하이브리드 클라우드 AI 워크로드 오케스트레이션 : 시장 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the hybrid cloud AI workload orchestration market size is expected to increase from USD 5.49 billion in 2025 to USD 7.56 billion in 2026 and reach USD 16.72 billion by 2031, expanding at a CAGR of 17.21% over 2026-2031.

Global Hybrid Cloud AI Workload Orchestration Market Trends and Insights

Accelerating LLM Training and Inference Deployment

Large language model training and inference are changing the operating requirements for hybrid AI infrastructure because production systems are no longer a single model running on a fixed set of servers, but a coordinated sequence of compute, storage, and networking tasks. Production systems must manage prefill nodes, decode nodes, and KV-cache tiers across private and public environments, often with different capacity conditions, performance requirements, and data controls. Static Kubernetes configurations cannot manage these stages efficiently without a dedicated orchestration layer that makes real-time decisions across resources and reflects the actual state of the underlying infrastructure. AWS introduced disaggregated inference capabilities for Amazon SageMaker HyperPod that leverage NVIDIA Dynamo, vLLM, and high-bandwidth KV cache movement across multi-node clusters. Google Cloud reported that Managed Lustre with GKE reduced GPU-hour requirements for Llama-3.3-70B inference by nearly 60% when KV caches were moved to a high-performance parallel file system. These multi-stage architectures broaden the role of the Hybrid cloud AI workload orchestration market from conventional job scheduling to dynamic routing of resource-intensive workload components.

GPU Utilization and Compute Cost Optimization Imperative

GPU utilization has become a central financial concern for organizations operating AI infrastructure at scale because unused accelerator capacity constrains new workloads while generating high fixed costs, whether the hardware is owned locally or consumed through a cloud commitment. Organizations with unused capacity face higher infrastructure costs, slower access to compute, less flexibility to meet changing model requirements, and more difficult internal discussions over where scarce GPU capacity should be allocated. Fujitsu reported that more than 75% of organizations had GPU utilization below 70% at peak load in 2024. Red Hat described an IBM Vela cluster deployment in which Kueue helped improve GPU utilization from a baseline below 30% to nearly 90% under active load. Microsoft stated that NVIDIA Run:ai supports dynamic GPU fractioning and quota sharing for Azure Kubernetes Service deployments. The Hybrid cloud AI workload orchestration market benefits because scheduling platforms are assessed as tools for recovering capacity, controlling spend, and creating fairer access to scarce compute across internal teams and tenants.

Heterogeneous Hardware and Cloud API Interoperability Gaps

The lack of a shared API layer among GPU vendors, cloud schedulers, and Kubernetes tools increases the cost and complexity of deploying a hybrid AI environment. Multi-cloud AI teams may need distinct integrations for AWS Elastic Fabric Adapter, Google Kubernetes Engine inference tools, and Azure Kubernetes Service with Run:ai, even when the business objective is a single, consistent operating model. These integrations do not provide a common workload abstraction that automatically evaluates availability, cost, carbon intensity, and the specific policy conditions of every possible environment. Vendor-specific communication libraries further complicate distributed use of different accelerator types, because moving data or coordinating jobs across hardware families can require custom implementation work. The constraint can delay adoption of multi-vendor GPU pools, even when those pools are attractive for supply resilience, purchasing flexibility, and cost control. The Hybrid cloud AI workload orchestration market faces added pressure because smaller vendors may lack the engineering resources to maintain integrations across clouds, Kubernetes distributions, accelerator platforms, and shifting provider interfaces.

