Top 10 Hybrid Cloud Providers

Introduction

Hybrid cloud has evolved from a tactical workaround to a strategic foundation. Enterprises increasingly blend private infrastructure with public cloud services to balance control, compliance and agility, and they are doing so at a moment when artificial intelligence and machine‑learning workloads are exploding. Gartner predicts that by 2027 some 90 % of organisations will adopt hybrid cloud models, reflecting a shift away from single‑provider dependency toward flexible architectures that can place every workload where it makes the most sense. Hybrid approaches are now board‑level priorities because they enable generative AI at scale, sovereign data control, legacy coexistence, predictable economics through FinOps, and measurable sustainability.

Modern hybrid platforms deliver more than compute and storage. They combine automation, AIOps, cost governance and carbon dashboards to provide day‑two operations that are responsive and intelligent. They also support edge computing and GPU‑accelerated tasks essential for AI/ML. The rise of open platforms like Kubernetes and container‑native services has further democratized hybrid cloud by allowing developers to build once and run anywhere. Meanwhile, Clarifai, a leader in artificial intelligence, provides compute orchestration, model inference and local runners that can be deployed across clouds or on‑premises to serve computer‑vision, NLP and multimodal workloads.

This comprehensive guide dissects the top 10 hybrid cloud providers for 2026. It evaluates each provider’s strengths, innovations and trade‑offs, integrating expert insights, real‑world data and trending topics. The article begins with foundational context—what hybrid cloud means today and how to choose a provider—then dives into detailed analyses of AWS, Azure, Google Cloud, IBM, Oracle, VMware, Cisco, HPE, Dell and Nutanix. A dedicated section explores how Clarifai’s AI platform fits into hybrid architectures, and we finish with emerging trends, future outlook and frequently asked questions.

Quick Digest

The following sections provide deep dives into each provider and guidance on selecting the right hybrid cloud strategy.

What Is Hybrid Cloud & Why It Matters?

Quick summary – Why does hybrid cloud matter in 2026?

Hybrid cloud combines private and public environments so organisations can place each workload where it runs best, optimising performance, cost, compliance and sustainability. With AI and data‑intensive workloads rising, hybrid architectures enable companies to keep sensitive data close while leveraging cloud scale.

A nuanced definition of hybrid cloud

A hybrid cloud is not just using two different clouds; it is an integrated environment that unifies on‑premises infrastructure or private clouds with public cloud services. Intel defines hybrid cloud as a model that leverages the computing resources of both private and public clouds. This integration allows organisations to assign each workload to the most suitable environment based on latency, regulatory requirements, performance and cost. Sensitive workloads or those requiring low latency can remain on‑premises or in a private cloud, while elastic or burstable workloads can run in the public cloud to tap scalable resources on demand. Hybrid cloud is therefore a dynamic model that adapts to business needs rather than a fixed deployment.

Hybrid cloud is distinct from multicloud. In a multicloud approach, enterprises use multiple public clouds but manage them independently. Hybrid cloud blends private and public environments under a unified management plane and often includes edge sites, such as factories or retail stores, which host compute and storage closer to where data is generated. Many modern strategies combine hybrid and multicloud capabilities because enterprises may connect private infrastructure to more than one public cloud for resilience and vendor diversification.

Why hybrid cloud is a board‑level priority

Five factors elevate hybrid cloud to the C‑suite agenda for 2026. First, generative AI requires proximity to data and accelerators: models need high‑bandwidth GPUs near data sources for training and inference, but overflow capacity in public regions is essential for spikes. Second, sovereign control over sensitive data demands in‑country processing and auditable controls. Third, legacy coexistence means enterprises cannot rewrite every application overnight; hybrid platforms allow mainframes or monoliths to run alongside modern containerised workloads. Fourth, predictable economics are achieved through FinOps practices that transform consumption data into business metrics and forecasting. Finally, sustainability targets push organisations to measure power use, renewable energy and lifecycle impact, aligning workload placement with carbon goals.

Adoption statistics and drivers

Analysts forecast massive growth in hybrid cloud adoption. Gartner predicts that 90 % of organisations will adopt hybrid cloud by 2027. This trend is driven by the need for flexibility, cost optimisation and disaster recovery; distributing data and applications across multiple environments reduces vendor lock‑in and improves resilience. Another driver is the rapid convergence of edge computing and serverless services, which push compute and data closer to the source and allow developers to focus on code rather than infrastructure. Cloud governance and data sovereignty pressures are pushing private cloud adoption back into vogue, while sustainable cloud initiatives and FinOps help organisations meet carbon mandates and manage budgets.

Hybrid cloud as an enabler of AI and edge computing

AI workloads often demand hybrid architectures. Training large language models or computer‑vision systems may require thousands of GPUs housed in hyperscale clouds, but inference for real‑time decisions (e.g., quality inspection on a factory line or patient monitoring in healthcare) must happen with sub‑millisecond latency. Hybrid cloud allows data scientists to train models in the cloud and deploy inference on‑premises for privacy and latency reasons. Clarifai facilitates this by providing compute orchestration and local runners that run models on edge servers or devices, while the central platform in the cloud manages versioning and updates. Hybrid cloud also enables data gravity management—keeping data local to avoid egress fees or comply with data‑sovereignty laws, yet synchronising with central models for continuous learning.

How to Choose a Hybrid Cloud Provider

Quick summary – What criteria should guide your choice?

