The Forrester Wave™: AI Infrastructure Solutions, Q4 2025

AI Infrastructure Solutions Vendor Product Comparisons 2025

Key features and pros and cons of top AI Infrastructure Solutions products:

Alibaba Cloud - Leaders

Pros:

Low-latency compute close to users in Asia Pacific
Configuration capabilities
Development support
Management and efficiency
Fault tolerance
Simple, upfront pricing less complex than competitors

Cons:

Limited ability to provide insights into trending and forecasting on operational KPIs and metrics
Lacks proprietary silicon
Limited relevance outside Asia Pacific
Less scalability than hyperscale peers

Amazon Web Services - Leaders

Pros:

Proprietary silicon (Inferentia and Trainium)
Global availability
Lower inference costs at scale
Data management
Development
Management
Fault tolerance
Security

Cons:

Custom silicon lags competitors in large-model training
Workload portability across accelerators remains complex
Pricing complexity makes capacity planning difficult

Google - Leaders

Pros:

Proprietary accelerators with fabric-level integration
Efficient inference scaling
Optionality between TPUs and GPUs
Data management
Development
Operations management
Fault tolerance
Security

Cons:

Ecosystem less built out than other evaluated Leaders
Long learning curve
Pricing clarity challenges
Lock-in concerns due to vertical integration

Microsoft - Leaders

Pros:

Investment in GPUs
Global reach
Rapid adoption of NVIDIA's newest hardware
Development
Fault tolerance
Efficiency
Broad regional availability
Tight integration with enterprise cloud services

Cons:

Model provider ecosystem could be stronger
Higher costs noted by customers

NVIDIA - Leaders

Pros:

Silicon innovation
Advanced interconnects
Vertically integrated systems
Best-in-class hardware performance and fabric integration
Concurrency features such as Multi-Instance GPU (MIG)
System-level integration that reduces latency for inference

Cons:

High cost
High power requirements
Energy consumption

Cerebras Systems - Strong Performers

Pros:

Low-latency inference for large models
Strong concurrency
Distinctive wafer-scale processor design
Eliminates interconnect bottlenecks

Cons:

Limited developer adoption
Ecosystem lock-in
Higher operational demands compared with conventional GPU deployments
Software ecosystem lacks maturity
Compatibility with common AI workflows can be challenging

Dell Technologies - Strong Performers

Pros:

Supply chain depth
Global service infrastructure
Validated reference architectures
Enterprise-grade support
Reliability
Consistent support

Cons:

Weaknesses in training
Weaknesses in inferencing
Weaknesses in development
Use of standard components limits differentiation

Hewlett Packard Enterprise - Strong Performers

Pros:

Validated AI system designs
Service-backed deployment
GreenLake consumption models
Systems engineering
Data center integration
Lifecycle services

Cons:

Weaknesses in inferencing
Weaknesses in development
Weaknesses in ecosystem
Breadth of certified AI software support
GreenLake AI service pricing and tiers are confusing

IBM - Strong Performers

Pros:

Flexibility in platform governance
Hybrid deployment support
Integration with watsonx and OpenShift for management
System reliability
Governance focus

Cons:

Limited differentiation in inferencing
Limited differentiation in security
Complexity of implementation
Opaque, expensive licensing
Depth of ISV and certification model lacking relative to peers
No obvious plans to bring parity across on-premises and public cloud AI infrastructure services

Graphcore - Contenders

Pros:

Processor specialization
Concurrency support
Architectural differentiation
Security via isolated on-chip memory

Cons:

Limited availability
Configuration limitations
Training limitations
Fault tolerance issues
Smaller ecosystem maturity
Fewer integrations in mainstream AI workflows
Efficiency challenges
Deployment options limited
Overall scalability versus scaling for smaller tasks
Small partner ecosystem
Limited AI frameworks support
Lack of pricing transparency and flexibility
Lack of supporting services

Lenovo - Contenders

Pros:

Energy-efficient system design
Validated reference architectures
Global enterprise support
Reliable and cost-effective systems

Cons:

Lack of unique performance features or proprietary acceleration technologies
Weaknesses in data management
Weaknesses in inferencing
Weaknesses in training
Weaknesses in development
Weaknesses in operations management
Weaknesses in security
Lack of differentiation compared to competitors
Depth of software ecosystem lacking
Fewer ISV partnerships
Limited focus on model certifications

Oracle - Contenders

Pros:

Direct hardware access via bare-metal
RDMA networking that reduces latency
Outstanding scalability
Predictable performance for enterprise workloads
Cost predictability
Low-latency performance

Cons:

Weakness in configuration
Fewer validated ISV or model certifications than larger cloud providers
Support responsiveness concerns
Complexity and inconsistency of UX
Little original hardware innovation
Global scale still a work in progress

Supermicro - Contenders

Pros:

Fast platform availability
High-density designs
Competitive cost structures
Scalability
Customizable GPU server options
Competitive pricing
Early availability of new GPU systems

Cons:

Fails to stand out in most criteria
Fewer enterprise certifications than peers
Less emphasis on lifecycle services
Service and support depth can lag larger OEMs
Lacks subscription model

FAQs

Q: Which vendors are Leaders in 2025?

A: Alibaba Cloud, Amazon Web Services, Google, Microsoft, NVIDIA

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders in the Forrester Wave for AI Infrastructure Solutions demonstrate the strongest current offerings and strategic vision in the market. They excel across multiple criteria including architecture, ecosystem support, workload optimization (training, inference, development), management capabilities, fault tolerance, efficiency, security, and scalability. Leaders typically offer either proprietary silicon innovation or comprehensive NVIDIA GPU implementations with strong global reach, mature partner ecosystems, and tight integration with enterprise services. They show clear roadmaps for continued investment in AI infrastructure and provide customers with flexible deployment options, whether through public cloud platforms or integrated hardware/software solutions.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for AI Infrastructure Solutions in 2025

AI Infrastructure Solutions Vendor Product Comparisons 2025

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FAQs

Q: Which vendors are Leaders in 2025?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

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