Who should enter the ‘Best AI Infrastructure Solution‘ category?
The ‘Best AI Infrastructure Solution’ category recognizes outstanding technologies providing the foundations organizations need to build, train, test, deploy, operate, scale, secure or govern artificial intelligence systems.
Nominees may span physical infrastructure, cloud and software-defined infrastructure, or specialist platforms that provide essential capabilities within the AI technology stack.
Eligible technologies may support generative AI, traditional machine learning, foundation models, AI agents, multi-agent systems or other advanced AI workloads.
Solutions may also operate at different points in the AI lifecycle, from preparing infrastructure for model development and training through to testing, deployment, inference and ongoing production management.
Entrants should focus their submission on how their solution performs a meaningful infrastructure function supporting the development or operation of AI.
Example Use Cases
Applications of AI assistants include, but are not limited to, the following example areas:
Modern AI workloads can place exceptional demands on compute resources. Infrastructure designed specifically for AI can increase the speed and efficiency with which organizations train models, perform inference and execute demanding workloads, while improving utilization of expensive computing resources.
Typical use cases:
- Accelerating model training and inference workloads
- Provisioning and managing GPU or specialist AI compute resources
- Distributing AI workloads across clusters or computing environments
- Optimizing compute allocation according to workload requirements
Demonstrable outcomes include:
- Reduced model training or inference times
- Increased compute or accelerator utilization
- Lower infrastructure cost per AI workload
Moving models into production requires infrastructure capable of executing them reliably, efficiently and at the scale demanded by real applications. Model serving and runtime technologies can manage how AI workloads execute, allocate resources and respond as demand or operating conditions change.
Typical use cases:
- Serving foundation models and machine learning models in production
- Scaling inference capacity according to changing demand
- Optimizing execution for different models, hardware or workload characteristics
- Managing multiple model versions or runtime environments
Example measurable outcomes include:
- Reduced inference latency
- Increased request throughput or workload capacity
- Improved availability or production reliability
Organizations need to deploy AI workloads across increasingly complicated infrastructure while controlling capacity, dependencies and resources. Orchestration technologies can determine where workloads run, allocate the resources they require and maintain efficient operation as environments change.
Typical use cases:
- Deploying AI workloads across cloud, on-premises or hybrid infrastructure
- Scheduling workloads according to compute and resource availability
- Managing dependencies across distributed AI environments
- Dynamically allocating resources according to workload demand
Example measurable outcomes include:
- Reduced time required to deploy AI workloads
- Improved infrastructure utilization
- Increased number or scale of workloads supported
AI systems increasingly interact with sensitive information, enterprise applications and external tools. Infrastructure-level security and identity technologies can establish which models, agents and users are permitted to access particular resources, while helping organizations enforce controls across complex AI environments.
Typical use cases:
- Managing identities and permissions for models and AI agents
- Controlling AI access to applications, APIs, tools and sensitive data
- Isolating AI workloads and execution environments
- Enforcing security policies across AI infrastructure
Measurable outcomes may include:
- Reduction in unauthorized or excessive access
- Increased coverage of AI workloads by consistent security controls
- Faster implementation and management of AI access policies
Agentic AI introduces infrastructure requirements beyond conventional model serving. Agents may need to maintain state, interact with tools, execute extended tasks, coordinate with other agents and operate securely across enterprise systems. Specialist infrastructure can provide the execution and control layer required to support these behaviors at production scale.
Typical use cases:
- Providing runtime environments for persistent or long-running AI agents
- Managing agent access to tools, APIs and enterprise applications
- Coordinating communication and task allocation between specialist agents
- Maintaining state, context or memory across agent execution
Example measurable outcomes include:
- Increased number of concurrent agents or agent tasks supported
- Improved reliability of multi-step agent execution
- Reduced infrastructure complexity associated with deploying agentic systems
Areas to Highlight in Your Submission
Judges score nominations across these five key areas:
Although not formally scored, focus on these areas specific to this category, can help your nomination stand out:
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Next Steps to Enter The A.I. Awards
To enter this AI Awards category, or any other category in The AI Awards, please follow these three simple steps:
The A.I. Awards is a program from The Cloud Awards. Since 2011, we’ve been helping organizations across the globe gain the recognition they deserve for market-leading innovation in the cloud computing and software sectors.
For a detailed breakdown of all the benefits you receive as an awards entrant as either a shortlistee, finalist or ultimate winner, please see our “Why Enter?” page. The many benefits are replicated across all international awards programs. If you have any questions about this category, please contact us.