What is private cloud hosting and why is it important? Private cloud hosting provides cloud‑like computing resources within a dedicated, enterprise‑controlled environment. It combines the elasticity and convenience of public cloud with heightened security, compliance and data sovereignty—making it ideal for regulated industries, latency‑sensitive applications and AI workloads.
In a public cloud, customers rent compute, storage and networking from providers like Amazon Web Services or Microsoft Azure. Resources are shared across customers, and data resides in provider‑owned facilities. A private cloud, however, runs on infrastructure dedicated to a single organisation. It may be located on‑premises or hosted in a service provider’s data centre. Hybrid clouds blend both models, allowing workloads to move between environments.
Private clouds appeal to industries with stringent compliance requirements—finance, healthcare and government. Regulations often require data residency in specific jurisdictions. Research shows that the rise of sovereign clouds is driven by privacy concerns and regulatory mandates. By hosting data on dedicated infrastructure, organisations maintain control over location, encryption and access policies. Hybrid models further allow them to burst into public cloud for peak loads without sacrificing sovereignty.
Analysts predict that private and sovereign clouds will continue to grow as organisations seek control over their data. Multi‑cloud adoption helps companies avoid vendor lock‑in and optimise costs. Meanwhile, the surge in edge computing and micro‑clouds means workloads are moving closer to where data is generated. These trends make private cloud hosting more relevant than ever.
Which public cloud extensions transform into private cloud solutions? AWS Outposts, Azure Stack/Local, Google Anthos & Distributed Cloud, and Oracle Cloud@Customer deliver public cloud services as fully managed hardware installed in customer facilities. They combine the familiarity of public cloud APIs with on‑premises control—ideal for regulated industries and low‑latency applications.
AWS Outposts is a fully managed service that brings AWS infrastructure, services and APIs to customer data centres and co‑location facilities. Outposts racks include compute, storage and networking hardware; AWS installs and manages them remotely. Customers subscribe to three‑year terms with flexible payment options. The same AWS console and SDKs are used to manage services like EC2, EBS, EKS, RDS and EMR. Use cases include low‑latency manufacturing control, healthcare imaging, financial trading and regulated workloads.
Clarifai Integration: Deploy Clarifai models directly on Outposts racks to perform real‑time inference near data sources. Use the Clarifai local runner to orchestrate GPU‑accelerated workloads inside the Outpost, ensuring data does not leave the site. When training requires scale, the same models can run in AWS regions via Clarifai’s cloud service.
Azure Stack Hub (rebranded as Azure Local) extends Azure services into on‑prem environments. Organisations run Azure VMs, containers and services using the same tools, APIs and billing as the public cloud. Benefits include low latency, consistent developer experience, and compliance with data residency. Disadvantages include a limited subset of services and the need for expertise in both on‑prem and cloud environments. Azure Local is ideal for edge analytics, healthcare, retail and scenarios requiring offline capability.
Clarifai Integration: Use Clarifai’s model inference engine to serve AI models on Azure Local clusters. Because Azure Local uses the same Kubernetes operator patterns, Clarifai’s containerised models can be deployed via Helm charts or operators. When connectivity to Azure public cloud is available, models can synchronise for training or updates.
Google’s Anthos provides a unified platform for building and managing applications across on‑premises, Google Cloud and other public clouds. It includes Google Kubernetes Engine (GKE) on‑prem, Istio service mesh, and Anthos Config Management for policy consistency. Google Distributed Cloud (GDC) extends services to edge sites: GDC Edge offers low‑latency infrastructure for AR/VR, 5G and industrial IoT, while GDC Hosted serves regulated industries with local deployments. Strengths include strong AI and analytics integration (BigQuery, Dataflow, Vertex AI), open‑source leadership and multi‑cloud freedom. Challenges include integration complexity for organisations tied to other ecosystems.
Clarifai Integration: Deploy Clarifai models into Anthos clusters via Kubernetes or serverless functions. Use Clarifai’s compute orchestration to schedule inference tasks across Anthos clusters and GDC Edge; pair with Clarifai’s model versioning for consistent AI behaviour across regions. For data pipelines, integrate Clarifai outputs into BigQuery or Dataflow for analytics.
