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ABI Research’s Cloud Research Service guides providers and implementers across hybrid, sovereign, and neocloud domains, covering infrastructure trends, regulatory needs, key drivers, and real-world use cases.

Cloud Coverage Areas

ABI Research’s Cloud Research Service provides in-depth advisory for technology providers and implementers navigating the rapidly evolving cloud ecosystem. It spans hybrid, sovereign, and neocloud (Graphics Processing Unit (GPU)-based) environments, while also examining the underlying data center infrastructure that powers them. The Cloud Research Service explores demand drivers, regulatory pressures, architectural evolution, and emerging technologies - including elastic compute and quantum computing - that are reshaping cloud strategies.

In addition to tracking vendor positioning and ecosystem shifts, the service delivers real-world use cases and implementation insights to inform both supply- and demand-side decision-making. This strategic guidance equips stakeholders to align with next-generation cloud opportunities and infrastructure transformation.

• Hybrid, sovereign, and neocloud (GPU cloud) architecture trends
• Data residency and compliance considerations in cloud architecture
• Quantum computing in the cloud: use cases and roadmap
• Hardware acceleration, virtualization, and disaggregated infrastructure trends
• Deployment models: multi-cloud, hybrid cloud, and cloud-adjacent architectures
• Cloud adoption drivers by vertical and region
• Competitive assessments: hyperscalers, sovereign cloud providers, and neocloud vendors
• Partner ecosystems: integrators, telcos, and specialist infrastructure vendors
• Sovereign cloud requirements and regional policy impacts
• Emerging regulatory models for cross-border and federated cloud services
• Neocloud platforms and GPU-as-a-Service market evolution
• Elastic compute adoption and infrastructure impacts
• Workload optimization and distribution strategies across cloud types
• Cloud use case taxonomy: Artificial Intelligence (AI), High-Performance Computing (HPC), analytics, and industry-specific workloads
• Strategic positioning of data center operators and cloud service providers
• Cloud Go-to-Market (GTM) strategies: vertical targeting, service packaging, and regional plays

Cloud Research Powers Technology Innovation & Implementation

For Innovators

• Identify emerging cloud models such as sovereign, neocloud (GPU-based), and elastic compute to inform product and service innovation.
• Support infrastructure planning through consultancy on data center design, workload distribution, and scalability strategies.
• Highlight differentiation opportunities across compute, storage, networking, and orchestration layers.
• Inform technology roadmaps with analysis on quantum computing readiness, hardware acceleration, and next-gen virtualization.
• Monitor regulatory and compliance trends to help build solutions aligned with data sovereignty and regional cloud policies.
• Analyze competitive positioning across hyperscalers, neocloud providers, and infrastructure vendors to guide strategic decisions.
• Surface high-impact use cases and vertical-specific adoption drivers to align technology development with customer needs.
• Provide actionable insights into real-world implementations to accelerate product development and feature prioritization.
• Connect cloud trends to adjacent innovations in AI, connectivity, and distributed computing for integrated solution strategies.
• Enable GTM strategy development by mapping buyer needs, deployment preferences, and regional demand signals.

For Implementers

• Assess cloud deployment models to match business needs with public, private, sovereign, and neocloud options.
• Evaluate infrastructure readiness for adopting elastic and GPU-based compute, including integration with existing systems.
• Understand regulatory and compliance impacts to ensure secure and compliant cloud strategies across regions and industries.
• Explore real-world use cases to benchmark adoption patterns, success factors, and technology combinations across verticals.
• Benchmark vendor capabilities to support partner selection, contract negotiation, and risk mitigation.
• Map workload distribution strategies across data centers and cloud environments for optimal performance and cost efficiency.
• Gain insights into emerging technologies such as quantum computing and next-gen virtualization to prepare for future scalability.
• Align cloud initiatives with business outcomes by connecting technology choices to Key Performance Indicators (KPIs) like agility, cost savings, and innovation potential.
• Navigate organizational change and adoption barriers through analysis of implementation best practices and stakeholder alignment.

Our Cloud Research Helps Solve Real Business Challenges For Key Stakeholders

Chipset Manufacturers

• What challenges can we help you solve?
  • Evaluate future demand for compute acceleration (GPUs, Field Programmable Gate Arrays (FPGAs), quantum, etc.)
  • Identify key workloads driving cloud-based chip adoption
  • Help align silicon innovation to cloud infrastructure needs and use cases
• What key questions can we help you answer?
  • Which workloads and industries are driving demand for compute acceleration?
  • How should chip vendors position for elastic, AI-native, and quantum cloud infrastructure?
  • What are the implications for data center design and scaling?
  • How does AI inference/training shape processor architecture and performance requirements?
  • What role do chip vendors play in enabling efficient AI infrastructure across clouds?

Cloud Data Solution Providers

• What challenges can we help you solve?
  • Surface vertical- and workload-specific cloud use cases
  • Provide clarity on regulatory and data sovereignty trends
  • Inform GTM strategy through buyer needs, compliance drivers, and cloud adoption barriers
  • Profile positioning across hybrid, sovereign, and neocloud ecosystems
• What key questions can we help you answer?
  • How are data sovereignty and compliance shaping cloud architecture?
  • What are the most impactful cloud use cases across verticals?
  • How should solution providers align GTM with cloud maturity and buying behaviors?
  • How does AI-driven data growth affect data management, integration, and governance?
  • What opportunities exist for enabling AI/ML pipelines across hybrid and sovereign clouds?

Hyperscalers

• What challenges can we help you solve?
  • Identify emerging cloud models and align infrastructure investments
  • Support development of hybrid, sovereign, and elastic compute offerings
  • Inform data center and workload distribution strategies
  • Guide GTM strategies for vertical-specific adoption
• What key questions can we help you answer?
  • What is the future of hybrid, sovereign, and neocloud architectures?
  • Where are the TAM and growth opportunities by region and vertical?
  • How are AI, elastic, and quantum compute reshaping cloud demand?
  • What cloud strategies are required to support Generative Artificial Intelligence (Gen AI) workloads at scale?
  • How should hyperscalers balance AI infrastructure with traditional cloud services?

Neocloud/GPU-as-a-Service Providers

• What challenges can we help you solve?
  • Highlight differentiators in GPU-based cloud and elastic compute platforms
  • Identify high-value use cases (AI/Machine Learning (ML), HPC, rendering)
  • Support GTM efforts by connecting compute performance to business outcomes
  • Benchmark against hyperscalers and niche rivals
• What key questions can we help you answer?
  • Which verticals demand GPU/cloud-native acceleration?
  • How can providers differentiate from hyperscalers in AI-focused cloud services?
  • What GTM and deployment models resonate across industries and regions?
  • How does Gen AI influence pricing, resource allocation, and capacity planning?
  • Where are the opportunities to host or fine-tune proprietary models for enterprise clients?