Report:

A: This research evaluates 12 leading AI infrastructure providers across 19 criteria grouped into three categories: current offering (solution, workloads, tools, deployment), strategy (vision, innovation, roadmap, partner ecosystem, pricing transparency, supporting services), and market presence (revenue and customer base). The evaluation covers solutions designed to support AI workloads including data preparation, model training, and inferencing across cloud, on-premises, and hybrid deployment models.

A: Enterprise technology professionals should use this research to evaluate and select AI infrastructure providers that align with their specific workload requirements, deployment preferences (cloud, on-premises, hybrid), scale needs, and overall infrastructure strategy. The report helps buyers understand vendor strengths and weaknesses, compare offerings, and make informed decisions about single or multi-vendor approaches based on their AI maturity and use case requirements.

A: Vendors included in this evaluation must offer: 1) AI infrastructure as defined by Forrester - specialized compute, storage, and network designed for AI workloads; 2) Comprehensive and differentiated AI infrastructure specifically designed for running AI workloads, offered as hardware and/or cloud services; 3) Active market participation by marketing and competing as AI infrastructure vendors; 4) Strong market presence with significant client interest demonstrated through inquiries, advisories, and event interactions.

A:

• Does not offer AI infrastructure as defined by Forrester (specialized compute, storage, network for AI workloads)
• Solution is not specifically designed for AI workloads - general-purpose infrastructure without AI optimizations
• Not marketed or positioned as AI infrastructure in the market
• Insufficient market participation - not actively competing with other AI infrastructure vendors
• Lack of market presence or client interest - minimal inquiries, advisories, or engagement from Forrester clients
• Offers only AI/ML platforms for building applications rather than infrastructure for running AI workloads
• Does not support the three core AI workloads: data preparation, model training, and model inferencing

A:

• Maximize the performance of core AI workloads - ensure the solution covers data preparation, model training, and inferencing with appropriate infrastructure requirements for throughput, latency, fault-tolerance, and cost
• Evaluate management layer capabilities - assess how AI infrastructure management software integrates with existing infrastructure management tools, policies, and ITOps practices
• Match enterprise's commitment to AI - consider model size requirements, edge deployment needs, HPC integration, and overall AI strategy context
• Inventory current and future AI workloads - understand what AI workloads are currently in use and anticipate future requirements
• Consider multi-vendor strategies - evaluate whether choosing multiple vendors for different workloads (e.g., on-premises for training, cloud for inferencing) makes sense
• Assess deployment flexibility - determine whether cloud-only, on-premises-only, or hybrid deployment aligns with enterprise needs
• Evaluate cost optimization features - consider pricing models, resource allocation capabilities, and efficiency optimizations

A:

• Workload multiplicity matters most - three core AI workloads (data prep, training, inferencing) have starkly different infrastructure requirements
• Cost optimization is critical - enterprises need management layers to optimize cost and tame complexity
• Hybrid deployment strategies - not just cloud vs on-premises, but alignment with overall infrastructure strategy
• Rise of generative AI alongside predictive AI - different data processing needs for structured vs unstructured data
• GPU dominance with emerging alternatives - deep learning requires GPUs or other AI-designed chip architectures
• Multi-vendor strategies becoming common - enterprises choosing different vendors for data management, training, and inferencing based on specific needs

A: Current Offering evaluates each vendor's position on the vertical axis of the Forrester Wave graphic and indicates the strength of its current offering through solution capabilities, workload support, tools, and deployment options. Strategy evaluates placement on the horizontal axis and indicates the strength of vendors' strategies through their vision, innovation, roadmap, partner ecosystem, pricing transparency, and supporting services and offerings. Market presence is represented by marker size and reflects revenue and number of customers.