Other drivers and restraints analyzed in the detailed report include:

1. Hybrid Data Residency and Sovereign AI Requirements
2. Distributed AI Scheduling Across Heterogeneous Accelerators
3. Shortage of AI Infrastructure Orchestration Specialists

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software accounted for 62.72% of revenue in 2025, reflecting the value customers assign to governance, scheduling, and policy enforcement layers above the physical compute environment. Within the Hybrid cloud AI workload orchestration market, these layers bring together functions that would otherwise reside in separate products, including workload admission, accelerator allocation, identity controls, audit records, and the rules governing how tenants use shared infrastructure across cloud and local systems. This integration can limit handoffs between tools and help administrators apply common operating rules to workloads that would otherwise be managed separately. Their recurring license and subscription model is less directly tied to hardware purchasing cycles, which supports a more durable revenue base for platform vendors. Red Hat launched Red Hat AI Enterprise in February 2026 as a unified platform extending from Linux and Kubernetes to governed AI models, agents, and applications. The release shows how providers are consolidating formerly separate infrastructure products into broader agreements, simplifying procurement and increasing software revenue per customer.

Services are projected to expand at a CAGR of 17.61% between 2026 and 2031, driven by implementation and operational requirements that many internal IT teams cannot initially manage on their own. A deployment may require an assessment of data locations, workload profiles, cluster configurations, cloud contracts, access rules, security controls, and ongoing performance requirements before a unified control plane can be put into production. Professional services can establish the architecture and integrate these operating elements, while managed services can maintain performance, cost visibility, and operational discipline after launch. Organizations often start with service-heavy projects while they build internal knowledge, then shift a greater share of spending toward software licenses and platform subscriptions as their teams take on more operational work. The Hybrid cloud AI workload orchestration industry, therefore, combines software pricing power with the continuing demand for external expertise to help customers configure, operate, and improve their environments.

Cloud held 57.28% of revenue in 2025, supported by elastic GPU capacity, broad regional availability, and managed infrastructure services that reduce the need for enterprises to own every part of their AI environment. The Hybrid cloud AI workload orchestration market continues to rely on cloud environments for workloads that require no strict data-residency constraints, temporary capacity needs, and access to large GPU fleets without a major upfront hardware investment. Cloud use can also reduce the time required to scale compute when a project moves from a small pilot to a larger production workload. Hyperscalers also provide managed services that can reduce routine management requirements for customers lacking deep internal expertise in cluster administration or distributed infrastructure. Roche deployed more than 3,500 NVIDIA Blackwell GPUs across hybrid cloud and on-premises environments in the United States and Europe. The deployment illustrates that advanced users do not necessarily choose between cloud and private infrastructure; instead, they may combine them so that workloads can use the location and capacity model that best fits their data, performance, and compliance needs.

On-premises deployments are projected to expand at a CAGR of 17.59% from 2026 to 2031 as regulated users retain greater control over sensitive workloads and remain subject to jurisdictional obligations. Training data, inference pipelines, and audit records may need to remain in a defined location, even when cloud resources are available within the same broader region. Dedicated local infrastructure can also enable buyers to diversify their supply across NVIDIA, AMD, and Intel hardware while retaining control over data preparation, fine-tuning, and sensitive inference tasks. HPE added support for NVIDIA Mission Control software, Run: ai, and NVIDIA Dynamo within its AI Factory portfolio in 2026. The Hybrid cloud AI workload orchestration market is shaped by this paired model, where private control supports residency and governance needs while cloud elasticity addresses peak compute requirements in the same operating estate.

Complete Report Scope:

• By Component
  • Software
  • Services
• By Deployment Environment
  • Cloud
  • On Premise
• By Application
  • GPU Scheduling and Allocation
  • Workload Placement and Migration
  • Cluster Management and Autoscaling
  • Governance, Multi-Tenancy, and Policy Enforcement
  • Monitoring, Observability, and Cost Optimization
• By End User
  • Cloud Service Providers and GPU-as-a-Service Providers
  • IT and Technology Companies
  • BFSI
  • Healthcare and Life Sciences
  • Manufacturing and Automotive
  • Government, Defense, and Research
  • Retail, Consumer, and Media
• By Organization Size
  • Large Enterprises
  • Small and Medium Enterprises
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Southeast Asia
    • Rest of Asia-Pacific
  • Middle East
    • Saudi Arabia
    • United Arab Emirates
    • Turkey
    • Rest of Middle East
  • Africa
    • South Africa
    • Nigeria
    • Rest of Africa