Oracle’s private cloud solution, Cloud@Customer, brings the OCI (Oracle Cloud Infrastructure) stack—compute, storage, networking, databases and AI services—into customer data centres. OCI offers flexible compute options (VMs, bare metal, GPUs), comprehensive storage, high‑performance networking, autonomous databases and AI/analytics integrations. Uniform global pricing and universal credits simplify cost management. Limitations include a smaller ecosystem, learning curve and potential vendor lock‑in. Cloud@Customer suits industries deeply tied to Oracle enterprise software—finance, healthcare and government.
Clarifai Integration: Host Clarifai’s inference engine on OCI bare‑metal GPU instances within Cloud@Customer to run models on sensitive data. Use Clarifai’s local runners for offline or air‑gapped environments. When needed, connect to Oracle’s AI services for additional analytics or training.
When selecting a public cloud extension, evaluate service breadth, integration, pricing models, ecosystem fit, and operational complexity. AWS Outposts offers the broadest service portfolio but requires a multi‑year commitment. Azure Local suits organisations already invested in Microsoft tooling. Anthos emphasises open source and multi‑cloud freedom but may require more expertise. OCI appeals to Oracle‑centric enterprises with consistent pricing.
Which enterprise solutions offer comprehensive private cloud platforms? HPE GreenLake, VMware Cloud Foundation, Nutanix Cloud Platform, IBM Cloud Private & Satellite, Dell APEX and Cisco Intersight provide turn‑key infrastructures combining compute, storage, networking and management. They emphasise security, automation and flexible consumption.
HPE GreenLake delivers a consumption‑based private cloud where customers pay for resources as they use them. HPE installs pre‑configured hardware—compute, storage, networking—and manages capacity planning. GreenLake Central provides a unified dashboard for monitoring usage, security, cost and compliance, enabling rapid scale‑up. GreenLake supports VMs and containers, integrated with HPE’s Ezmeral for Kubernetes and with partnerships for storage and networking. Recent expansions include HPE Morpheus VM Essentials, which reduces VMware licensing costs by supporting multiple hypervisors; zero‑trust security with micro‑segmentation via Juniper; stretched clusters for failover; and Private Cloud AI bundles with NVIDIA RTX GPUs and FIPS‑hardened AI software.
Clarifai Integration: Run Clarifai inference workloads on GreenLake’s GPU‑enabled nodes using the Clarifai local runner. The consumption model aligns with variable AI workloads: pay only for the GPU hours consumed. Integrate Clarifai’s compute orchestrator with GreenLake Central to monitor model performance and resource utilisation.
VMware Cloud Foundation (VCF) unifies compute (vSphere), storage (vSAN), networking (NSX) and security in a single software‑defined data‑centre stack. It automates lifecycle management via SDDC Manager, enabling seamless upgrades and patching. The platform includes Tanzu Kubernetes Grid for container workloads, offering a consistent platform across private and public VMware clouds. An IDC study reports that VCF delivers 564 % return on investment, 42 % cost savings, 98 % reduction in downtime and 61 % faster application deployment. Built‑in security features include zero‑trust access, micro‑segmentation, encryption and IDS/IPS. VCF also supports private AI add‑ons and integrates with partner solutions for ransomware protection.
Clarifai Integration: Deploy Clarifai’s AI models on VCF clusters with GPU‑backed VMs. Use Clarifai’s compute orchestrator to allocate GPU resources across vSphere clusters, automatically scaling inference tasks. When training models, integrate with Tanzu services for Kubernetes‑native MLOps pipelines.
Nutanix offers a hyperconverged platform combining compute, storage and virtualisation. Recent releases focus on sovereign cloud deployment with Nutanix Cloud Infrastructure 7.5, enabling orchestrated lifecycle management for multiple dark‑site environments and on‑premises control planes. Security updates include SOC 2 and ISO certifications, FIPS 140‑3 validated images, micro‑segmentation and load balancing. Nutanix Enterprise AI supports government‑ready NVIDIA AI Enterprise software with STIG‑hardened microservices. Resilience enhancements include tiered disaster recovery strategies and support for 10 000 VMs per cluster. Nutanix emphasises data sovereignty, hybrid multicloud integration and simplified management.
Clarifai Integration: Use Clarifai’s local runner to deploy AI inference on Nutanix clusters. The platform’s GPU support and micro‑segmentation align with high‑security AI workloads. Nutanix’s replication features enable cross‑site model redundancy.
IBM Cloud Private (ICP) combines Kubernetes, a private Docker image repository, management console and monitoring frameworks. The community edition is free (limited to one master node); commercial editions bundle over 40 services, including developer versions of IBM software, enabling containerisation of legacy applications. IBM Cloud Satellite extends IBM Cloud services to any environment using a control plane in the public cloud and satellite locations in customers’ data centres. Satellite leverages Istio‑based service mesh and Razee for continuous delivery, enabling open‑source portability. This architecture is ideal for regulated industries requiring data residency and encryption.
Clarifai Integration: Deploy Clarifai models as containers within ICP clusters or on Satellite sites. Use Clarifai’s workflow to integrate with IBM Watson NLP or generate multimodal AI solutions. Because Satellite uses OpenShift, Clarifai’s Kubernetes operators can manage model lifecycle across on‑prem and cloud environments.
Dell’s APEX Private Cloud provides a consumption‑based infrastructure-as-a-service built on VMware vSphere Enterprise Plus and vSAN. It targets remote and branch offices and offers centralised management through the APEX console. Custom solutions allow mixing Dell’s storage, server and HCI offerings under a flexible procurement model called Flex on Demand. Cisco Intersight delivers cloud‑managed infrastructure for Cisco UCS servers and hyperconverged systems, providing a single management plane, Kubernetes services and workload optimisation.
Clarifai Integration: For Dell APEX, deploy Clarifai models on VxRail hardware, taking advantage of GPU options. Use Intersight’s Kubernetes Service to host Clarifai containers and integrate with Clarifai’s APIs for inference orchestration.
Enterprise solutions differ in billing models, ecosystem fit and AI readiness. HPE GreenLake emphasises consumption and zero‑trust; VMware provides a familiar VMware stack and strong ROI; Nutanix excels in sovereign deployments and resilience; IBM packages open‑source Kubernetes with enterprise tools; Dell and Cisco target edge and remote sites. Consider factors like hypervisor compatibility, GPU support, management complexity and licensing changes.
What open‑source frameworks power private clouds? Apache CloudStack, OpenStack, OpenNebula, Eucalyptus, Red Hat OpenShift and managed services like Platform9 provide flexible foundations for building private clouds. They offer vendor independence, customization and a community‑driven ecosystem.
Apache CloudStack is an open‑source IaaS platform that supports multiple hypervisors and provides integrated usage metering. It offers features like dashboard‑based orchestration, network provisioning and resource allocation. CloudStack appeals to organisations seeking an easy‑to‑deploy private cloud with minimal licensing costs. With built‑in support for VMware, KVM and Xen, it enables multi‑hypervisor environments.
OpenStack is a popular open‑source cloud operating system providing compute, storage and networking services. Benefits include cost control, vendor independence, complete infrastructure control, unlimited scalability and self‑service APIs. Its modular architecture (Nova, Cinder, Neutron, etc.) allows custom deployments. However, deploying OpenStack can be complex and requires skilled operators.
OpenNebula offers an open‑source cloud platform that emphasises vendor neutrality, unified management, high availability and flexibility. It supports KVM and VMware hypervisors, Kubernetes orchestration, and integrates with NetApp and Pure Storage. OpenNebula’s AI‑ready features include NVIDIA GPU support for large language models and multi‑site federation for global operations.
Eucalyptus is a Linux‑based IaaS that provides AWS‑compatible services like EC2 and S3. It supports various network modes (Static, System, Managed), access control, elastic block storage, auto‑scaling and integration with DevOps tools like Chef and Puppet. Eucalyptus enables organisations to build private clouds that seamlessly integrate with Amazon ecosystems.
Although not fully open-source (enterprise support is required), OpenShift is built on Kubernetes and provides enterprise security, CI/CD pipelines, developer‑focused tools, multi‑cloud portability and operator‑based automation. Version 4.20 emphasises security hardening, introducing post‑quantum cryptography, zero‑trust workload identity and advanced cluster security. It also enhances AI acceleration with features like LeaderWorkerSet API for distributed AI workloads and virtualization flexibility.
Platform9 offers a managed service for OpenStack and Kubernetes. Features include high availability, live migration, software‑defined networking, predictive resource rebalancing and built‑in observability. The platform supports both VMs and container workloads and can be deployed at scale across data centres or edge sites. Its vJailbreak migration tool simplifies migration from VMware or other virtualisation platforms.
With open‑source frameworks, organisations can use Clarifai’s local runner and compute orchestration API to deploy AI models on KVM or Kubernetes clusters. The vendor‑independent nature of these frameworks ensures control and customization, allowing Clarifai models to run near data sources without proprietary lock‑in.
Which emerging platforms address specific niches? Platforms like Platform9, Civo, Nutanix NC2, IBM Cloud Satellite, Google Distributed Cloud Edge, HPE Morpheus, and AWS Local Zones cater to specialised requirements such as edge computing, developer simplicity and sovereign deployments.
Platform9 provides a managed open‑source private cloud with features like familiar VM management, live migration, software‑defined networking and dynamic resource rebalancing. It offers both hosted and self‑hosted management planes, enabling enterprises to maintain control over security. Predictive resource rebalancing uses machine learning to optimise workloads, and built‑in observability surfaces metrics without external tools. Platform9’s hybrid capability supports edge deployments and remote sites.
Clarifai Integration: Use Platform9’s Kubernetes service to deploy Clarifai’s containerised models. The predictive resource feature can work in tandem with Clarifai’s compute orchestration to allocate GPU resources efficiently.
Civo is a developer‑first Kubernetes platform that provides a simple, cost‑effective private cloud. Its focus on rapid cluster provisioning and low overhead appeals to startups and development teams seeking to experiment with microservices. Civo’s managed environment offers predictable pricing, but its smaller ecosystem may limit integration options compared to major vendors.
Clarifai Integration: Deploy Clarifai models as containers on Civo clusters. Use Clarifai’s API to orchestrate inference workloads and manage models through CLI tools.
Nutanix NC2 on public clouds extends Nutanix’s hyperconverged infrastructure to AWS and Azure. The new sovereign cluster options support region‑based control planes, aligning with regulatory requirements. The platform’s security certifications and resilience enhancements cater to government and regulated industries.
IBM Cloud Satellite delivers a public cloud control plane and observability while running workloads locally. It uses an Istio‑based service mesh (Satellite Mesh) and integrates with IBM’s watsonx AI services. Google Distributed Cloud Edge offers a fully managed hardware and software stack for ultra‑low latency use cases such as AR/VR and 5G, built on Anthos. Both solutions enable consistent management across heterogenous sites.
Clarifai Integration: Deploy Clarifai models on Satellite or GDC Edge devices to perform inference near sensors or end‑users. Use Clarifai’s orchestrator to manage deployments across multiple edge locations.
HPE Morpheus VM Essentials reduces VMware licensing costs and provides multi‑hypervisor support. It introduces zero‑trust security with micro‑segmentation and stretched cluster technology for near‑zero downtime. AWS Local Zones bring select AWS services to metro areas for low‑latency access; they differ from Outposts by being provider‑owned but physically closer to users.
These emerging platforms fill gaps not addressed by mainstream solutions: Platform9 emphasises simplicity and predictive optimisation; Civo targets developers; Nutanix NC2 focuses on sovereign cloud; Satellite and GDC Edge cater to ultra‑low latency; Morpheus and Local Zones offer alternatives for cost and performance. Each can integrate with Clarifai to deliver AI inference at the edge or across multi‑cloud.
What trends are reshaping private cloud strategy?
Important trends include the surge of sovereign clouds, growing multi‑cloud adoption, end‑to‑end security & observability, edge computing and micro‑clouds, AI‑driven infrastructure, rising ARM servers, zero‑trust and confidential computing, sustainability mandates, and power/cooling constraints.
Governments increasingly require data to stay within national borders, driving demand for private and sovereign clouds. Providers respond by offering dedicated regions and sovereign clusters; companies must evaluate cross‑border compliance. Clarifai’s ability to run models entirely on‑premises helps maintain compliance with data residency laws.
Organisations adopt multiple clouds to avoid reliance on a single vendor and optimise costs. Private clouds must interoperate with public clouds and other private environments. Tools like Anthos, Platform9 and Clarifai’s compute orchestration facilitate cross‑cloud workload management.
Hybrid environments create blind spots. Emerging solutions emphasise cloud identity and entitlement management and observability across clouds. Platforms like OpenShift 4.20 and HPE Morpheus incorporate zero‑trust features. Clarifai ensures models are secured with access controls and can integrate with zero‑trust architectures.
Edge computing requires compact, self‑managing micro clouds. Autonomous edge clouds self‑configure and self‑heal, using AI to manage resources. Clarifai’s local runners allow AI inference on micro‑edge devices, connecting to central orchestration only when necessary.
The explosive demand for AI leads to AI‑first infrastructure with diverse GPU options and AI accelerators. Providers integrate GPU support (OpenNebula, GreenLake Private Cloud AI, Nutanix Enterprise AI) to meet LLM requirements. Clarifai’s platform abstracts hardware differences, enabling developers to deploy models without worrying about GPU vendor diversity.
ARM‑based servers enter mainstream due to lower power consumption and high core density. Private cloud platforms need to support heterogeneous architectures, including x86 and ARM. Clarifai’s inference engine runs on both architectures, providing flexibility.
Security strategies shift to zero‑trust, eliminating implicit trust and verifying each request. Confidential computing encrypts data in use, protecting data even from administrators. OpenShift 4.20 introduces post‑quantum cryptography and workload identity. Confidential VMs and enclaves appear in many platforms. Clarifai uses secure enclaves to protect sensitive AI models.
Regulations will require organisations to disclose the environmental impact of their IT infrastructure. Data centres face power and cooling constraints; thus, efficient design, renewable energy and optimisation become priorities. Some providers offer carbon accounting dashboards. Clarifai optimises model inference to reduce compute usage and energy consumption.
How should organisations evaluate private cloud platforms? Assess workload requirements, existing infrastructure, regulatory obligations, AI needs, cost models and vendor ecosystem. Create a shortlist by mapping must‑have capabilities to platform features and test with pilot deployments.
Below is a comparison of selected platforms across key features. Note that high‑level summaries cannot capture every nuance; conduct detailed evaluations for procurement decisions.
|
Platform |
Billing Model |
AI/GPU Support |
Multi‑Cloud Integration |
Security Features |
Unique Strengths |
|
HPE GreenLake |
Consumption‑based pay‑per‑use |
Private Cloud AI with NVIDIA GPUs |
Integrates with public clouds and edge |
Zero‑trust micro‑segmentation, stretched clusters |
Flexible hypervisor support, strong hardware portfolio |
|
VMware Cloud Foundation |
Traditional licensing with ROI benefits |
GPU support via vSphere & Tanzu |
Hybrid via VMware Cloud on AWS/Azure |
Zero‑trust, micro‑segmentation, encryption |
Unified compute, storage & networking; high ROI |
|
Nutanix Cloud Platform |
Subscription |
NVIDIA AI Enterprise with STIG compliance |
Multicloud with NC2 & sovereign clusters |
Micro‑segmentation, ISO & FIPS certifications |
Sovereign cloud focus, resilience features |
|
IBM Cloud Private/Satellite |
Subscription |
GPU via OpenShift & watsonx |
Satellite extends IBM Cloud anywhere |
Istio‑based service mesh, encryption |
Open‑source portability, strong enterprise software integration |
|
Oracle Cloud@Customer |
Universal credits, pay‑as‑you‑go |
GPU instances, AI services |
OCI Dedicated Region & Cloud@Customer |
Isolated network virtualization, compliance |
Integration with Oracle databases, consistent pricing |
|
AWS Outposts |
Multi‑year subscription |
GPU options via EC2 |
Unified AWS ecosystem |
AWS security & compliance features |
Broadest service portfolio, low latency |
|
Azure Local/Stack |
Pay‑as‑you‑go |
GPU support via Azure services |
Hybrid via Azure Arc & public cloud |
Azure’s security tools |
Consistent developer experience across cloud & on‑prem |
|
Google Anthos & GDC |
Subscription |
GPU via GKE & GDC Edge |
Multi‑cloud across Google & other clouds |
Anthos Config Management & Istio mesh |
Open‑source leadership, strong AI & analytics |
|
Dell APEX |
Consumption model |
GPU options via Dell hardware |
Limited; more edge/branch oriented |
VMware security features |
Flex on Demand procurement; edge focus |
|
OpenStack |
Free (open source); paid support |
GPU via integration |
Federation & multi‑cloud; vendor neutral |
Depends on deployment |
High flexibility, community ecosystem |
|
OpenShift |
Subscription |
AI acceleration & virtualization |
Multi‑cloud portability |
Post‑quantum cryptography, zero‑trust |
Developer‑centric, CI/CD integration |
How can organisations effectively run AI and machine learning workloads on private clouds? By selecting GPU‑enabled hardware, leveraging Kubernetes and serverless frameworks, adopting MLOps practices, and integrating with Clarifai’s AI platform for model management and inference.
AI workloads benefit from GPUs and accelerators. When building a private cloud, choose nodes with NVIDIA GPUs or other accelerators. HPE GreenLake’s Private Cloud AI bundles include NVIDIA RTX GPUs; OpenNebula offers integrated GPU support; Nutanix provides government‑ready NVIDIA AI Enterprise software.
Modern AI workloads are containerised. Use Kubernetes with operators to deploy and scale models. OpenShift offers built‑in CI/CD and operator frameworks. Clarifai provides Kubernetes operators and Helm charts for deploying inference services. For batch processing, schedule jobs with Kubernetes CronJobs or serverless functions.
Establish pipelines for model training, validation, deployment and monitoring. Integrate tools like Kubeflow, Jenkins or GitLab CI. Clarifai’s platform includes model versioning, A/B testing and drift detection, enabling continuous learning across private clouds. Use Anthos Config Management or OpenShift GitOps to enforce consistent policies.
Deploy models near data sources to minimise latency. Use Outposts, Azure Local, GDC Edge, IBM Satellite or HPE Morpheus to run inference. Clarifai’s local runner executes models offline, synchronising results when connectivity is available. This is essential for autonomous vehicles, industrial robots and field sensors.
Protect AI models and data with encryption, access controls and isolated environments. Use zero‑trust architecture and confidential computing where possible. Implement robust logging and monitoring, integrating with platforms like VMware Aria or Platform9’s observability. Clarifai supports secure APIs and can run within encrypted enclaves.
Benchmark model performance on target hardware. Use GPU utilisation metrics and dynamic resource rebalancing (e.g., Platform9’s predictive rebalancing). Clarifai’s compute orchestrator allocates resources based on workload demands and can spin up additional nodes if necessary.
What are the most common questions about private cloud hosting? Readers often ask about the differences between private and public clouds, cost considerations, security benefits, integration with AI platforms like Clarifai, and strategies for migration and scaling.
Private cloud hosting is evolving rapidly to meet the demands of regulation, AI and edge computing. Organisations now have a rich landscape of options—from consumption‑based enterprise stacks and managed public cloud extensions to open‑source frameworks and niche providers. Key trends such as sovereign cloud, multi‑cloud strategies, zero‑trust security and sustainability shape the ecosystem. When selecting a platform, consider workload requirements, AI readiness, cost models and vendor ecosystems. Integrating a flexible AI platform like Clarifai ensures you can deploy and manage models across any environment, unlocking value from data while maintaining control, compliance and performance