When selecting a hybrid cloud partner, consider workload requirements, integration with existing systems, AI‑readiness, cost models, compliance needs, security posture, sustainability metrics and operational maturity. Also evaluate vendor lock‑in, portability and support for edge and DevOps workflows.

Understand your workloads and application dependencies

Begin by analysing your workload portfolio. Are you hosting legacy enterprise applications, microservices, AI/ML pipelines or IoT workloads? Some providers excel at database‑heavy workloads (Oracle), others at containerised applications (Google Anthos, Azure Arc), while certain platforms are designed for HPC and AI (AWS, HPE). Knowing your requirements will help you align with providers’ strengths and avoid mismatches.

Assess application dependencies such as specific databases, middleware and operating systems. For example, if your organisation relies on VMware vSphere, a platform like VMware Cloud Foundation or Dell APEX Hybrid Cloud may ease migration and avoid expensive refactoring. Similarly, heavy use of Microsoft SQL and Windows may push you toward Azure, whereas Oracle workloads may benefit from OCI.

Evaluate integration and management tools

A key differentiator among providers is how they manage hybrid environments. Azure Arc projects on‑premises servers and Kubernetes clusters into Azure Resource Manager, allowing you to use familiar tools like Azure Policy and Monitor across environments. AWS Control Tower and Systems Manager provide governance and automated patching across accounts and on‑premises environments. Google Anthos uses the same control plane across clouds and on‑premises. Evaluate whether a provider’s management tooling integrates with your existing monitoring, CI/CD pipelines and infrastructure‑as‑code frameworks (e.g., Terraform, Ansible).

Integration also extends to AI and ML services. If your strategy relies on accelerated computing, check whether the provider offers GPUs, TPUs or dedicated AI hardware and whether you can provision them on‑premises (e.g., AWS Outposts with GPU‑enabled servers) or via partner solutions (e.g., HPE GreenLake’s Alletra Storage MP supporting AI workloads). Clarifai’s platform can orchestrate workloads across providers, but hardware availability influences performance and cost.

Examine pricing models and FinOps capabilities

Hybrid cloud pricing can be complex. Some providers offer pay‑as‑you‑go models with consumption‑based billing (AWS, Azure, Nutanix), while others use reserved capacity or subscription credits (OCI’s Universal Credits). Evaluate egress fees, licensing portability (Nutanix NC2 allows you to bring existing licences across clouds), and support costs (OCI includes enterprise support in base pricing).

FinOps discipline is crucial for hybrid environments. Leading providers now embed cost analytics and anomaly detection. GreenLake Intelligence delivers spend anomaly alerts and recommendations for cost‑saving changes. AWS Security Lake aggregates logs for centralised security and cost auditing. Clarifai workloads generate compute and storage costs, so ensure your provider’s FinOps tools can allocate AI expenses accurately across departments.

Prioritise compliance, security and sovereignty

Compliance requirements vary by industry and geography. Providers offer sovereign regions, security certifications (FedRAMP, ISO 27001), and private connectivity options like AWS Direct Connect, Azure ExpressRoute and Oracle FastConnect. IBM Cloud Satellite and OCI Cloud@Customer bring cloud services into customer facilities to meet strict data‑residency mandates. Evaluate encryption, identity, and zero‑trust controls across the hybrid environment. Cisco’s platform integrates networking and security so policies can be enforced consistently.

Gauge operational maturity: automation and self‑healing

Day‑two operations differentiate leading providers. Automation‑first operations reduce manual toil and errors. VMware offers intrusion detection and recovery in Cloud Foundation. HPE GreenLake Intelligence deploys agentic AI agents that coordinate across storage, networking and compute. Azure Arc integrates with DevOps and GitOps workflows, enabling policy‑as‑code. Evaluate features like automatic patching, self‑healing, and integrated observability to ensure long‑term stability.

Consider sustainability and carbon dashboards

Sustainability is now a core selection criterion. Cloud providers publish power usage effectiveness (PUE) and renewable energy metrics. HPE GreenLake offers a Sustainability Insight Center with predictive forecasting and hardware‑related carbon footprints. LinkedIn’s analysis notes that top providers provide measurable sustainability and carbon dashboards. Align your hybrid strategy with environmental, social and governance (ESG) goals by choosing providers that disclose energy use and offer tools to optimise placement based on carbon impact.

Amazon Web Services (AWS) – Outposts, Local Zones & Beyond

Quick summary – Why choose AWS for hybrid deployments?

AWS extends its cloud into customer sites via Outposts racks and servers, Local Zones and Wavelength, while offering unified governance and security tools. It’s ideal for organisations seeking a comprehensive service catalog and consistency across cloud and on‑prem, though pricing can be complex.

Hybrid offerings: Outposts, Local Zones and Wavelength

AWS pioneered hybrid cloud by bringing its services on‑site. AWS Outposts are fully managed racks or servers delivered to a customer’s facility, running the same infrastructure, services and APIs as AWS regions. DataCamp explains that Outposts bring AWS infrastructure, services, APIs and tools to on‑premises locations, allowing organisations to avoid re‑architecting applications and maintain consistent operations. Outposts offer core services like EC2, EBS, S3, ECS/EKS, RDS and EMR, with AWS managing maintenance and patches. Businesses can connect Outposts to AWS through Direct Connect or VPN for low‑latency networking.

AWS Local Zones extend AWS infrastructure to metropolitan areas for ultra‑low latency, supporting use cases like video editing, real‑time gaming and financial trading. AWS Wavelength brings compute and storage to telco edge sites to enable 5G applications. These services complement Outposts by positioning compute closer to end users or devices.

Management and governance tools

Operating across environments can be complex, so AWS offers tools to standardise governance. AWS Systems Manager provides unified operational control, patching and inventory across EC2 instances, on‑prem servers and virtual machines. Control Tower sets up landing zones and enforces guardrails across AWS accounts. Amazon Security Lake centralises security data from various sources, simplifying threat detection and compliance, while IAM Roles Anywhere extends AWS Identity and Access Management to on‑premises workloads.

These tools are essential when running hybrid AI workloads. For example, a manufacturing company using Clarifai’s computer‑vision models may deploy inference on Outposts servers near production lines to avoid latency. The models sync with training pipelines in the AWS cloud. Systems Manager ensures consistent configuration and patching, while Security Lake aggregates logs for compliance.

Strengths, trade‑offs and expert insights

AWS offers the largest portfolio of cloud services, a global footprint and deep integration with DevOps and AI tools. The main trade‑off is pricing complexity; users must monitor consumption across resources, and egress fees can accumulate. AWS’s hybrid strategy emphasises tight integration with its public cloud; organisations seeking independence may find vendor lock‑in a concern.

Expert insights:

• Plan network connectivity early: Use Direct Connect or Local Zones to minimise latency and bandwidth costs for on‑premises AI inference.
  
  
• Use AWS License Manager to track software entitlements across cloud and Outposts.
  
  
• Leverage AWS’s AI services (SageMaker, Bedrock) for training and use Clarifai on Outposts for specialised inference.
  
  
• Monitor with FinOps tools; AWS provides cost explorer and budgets, but third‑party tools can help allocate costs by department.
  
  

Microsoft Azure – Arc & Hybrid Stacks

Quick summary – Why choose Azure for hybrid cloud?

Azure’s Arc and Stack solutions provide a unified management plane across on‑premises, edge and multicloud environments, allowing organisations to use familiar Azure tools anywhere. Azure’s integration with Microsoft products and extensive compliance certifications make it attractive for enterprises.

Azure Arc: project your resources into Azure

Azure Arc is a bridge connecting disparate environments to the Azure control plane. According to Microsoft’s documentation, Azure Arc delivers a consistent multicloud and on‑premises management platform by projecting servers, Kubernetes clusters and databases into Azure Resource Manager. This means you can apply Azure policies, monitoring, identity and governance to resources running outside Azure. It enables operations teams to manage VMs and clusters as if they were native Azure resources and to integrate with DevOps pipelines.

Arc also extends services such as Azure Machine Learning, Azure App Service and Logic Apps to on‑premises or other clouds. For AI workloads, you can train models in Azure and then deploy inference on Arc‑enabled Kubernetes clusters running in your data centre or at the edge. Clarifai can run inside Kubernetes clusters orchestrated by Arc, allowing consistent management.

Azure Stack family and Azure Hybrid Benefit

For organisations needing dedicated hardware on‑premises, Azure Stack HCI and Azure Stack Hub provide hyper‑converged infrastructure that runs Azure services. Customers can deploy IaaS and PaaS services locally with integrated updates and unified billing. Azure Stack is often used by industries with strict data‑residency requirements or intermittent connectivity.

Azure Hybrid Benefit allows customers with existing Windows Server and SQL Server licences to reduce costs when running these workloads in Azure or on Azure Stack. Combined with Azure ExpressRoute, which provides private connectivity to Microsoft’s backbone, enterprises can build resilient hybrid architectures.

Strengths, trade‑offs and expert insights

Azure’s key strength lies in its synergy with the Microsoft ecosystem: integration with Windows, Office 365, Power BI and Dynamics 365, plus strong identity and access management through Azure Active Directory. Azure has a broad network of compliance certifications and government regions.

Challenges include potential complexity in Arc configuration and licensing if you aren’t already a Microsoft customer. Azure’s AI services (OpenAI on Azure) may be subject to region availability.

Expert insights:

• Adopt Arc gradually: Start with servers or Kubernetes clusters before enabling advanced services.
  
  
• Use Azure Policy to enforce configurations across hybrid resources.
  
  
• Evaluate Arc’s data services (PostgreSQL Hyperscale, SQL Managed Instance) for running databases on premises with cloud‑based updates.
  
  
• Combine Clarifai with Azure Cognitive Services; choose the appropriate service for inference, using Clarifai when custom training or privacy is required.
  
  

Google Cloud – Anthos & Distributed Cloud

Quick summary – Why choose Google Cloud for hybrid?

Anthos provides a unified platform to build and manage applications across on‑premises, Google Cloud and other public clouds, with strong support for Kubernetes and open‑source technology. Google’s AI and analytics offerings complement hybrid deployments.

Anthos and multicloud consistency

Google Anthos is built on Kubernetes and Istio, enabling organisations to deploy and manage containerised applications consistently across different environments. Data Centre Magazine notes that Anthos manages applications across on‑premises, Google Cloud and other clouds. With Anthos, developers can build once and deploy anywhere, using the same CI/CD pipelines, service mesh, monitoring and policy frameworks.

Anthos supports VMware, bare metal and public cloud environments. Google further offers the Google Cloud VMware Engine to run VMware workloads natively on Google Cloud, which simplifies migration.