Geography Analysis

North America held 38.92% of revenue in 2025, giving the region the leading position in the Hybrid cloud AI workload orchestration market. The region combines hyperscalers, purpose-built GPU cloud providers, research capacity, and enterprise AI programs that are already moving beyond pilot projects into active production. The United States drives most of the demand through large managed GPU fleets operated by AWS, Microsoft Azure, and Google Cloud, as well as specialist infrastructure providers serving intensive AI workloads. NVIDIA invested USD 2 billion in CoreWeave in January 2026, and the companies planned an AI factory capacity exceeding 5 gigawatts by 2030. Canada and Mexico add demand from financial services and manufacturing users that can use cross-border hybrid architectures anchored in the United States hyperscaler infrastructure.

Asia-Pacific is projected to expand at a CAGR of 18.23% from 2026 to 2031, making it the fastest-expanding regional part of the Hybrid cloud AI workload orchestration market. Government-backed sovereign AI programs, expanding enterprise adoption, and national supercomputing investments are driving demand across China, Japan, India, and South Korea. National data residency policies make hybrid control layers relevant, as public cloud capacity may need to operate alongside domestic infrastructure that retains sensitive workloads. Japan's AI Strategy and IndiaAI Mission direct public funding toward domestic AI infrastructure operating under national data-residency frameworks. Southeast Asia and Australia add demand from financial services, healthcare, and government organizations that manage local data requirements and seek capacity for both commercial and research workloads.

Europe represents a significant share of the Hybrid cloud AI workload orchestration market, led by Germany, France, and the United Kingdom. Pharmaceutical, automotive, and financial services organizations in these countries require controlled AI environments that meet both EU-wide and national data rules. The EU AI Act, General Data Protection Regulation, EU Data Act, and Digital Operational Resilience Act shape procurement toward systems with governance, auditability, and data-residency features. The Middle East is gaining momentum through sovereign AI programs in Saudi Arabia and the United Arab Emirates, while South America and Africa remain early-stage regions with demand centered on Brazil, Colombia, South Africa, and Nigeria.

1. NVIDIA Corporation
2. Amazon Web Services, Inc.
3. Microsoft Corporation
4. Google LLC
5. International Business Machines Corporation
6. Hewlett Packard Enterprise Company
7. Red Hat, Inc.
8. Broadcom Inc.
9. VMware LLC
10. Nutanix, Inc.
11. CloudBolt Software, Inc.
12. Morpheus Data LLC
13. Flexera Software LLC
14. Scalr Inc.
15. Mirantis, Inc.
16. Platform9 Systems, Inc.
17. Anyscale, Inc.
18. CoreWeave, Inc.
19. Rafay Systems, Inc.
20. Hopsworks AB
21. ClearML Ltd.
22. Domino Data Lab, Inc.
23. Lambda Labs, Inc.
24. RunPod, Inc.
25. Vast.ai, Inc.
26. Modal Labs, Inc.
27. Nscale Limited
28. Vultr Holdings Corporation

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Accelerating LLM Training and Inference Deployment
  • 4.2.2 GPU Utilization and Compute Cost Optimization Imperative
  • 4.2.3 Hybrid Data Residency and Sovereign AI Requirements
  • 4.2.4 Distributed AI Scheduling Across Heterogeneous Accelerators
  • 4.2.5 Agentic AIOps and Policy-Driven Automation Adoption
  • 4.2.6 Energy-Aware Scheduling and Sustainable AI Infrastructure
• 4.3 Market Restraints
  • 4.3.1 Heterogeneous Hardware and Cloud API Interoperability Gaps
  • 4.3.2 Shortage of AI Infrastructure Orchestration Specialists
  • 4.3.3 Multi-Tenant GPU Security and Isolation Complexity
  • 4.3.4 Migration Friction Across Hybrid Control Planes
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Intensity of Competitive Rivalry
  • 4.8.2 Threat of New Entrants
  • 4.8.3 Bargaining Power of Suppliers
  • 4.8.4 Bargaining Power of Buyers
  • 4.8.5 Threat of Substitutes

