energy, government, healthcare and industrial operations can combine sensitive information with continuous processing, regulatory oversight and latency needs. For those organizations, where data and computing run can affect governance, service performance, resilience and cost.
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Sansbury says some large institutions have high-volume, predictable workloads that may be more economical to run on owned or private infrastructure than on a hyperscaler. He points to bank fraud detection as an example of a workload that may run continuously. By contrast, public cloud can suit workloads that need to scale quickly or are experimental. “We’re not trying to compete for every workload,” he said in the interview.
That is a workload-matching argument, not a claim that private infrastructure is always cheaper. The economics depend on the application’s demand pattern and the costs of operating the infrastructure. A steady, always-on workload may justify owned capacity; a variable workload may benefit from the ability to scale in the cloud.
Does hybrid AI mean keeping data out of public cloud?
No. Hybrid AI does not require an enterprise to keep all information on-premises, nor does it require every workload to move to public cloud. The design choice can vary by data type, application and operating requirement. Sensitive financial, government or healthcare data may need to remain under national or corporate control, while other work can run in a public-cloud environment.
Sansbury’s position is that “not everything goes to the cloud.” He describes customers seeking “the cloud experience but with on-premise economics and control.” In practice, that means seeking cloud-like deployment and management while retaining choices about where data and applications reside.
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How do the main operating models compare?
| Decision factor | Public cloud | Private or on-premises infrastructure | Hybrid approach |
|---|---|---|---|
| Data control and governance | Can be suitable where the organization’s data, compliance and governance requirements permit it. | Can give an organization more direct control over where infrastructure and data are operated. | Places workloads according to their data-control and governance needs. |
| Cost for steady-state workloads | May be less attractive for predictable, continuously running workloads, according to Sansbury’s argument. | May be economical for high-volume, always-on workloads, depending on operating costs and utilization. | Matches steady workloads and elastic or experimental work to different environments. |
| Latency and resilience | Suitability depends on the application’s latency needs and the cloud environment selected. | May be appropriate when an organization needs local processing or direct operational control. | Allows placement choices based on application requirements; it does not by itself guarantee resilience or low latency. |
| Portability | Can be one destination for a workload, but moving between providers may require compatibility and operational planning. | Can host workloads the organization chooses to retain privately. | Seeks a consistent way to operate workloads across public, private, sovereign and isolated environments. |
What role do Cloudera’s platform and Taikun play?
Cloudera describes its Anywhere Cloud as a modular platform for data and AI applications across public clouds, sovereign infrastructure, private data centers and air-gapped networks. Its product description highlights write-once portability, zero-copy querying of Apache Iceberg data, automated governance, personally identifiable information (PII) masking, and a marketplace that includes Cloudera services, partner services and open-source engines.
Cloudera’s Taikun acquisition adds Kubernetes and cloud-infrastructure management capabilities. The company says Taikun contributes a container-native Kubernetes platform and a unified control plane intended to make deployments and upgrades more consistent across cloud, on-premises, sovereign and air-gapped environments. Sansbury described the acquisition as a step toward bringing “the cloud experience wherever enterprise data resides.” These are Cloudera’s product and strategy claims; they do not establish that every application can move between environments without adaptation.
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What does Saudi Arabia’s AWS region announcement signal?
Cloudera announced plans to launch its platform on the AWS Saudi Arabia Region, framing the move around local data control, governance, compliance and Vision 2030. The announcement is a concrete example of how a cloud provider’s regional infrastructure could fit into a sovereignty-oriented deployment strategy. It is an announcement of plans, not evidence here that the launch has been completed.
Cloudera’s 2025 announcement cited an IDC forecast that sovereign-cloud infrastructure investment would grow by an average of 27% a year to reach $258 billion by 2027. That is a forecast for the broader infrastructure market, not a measure of Cloudera revenue or Middle East customer adoption. Separately, Computer Weekly reported Cloudera annual revenue of $1.1 billion in 2025; that is company-level context, not a regional sales figure.
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What should decision-makers test before choosing a deployment?
- Data obligations: Identify which data and workloads must remain under national, sectoral or corporate control, and what rules apply to each.
- Demand shape: Separate continuously used workloads from seasonal, bursty or experimental ones before comparing infrastructure costs.
- Performance and continuity: Set application-specific latency and availability requirements, then confirm the proposed environment can meet them.
- Portability in practice: Ask which components move with the workload, what needs reconfiguration, and whether governance and security controls remain consistent.
- Operations: Establish who manages Kubernetes, upgrades, infrastructure and incident response in each environment; a unified control plane does not remove those responsibilities.
Cloudera’s 2026 launch release said 73% of IT leaders reported infrastructure-performance constraints that hindered operational initiatives. The release does not provide enough detail here about the survey sample or methodology to treat that figure as an independently verified measure of Gulf enterprises’ experience.
How strong is the case for hybrid AI in the Middle East?
The case is strongest where an organization has a genuine mix of constraints: sensitive data that needs careful control, workloads that must run continuously or close to operations, and other applications that benefit from elastic cloud capacity. In that setting, portability can reduce dependence on a single deployment location and make workload placement a deliberate choice.
It is not proof that hybrid is automatically cheaper, simpler or more secure. Sansbury’s interview is management commentary, and the figures above do not establish independently audited regional adoption attributable to workload portability. Enterprises still need to evaluate their own data obligations, total operating costs, performance needs and the effort required to keep applications portable.
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