Google Distributed Cloud: edge and hosted solutions

Google Distributed Cloud (GDC) extends Google services to the edge and into customer data centres. It has two variants: GDC Edge, which runs on telecom and enterprise edge sites to support low‑latency applications such as AR/VR and 5G, and GDC Hosted, a fully managed solution running in customer data centres for regulated industries. GDC integrates with Anthos to offer a consistent development and operations experience.

Strengths, trade‑offs and expert insights

Google’s strengths include open‑source leadership, strong data analytics (BigQuery, Dataflow), and AI services with TPUs for machine learning. Anthos emphasises developer productivity and multi‑cloud freedom, appealing to organisations prioritising modern application development. However, enterprises heavily invested in Microsoft or VMware ecosystems may find migration more involved.

Expert insights:

• Leverage Anthos Config Management to enforce policies and keep configurations in sync across clusters.
  
  
• Use GDC Edge for latency‑sensitive AI inference, and combine Clarifai’s models with Google’s AI platform for training.
  
  
• Evaluate migration with Migrate to Containers or Migrate to VM when moving traditional workloads to containerised environments.
  
  
• Plan identity integration with Google Identity-Aware Proxy and Cloud IAM when spanning multiple clouds.
  
  

IBM – Cloud Satellite & OpenShift Ecosystem

Quick summary – Why choose IBM for hybrid?

IBM Cloud Satellite extends IBM Cloud services—including compute, data, AI and security—to any environment, delivering a consistent experience across data centres, edge locations and public clouds. Its foundation on Red Hat OpenShift provides open‑source flexibility and Kubernetes portability.

IBM Cloud Satellite: a control plane anywhere

IBM Cloud Satellite uses a control plane in the public cloud and satellite locations in customers’ data centres or other clouds. SDxCentral reports that Satellite allows workloads to run wherever it makes the most sense, while centralised management provides observability, configuration and security policies across environments. Satellite’s architecture uses Razee for continuous delivery and Istio‑based Satellite Mesh for service discovery and security. This design ensures that applications can run with the same DevOps tools and managed services, regardless of location.

Satellite integrates with IBM watsonx for AI and Cloud Pak solutions for security, data and automation. Because it’s built on Red Hat OpenShift, customers can use open‑source Kubernetes tools and run workloads consistently across multiple clouds. IBM emphasises its ability to meet regulated industry requirements (financial services, healthcare, government) with features like data residency controls and encryption.

Strengths, trade‑offs and expert insights

IBM’s hybrid strategy is attractive to industries requiring security, compliance and open‑source alignment. By using OpenShift, IBM avoids vendor lock‑in and appeals to organisations adopting Kubernetes. IBM invests heavily in AI and quantum computing, offering dedicated cloud services for both.

Trade‑offs include potentially smaller market share and ecosystem compared to AWS or Azure, and integration complexity if you’re not already using Red Hat tools.

Expert insights:

• Leverage Satellite Locations for regulated workloads requiring in‑country deployment.
  
  
• Use IBM Cloud Pak for Data to build AI models and integrate with Clarifai when custom computer‑vision models are needed.
  
  
• Combine OpenShift with Ansible Automation Platform for infrastructure and application automation.
  
  
• Evaluate pricing as IBM sometimes bundles services; ensure transparency.
  
  

Oracle – OCI & Cloud@Customer

Quick summary – Why choose Oracle for hybrid?

Oracle Cloud Infrastructure (OCI) delivers high‑performance compute, storage and networking with flexible deployment models and lower costs than competitors, while Cloud@Customer brings OCI into customer data centres for stringent data‑residency requirements. OCI’s hybrid capabilities make it appealing for enterprises running Oracle databases or ERP systems.

OCI’s high‑performance architecture and services

Finout’s analysis notes that OCI differentiates itself through high performance, hybrid capabilities and integration with Oracle’s enterprise software. It allows organisations to deploy applications in the cloud or in a hybrid mode spanning on‑premises and cloud infrastructure. OCI uses isolated network virtualisation and off‑box network virtualisation to enhance security and performance.

OCI offers a wide range of services across compute, storage, networking, databases and AI. Compute options include virtual machines, bare metal and GPU instances; storage options range from block volumes to object and file storage; networking features include FastConnect for private connectivity and multicloud integration. Oracle Autonomous Database and Exadata provide high‑availability, self‑managing databases. OCI also offers AI, analytics and integration services that allow organisations to process large datasets and build applications across hybrid environments.

Transparent pricing and cost controls

OCI’s pricing is notable for its uniform global pricing and lower costs compared with other major clouds. Flexible compute and storage costs allow customers to select exact CPU and memory configurations. Public bandwidth egress fees are up to ten times lower than competitors, with the first 10 TB per month included. Cost controls include budgets, usage reports and recommendations from Oracle Cloud Advisor. Oracle Universal Credits let customers prepay for services and apply them flexibly across OCI, while Support Rewards reduce on‑premises support costs when OCI usage increases.

Cloud@Customer: OCI in your data centre

OCI Cloud@Customer brings the same OCI services and infrastructure into customer data centres, enabling organisations to run workloads locally for latency, regulatory or data‑sovereignty reasons while still consuming services as if they were in the cloud. Cloud@Customer is particularly suited for industries like finance, healthcare and government that require dedicated hardware.

Strengths, trade‑offs and expert insights

OCI excels in high‑performance workloads and cost predictability. Its integration with Oracle’s database and enterprise software is unrivalled, making it a natural choice for Oracle-centric organisations. However, OCI’s ecosystem is smaller than those of AWS and Azure, which may limit third‑party integrations.

Expert insights:

• Take advantage of uniform pricing to forecast budgets; use OCI’s cost estimator before migration.
  
  
• Leverage FastConnect for dedicated connectivity when running Clarifai models requiring low‑latency access to data.
  
  
• Use Reserved Instances for predictable workloads to secure discounts.
  
  
• Implement fault domains and multi‑availability domains to enhance resilience.
  
  

VMware – Cloud Foundation & Cross‑Cloud Services

Quick summary – Why choose VMware for hybrid cloud?

VMware Cloud Foundation (VCF) delivers a consistent, secure hybrid platform across private and public clouds, combining vSphere, vSAN, NSX and vRealize, and it enables workload portability to AWS, Azure, Google, Oracle and IBM. Organisations heavily invested in VMware can extend their environments without refactoring.

Unified software stack and partner integrations

VCF bundles vSphere for compute virtualisation, vSAN for software‑defined storage, NSX for software‑defined networking and security, and vRealize (now part of VMware Aria) for management and automation. Data Centre Magazine notes that VCF provides a consistent, secure platform with intrusion detection and recovery. This consistency allows organisations to move workloads between on‑premises and partner clouds (VMware Cloud on AWS, Azure VMware Solution, Google Cloud VMware Engine, Oracle Cloud VMware Solution) with minimal changes.

VCF integrates with VMware Tanzu for containerised workloads, enabling developers to run Kubernetes alongside traditional VMs. VMware Cross‑Cloud services provide a console for multi‑cloud management, cost optimisation and application networking.

Strengths, trade‑offs and expert insights

The primary strength of VCF is its familiar environment; IT teams can leverage existing VMware skills and tools, reducing learning curves. VCF is also widely supported across hyperscalers, giving enterprises flexibility. However, licensing can be expensive, and organisations may still need to invest in separate services for advanced AI or analytics.

Expert insights:

• Plan your SDDC design carefully to balance performance and availability across fault domains.
  
  
• Use vRealize Operations (Aria Operations) to monitor hybrid environments and right‑size resources.
  
  
• Integrate Tanzu for modern apps; Clarifai can run in containers managed by Tanzu.
  
  
• Evaluate partner ecosystems (e.g., AWS, Azure, Google) for region availability and pricing.
  
  

Cisco – Intersight & Platform Approach

Quick summary – What makes Cisco’s hybrid strategy unique?

Cisco adopts a platform approach that unifies networking, security and compute, and uses automation and AI‑driven insights to streamline IT operations. Its Intersight platform manages UCS and HyperFlex infrastructure while integrating with third‑party tools for a cohesive hybrid experience.

The platform approach and unified management

Cisco’s platform strategy aims to integrate hardware, software and services into cohesive systems to improve efficiency and agility. In practice this means combining networking (Catalyst and Nexus switches), security (Cisco Secure Access) and collaboration tools under common automation, telemetry and APIs. For hybrid cloud, the flagship is Cisco Intersight, a SaaS‑based or on‑premises platform that provides automation and AI‑driven insights for infrastructure lifecycle management. Intersight allows administrators to view and control Cisco UCS servers and HyperFlex hyper‑converged infrastructure; it also connects to third‑party targets, offering predictive analytics and workflow automation.

Intersight is complemented by Cisco ACI (Application Centric Infrastructure) for software‑defined networking and Cisco Nexus Dashboard for multi‑site management. Cisco also provides Meraki for cloud‑managed networking and AppDynamics for application performance monitoring, enabling full‑stack observability.

Strengths, trade‑offs and expert insights

Cisco’s strengths lie in networking and security. For organisations with complex networks or branch offices, Cisco’s platform approach reduces complexity and provides consistent policy across on‑premises and cloud. AI‑driven insights help automate updates and reduce downtime. However, Cisco’s ecosystem is primarily focused on infrastructure; it may require partnering with cloud providers for platform services and advanced AI.

Expert insights:

• Leverage Intersight Workload Optimizer to allocate resources efficiently and avoid overprovisioning.
  
  
• Use ACI and Secure Firewall to enforce consistent micro‑segmentation across hybrid environments.
  
  
• Integrate Clarifai models into edge devices (e.g., Cisco cameras or IoT modules) and orchestrate them through Intersight for updates and monitoring.
  
  
• Consider sustainability; Cisco emphasises energy‑efficient hardware and offers energy management capabilities in Intersight.
  
  

HPE – GreenLake & GreenLake Intelligence

Quick summary – Why choose HPE’s GreenLake for hybrid?

HPE GreenLake provides consumption‑based infrastructure across edge, private and public environments and now integrates agentic AI through GreenLake Intelligence for real‑time optimisation, FinOps and sustainability.

From GreenLake to GreenLake Intelligence

Originally launched as a pay‑per‑use on‑premises infrastructure service, HPE GreenLake has evolved into a comprehensive edge‑to‑cloud platform. It offers servers, storage, networking and services under a consumption model, allowing enterprises to scale up or down without overprovisioning. Customers pay for actual usage, with capacity buffers installed on site.

In 2025 HPE introduced GreenLake Intelligence, an agentic AI framework that injects intelligence at every layer of the stack. IT Brief Asia reports that GreenLake Intelligence uses AI agents to simplify and enhance hybrid infrastructure management, reducing manual workflows and providing real‑time optimisation. The framework coordinates across domains—including storage, networking, compute, cost management, observability and sustainability—to analyse and act. For example, the HPE Aruba Networking Central agentic mesh analyses network conditions and recommends actions. The OpsRamp copilot provides automation for infrastructure remediation and incident management.

GreenLake Intelligence also includes FinOps and sustainability enhancements. The workload and capacity optimiser aligns resources with business objectives while controlling costs. A Sustainability Insight Center offers predictive carbon forecasting and hardware lifecycle metrics. These features are accessible via GreenLake Copilot, a conversational interface.

Strengths, trade‑offs and expert insights

HPE’s hybrid offering stands out for its agentic AI and integrated FinOps and sustainability capabilities. It is well suited for organisations wanting consumption‑based economics without sacrificing control. However, GreenLake may involve longer deployment timelines than public cloud, and customers must manage on‑premises capacity planning.

Expert insights:

• Use CloudPhysics Plus (now part of GreenLake) to assess workloads and determine optimal placement.
  
  
• Adopt OpsRamp Software Suite for orchestration, governance and cyber resiliency across multivendor infrastructure.
  
  
• Explore sustainability features to align workloads with power consumption and renewable energy targets.
  
  
• Integrate Clarifai workloads using HPE’s GPU‑ready servers and local AI accelerators; combine with GreenLake Intelligence for resource optimisation.
  
  

Dell – APEX Hybrid Cloud & Multicloud Platforms

Quick summary – Why choose Dell APEX?

Dell APEX delivers hybrid cloud and storage/compute as a service, combining VMware Cloud Foundation–based automation with flexible consumption models and a unified console. It appeals to organisations seeking on‑premises control with cloud‑like agility.

APEX services and hybrid offerings

The APEX portfolio comprises Data Storage Services, Cloud Services, and Custom Solutions. Within Cloud Services, APEX Hybrid Cloud is built on VMware Cloud Foundation, enabling workload automation across an organisation’s entire cloud environment. APEX Private Cloud uses VMware vSphere and vSAN to provide entry‑level infrastructure as a service for remote and branch offices.

APEX Cloud Platforms deliver turnkey on‑premises infrastructure aligned with public cloud partners. Dell offers platforms for Microsoft Azure, Red Hat OpenShift and VMware, allowing customers to run these ecosystems on Dell hardware. Dell has also integrated AWS storage services via APEX Block Storage and APEX File Storage.

APEX Custom Solutions provide flexible consumption models. Flex on Demand lets organisations pay only for the infrastructure they use, with a cap at 85 % of deployed capacity. Data Centre Utility offers fully managed data‑centre operations with a single invoice, using a pay‑per‑use model.

Dell’s APEX Console serves as a unified portal for selecting, provisioning and managing APEX services. It provides performance metrics and real‑time expense monitoring, enabling businesses to align spending with IT usage.

Strengths, trade‑offs and expert insights

APEX’s advantage is its holistic approach to hybrid cloud—combining infrastructure, storage, compute and data protection with consumption‑based billing. It leverages Dell’s hardware expertise and VMware’s software stack. However, the portfolio can be complex, and some services may not be available globally.

Expert insights:

• Use APEX Cloud Platforms to simplify adoption of Azure Arc or OpenShift on dedicated hardware.
  
  
• Deploy APEX Hybrid Cloud for VMware‑centric environments; pair with Clarifai for AI at the edge or in branch locations.
  
  
• Monitor through APEX Console and integrate with FinOps tools to optimise consumption.
  
  
• Explore custom solutions like Flex on Demand when planning capacity expansions.
  
  

Nutanix – NC2 & Hybrid Multicloud Freedom

Quick summary – Why choose Nutanix NC2?

Nutanix Cloud Clusters (NC2) deliver a hybrid multicloud platform that runs the Nutanix HCI stack on both on‑premises and public clouds, offering a single operational experience, portable licences and fast deployment.

Unified platform across clouds

NC2 runs Nutanix AOS (Acropolis Operating System), AHV (Nutanix’s hypervisor) and Prism management on bare‑metal instances in public clouds such as AWS, Azure, Google Cloud and OVHcloud. This means your on‑premises cluster and cloud cluster share the same data and management planes. Applications and data can be migrated or extended without redesign; the operational complexity of managing separate platforms is drastically reduced.

NC2 differentiates itself by being customer‑controlled rather than a managed service. Customers decide where and when to deploy clusters and repatriate workloads. This autonomy appeals to organisations that require flexibility or have compliance mandates.

Flexible licencing and consumption models

Nutanix offers portable licences so you can bring your own licences from on‑premises to NC2. Customers can also opt for pay‑as‑you‑go billing or a cloud commit model with a minimum term. The ability to pay for cloud infrastructure separately (to AWS or Azure) and Nutanix software separately gives customers cost transparency.

Strengths, trade‑offs and expert insights

NC2’s major strength is its consistent operating model across on‑premises and multiple clouds, reducing learning curves and simplifying management. It offers rapid deployment (clusters can be spun up within hours) and the flexibility to avoid vendor lock‑in. However, NC2 may require deeper knowledge of Nutanix’s ecosystem and may not offer the breadth of cloud services available from hyperscalers.

Expert insights:

• Use NC2 for migration and disaster recovery; spin up a secondary cluster on demand for DR or test/dev.
  
  
• Leverage portable licences to optimise costs when shifting workloads between on‑prem and cloud.
  
  
• Integrate Clarifai by running its models on AHV virtual machines or Kubernetes clusters managed by Nutanix Karbon, ensuring consistent management across sites.
  
  
• Assess network connectivity; ensure connectivity between clusters and data centres to avoid latency issues.
  
  

Integrating AI & Clarifai into Hybrid Cloud Deployments

Quick summary – How does Clarifai enhance hybrid cloud strategies?

Clarifai’s platform orchestrates AI workloads across cloud and on‑premises environments, providing model inference, training pipelines and local runners that can run wherever data lives. This flexibility makes it an ideal complement to hybrid cloud infrastructures.

AI‑ready infrastructure and Clarifai’s capabilities

Hybrid cloud adoption is tightly linked to AI deployment. Generative AI at scale requires GPU‑accelerated infrastructure, fast networking and high‑throughput storage. However, not every workload can run in a public cloud; privacy, latency and cost constraints dictate local inference. Clarifai addresses this by offering:

1. Compute orchestration – a platform that schedules training and inference tasks across cloud GPUs and on‑premises accelerators. This ensures efficient utilisation and reduces idle capacity.
   
   
2. Model inference and serving – packaged models that can be deployed as APIs on any infrastructure (containers, VMs, serverless). Clarifai’s models support computer vision, NLP and audio tasks.
   
   
3. Local runners – lightweight modules that allow models to run on edge devices or private servers without internet connectivity, synchronising results with the central platform when connectivity is available.
   
   
4. Data management and annotation tools – integrated tools for dataset curation, annotation, versioning and continuous improvement.
   
   

These capabilities enable enterprises to design hybrid AI pipelines: data is processed and annotated locally, models are trained in the cloud where GPUs are abundant, and inference is deployed on edge or private infrastructure using local runners. Clarifai’s orchestration ensures reproducibility and security, while its open APIs allow integration with DevOps pipelines.

Mapping Clarifai workloads to providers

Each provider’s hybrid platform offers different AI capabilities. When deploying Clarifai:

• AWS – Outposts can host GPU‑enabled servers for real‑time inference; training can occur in AWS using EC2 P4d instances or managed services like SageMaker, while Clarifai orchestrates models across both.
  
  
• Azure – Arc‑enabled Kubernetes or Azure ML services can run Clarifai containers in your data centre; Azure’s AI Accelerators (like the ND A100 v4 series) provide powerful training hardware.
  
  
• Google Cloud – Anthos and GDC allow Clarifai models to run on Kubernetes clusters across clouds; Google’s TPUs can accelerate training.
  
  
• IBM – Cloud Satellite integrated with watsonx supports AI workloads; Clarifai can augment IBM’s AI suite with custom computer‑vision models.
  
  
• Oracle – OCI’s GPU instances and Cloud@Customer deployments enable Clarifai to run inference next to Oracle databases, ensuring low latency and compliance.
  
  
• VMware – Tanzu with vSphere supports GPU pass‑through, allowing Clarifai to run on‑prem or on partner clouds.
  
  
• Cisco – Intersight can orchestrate hardware accelerators and manage network policies for edge devices running Clarifai models.
  
  
• HPE – GreenLake’s GPU‑ready servers combined with GreenLake Intelligence provide dynamic scaling and cost optimisation for Clarifai workloads.
  
  
• Dell – APEX Hybrid Cloud with VMware Cloud Foundation allows Clarifai containers to run across on‑premises and cloud; the APEX Console helps monitor AI spend.
  
  
• Nutanix – NC2’s unified management ensures Clarifai can be deployed consistently across on‑premises and cloud clusters, leveraging portable licences to optimise costs.
  
  

Best practices and expert insights

• Co‑locate data and inference: Keep inference close to data sources (e.g., factories, clinics) to minimise latency; train models in the cloud where compute is abundant.
  
  
• Use GPU scheduling: Many hybrid platforms now offer GPU scheduling and partitioning. Align Clarifai workloads with these capabilities to maximise utilisation.
  
  
• Implement FinOps for AI: AI workloads can be cost‑intensive. Use cost analytics and anomaly detection to manage spending, and plan ahead for training bursts.
  
  
• Govern data pipelines: Ensure data governance and sovereignty when moving data between environments. Encrypt data at rest and in transit, and comply with jurisdictional rules.
  
  

Emerging Trends & Future Outlook

Quick summary – What trends will shape hybrid cloud’s future?

Emerging trends include AI‑as‑a‑Service and AI‑driven operations, mass adoption of hybrid/multi‑cloud, serverless & edge convergence, quantum computing as a service, industry‑specific cloud platforms, data sovereignty and private cloud resurgence, sustainable cloud initiatives with FinOps, and agentic AI for day‑two operations.

AI‑driven cloud operations and AI‑as‑a‑Service

AI is moving beyond applications and into infrastructure management. iLink Digital notes that AI‑driven cloud operations will provide real‑time resource allocation, threat detection and optimisation, enabling unprecedented efficiency. AI‑as‑a‑Service will democratise access to large models and accelerators, while agentic AI frameworks like HPE GreenLake Intelligence will coordinate actions across the stack. Providers will compete on how quickly and accurately their AI can predict and remediate issues.

Hybrid/multicloud ubiquity and serverless/edge convergence

Hybrid adoption will become nearly universal by 2027. Serverless computing is merging with edge computing, enabling developers to run functions close to data sources with no infrastructure management. This synergy powers new applications such as autonomous vehicles and real‑time industrial monitoring. Hybrid platforms will need to support event‑driven architectures and edge functions alongside traditional services.

Quantum computing and industry clouds

Quantum computing is emerging as a cloud service, with forecasts estimating growth from US $1.1 billion in 2024 to US $12.6 billion by 2032. Hybrid platforms will integrate quantum simulators and processors, initially via cloud APIs, enabling hybrid classical‑quantum workflows. Industry‑specific clouds—tailored for sectors such as healthcare, finance and manufacturing—will package regulatory compliance, data models and integration templates.

Data sovereignty, private cloud resurgence and sustainable cloud

Rising privacy regulations and geopolitical considerations are driving a resurgence of private clouds, with organisations adopting hybrid strategies for sovereignty, cost and security. Providers are rolling out sovereign regions, data clean rooms and private cloud hardware (OCI Cloud@Customer, IBM Cloud Satellite) to address these concerns. Sustainability initiatives are also accelerating. Enterprises are using FinOps to measure carbon emissions and cost simultaneously. Uptime Institute reports an average power usage effectiveness (PUE) of 1.56, leaving room for efficiency improvements through renewable energy and smarter placement.

Agentic AI and policy‑as‑code

Agentic AI frameworks, such as HPE GreenLake Intelligence, represent a shift toward autonomous operations. LinkedIn’s analysis notes that top providers deliver day‑two operations with policy orchestration, self‑healing and full‑stack observability. Policy‑as‑code will become mainstream, enabling organisations to define security, compliance and resource rules programmatically and enforce them across environments. GPU scheduling and AI‑native infrastructure will be integrated into management platforms.

Future outlook

The next decade will see hybrid cloud become the default operating model. Providers will differentiate based on AI capabilities, open‑source flexibility, sustainability and industry expertise. Companies like Clarifai will help enterprises build AI‑native applications by providing portable, orchestrated models that run across any hybrid environment. Adopting hybrid strategies today positions organisations to leverage innovations like quantum computing, edge AI and carbon‑aware workloads tomorrow.

Frequently Asked Questions

What is the difference between hybrid cloud and multicloud?

Hybrid cloud integrates private infrastructure or on‑premises data centres with public cloud services under a unified management framework. Multicloud refers to using multiple public cloud providers independently. Hybrid architectures often include multicloud elements but focus on integration and mobility across environments.

How do I start migrating to a hybrid cloud?

Begin by assessing your workloads and data, identifying candidates for public cloud and those that must remain on‑premises (due to latency, compliance or data gravity). Pilot a small workload using a provider’s hybrid solution—such as AWS Outposts, Azure Arc or Nutanix NC2—to test integration and performance. Use assessment tools like CloudPhysics Plus (HPE) or OCI’s cost estimator to plan capacity and costs.

What are the key cost considerations?

Key factors include compute/storage pricing, egress fees, support costs and licensing. Providers like OCI offer uniform global pricing and lower egress fees; Nutanix allows portable licences; Dell’s Flex on Demand caps billing at 85 % usage. Use FinOps tools to track spending and allocate costs; many providers offer cost anomaly alerts and recommendations.

How is data secured across hybrid environments?

Security involves identity management, encryption, network segmentation and compliance controls. Providers offer features like AWS IAM Roles Anywhere, Azure Active Directory, Google Cloud IAM, Cisco ACI and VMware NSX. Many hybrid solutions provide private connectivity (Direct Connect, ExpressRoute, FastConnect) and in‑country deployments (OCI Cloud@Customer, IBM Cloud Satellite). Implement zero‑trust architectures and use policy‑as‑code to enforce rules across environments.

Can Clarifai models run in hybrid environments?

Yes. Clarifai provides compute orchestration, model inference and local runners that run on cloud, on‑premises or edge infrastructure. Models can be deployed via containers (Docker/Kubernetes) or APIs. You can train models in the cloud (using GPU instances) and deploy inference on edge hardware through providers like AWS Outposts, Azure Arc or Nutanix NC2. Clarifai integrates with CI/CD pipelines and supports offline operation with later synchronisation.

How do FinOps and sustainability fit into hybrid strategies?

FinOps practices enable organisations to align cloud spending with business outcomes and track resource utilisation. Sustainability metrics quantify energy use and carbon emissions. Leading providers embed cost analytics, anomaly detection and carbon dashboards. Adopt FinOps frameworks to make informed decisions about workload placement, such as moving a compute‑intensive task to a region with renewable energy or adjusting GPU allocation to reduce idle power consumption.

Conclusion

Hybrid cloud is no longer a transitional stage—it is the foundation for future computing. As enterprises race to deploy AI, meet regulatory obligations and achieve sustainability goals, hybrid architectures offer the flexibility and control needed to innovate responsibly. The top 10 providers discussed here—AWS, Azure, Google Cloud, IBM, Oracle, VMware, Cisco, HPE, Dell and Nutanix—represent a spectrum of strengths, from hyperscale service portfolios to industry‑focused platforms and AI‑native operations.

Selecting the right partner requires aligning business priorities with each provider’s capabilities. Consider workload characteristics, integration needs, AI readiness, pricing, security, sustainability and long‑term innovation roadmaps. Clarifai can accelerate your AI journey by orchestrating models across these hybrid platforms, enabling you to train in the cloud and deploy anywhere. Finally, stay attuned to emerging trends—agentic AI, quantum computing, serverless edge, industry clouds, data sovereignty and green computing—which will shape the next decade of hybrid cloud innovation.