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Deployment Environment
  • 5.2.1 Cloud
  • 5.2.2 On Premise
• 5.3 By Application
  • 5.3.1 GPU Scheduling and Allocation
  • 5.3.2 Workload Placement and Migration
  • 5.3.3 Cluster Management and Autoscaling
  • 5.3.4 Governance, Multi-Tenancy, and Policy Enforcement
  • 5.3.5 Monitoring, Observability, and Cost Optimization
• 5.4 By End User
  • 5.4.1 Cloud Service Providers and GPU-as-a-Service Providers
  • 5.4.2 IT and Technology Companies
  • 5.4.3 BFSI
  • 5.4.4 Healthcare and Life Sciences
  • 5.4.5 Manufacturing and Automotive
  • 5.4.6 Government, Defense, and Research
  • 5.4.7 Retail, Consumer, and Media
• 5.5 By Organization Size
  • 5.5.1 Large Enterprises
  • 5.5.2 Small and Medium Enterprises
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 South America
    • 5.6.2.1 Brazil
    • 5.6.2.2 Argentina
    • 5.6.2.3 Colombia
    • 5.6.2.4 Rest of South America
  • 5.6.3 Europe
    • 5.6.3.1 Germany
    • 5.6.3.2 United Kingdom
    • 5.6.3.3 France
    • 5.6.3.4 Italy
    • 5.6.3.5 Rest of Europe
  • 5.6.4 Asia-Pacific
    • 5.6.4.1 China
    • 5.6.4.2 Japan
    • 5.6.4.3 India
    • 5.6.4.4 South Korea
    • 5.6.4.5 Australia
    • 5.6.4.6 Southeast Asia
    • 5.6.4.7 Rest of Asia-Pacific
  • 5.6.5 Middle East
    • 5.6.5.1 Saudi Arabia
    • 5.6.5.2 United Arab Emirates
    • 5.6.5.3 Turkey
    • 5.6.5.4 Rest of Middle East
  • 5.6.6 Africa
    • 5.6.6.1 South Africa
    • 5.6.6.2 Nigeria
    • 5.6.6.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 Amazon Web Services, Inc.
  • 6.4.3 Microsoft Corporation
  • 6.4.4 Google LLC
  • 6.4.5 International Business Machines Corporation
  • 6.4.6 Hewlett Packard Enterprise Company
  • 6.4.7 Red Hat, Inc.
  • 6.4.8 Broadcom Inc.
  • 6.4.9 VMware LLC
  • 6.4.10 Nutanix, Inc.
  • 6.4.11 CloudBolt Software, Inc.
  • 6.4.12 Morpheus Data LLC
  • 6.4.13 Flexera Software LLC
  • 6.4.14 Scalr Inc.
  • 6.4.15 Mirantis, Inc.
  • 6.4.16 Platform9 Systems, Inc.
  • 6.4.17 Anyscale, Inc.
  • 6.4.18 CoreWeave, Inc.
  • 6.4.19 Rafay Systems, Inc.
  • 6.4.20 Hopsworks AB
  • 6.4.21 ClearML Ltd.
  • 6.4.22 Domino Data Lab, Inc.
  • 6.4.23 Lambda Labs, Inc.
  • 6.4.24 RunPod, Inc.
  • 6.4.25 Vast.ai, Inc.
  • 6.4.26 Modal Labs, Inc.
  • 6.4.27 Nscale Limited
  • 6.4.28 Vultr Holdings Corporation

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment