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Best practices for building scalable AI infrastructure
Scalability is vital for AI platforms. These 10 best practices can help businesses build scalable infrastructure that supports AI workloads and adapts to fluctuating demands.
Like all enterprise workloads, AI platforms depend on a comprehensive and resilient computing infrastructure. What sets AI platforms apart from traditional enterprise workloads, however, is the need for infrastructure scalability.
The computing, storage and organizational demands involved in data management and AI training are formidable, but not continuous. Allocating enormous computing resources upfront can reduce AI training from weeks to mere hours. However, once the AI platform is in production, the demand for extensive AI hardware, storage and orchestration frameworks diminishes and becomes sporadic. The infrastructure focus shifts instead to resilience, security and cost management.
This digital divide poses a dilemma: Scalability must ensure an IT infrastructure can scale up to deliver the resources needed to develop and train AI efficiently, scale back when deploying the AI to production and then support scalability cost-effectively as AI use changes over time.
Therefore, business leaders must evaluate the factors involved in scalable AI infrastructure and consider a range of practices to help ensure adequate scalability.
Scalable AI infrastructure components
AI infrastructure is serious business. A report from Fortune Business Insights valued the global AI infrastructure market size at $58.78 billion in 2025. It's projected to reach $75.40 billion in 2026 and approach a whopping global value of $497.98 billion by 2034.
Substantial AI infrastructure growth puts the need for scalability into sharp focus. A scalable AI infrastructure isn't a single concept or practice, but a highly integrated combination of technology components and management strategies.
Successful scalability for AI platforms typically includes the following aspects:
- Modular AI software design. Scalability starts with the workload, and AI platforms typically adopt a flexible, efficient and full-featured design, such as a container-based microservices architecture. A modular design enhances resource efficiency by scaling only the necessary components, and simplifies software design and testing because changes can happen much faster than with traditional monolithic software designs.
- Computing acceleration. Scalable AI infrastructure requires computing accelerators such as GPUs, tensor processing units (TPUs), neural processing units (NPUs) and other computing assets capable of supporting parallel processing. This is especially essential for large-scale model training.
- Data storage. AI training demands vast data warehouses or data lakes capable of supporting structured and unstructured data with high performance and low latency. Storage needs can be significant in production as data arrives from varied sources, including users, knowledge base searches and IoT devices. Storage might be centralized, available at edge locations or a combination of the two.
- Data security. The data used to train and operate AI can be extremely sensitive and contain personally identifiable information. Sensitive data demands strong data security technologies, including access control, encryption, logging and adherence to prevailing regulatory obligations, including data sovereignty.
- Network support. Powerful computing and vast storage demand network connectivity with high bandwidth and low latency. This helps minimize bottlenecks and optimize training and production AI tasks. Local network interconnections might use InfiniBand, and cloud resources might rely on specialized network connections.
- Management. Core management technologies, such as automation and orchestration, often drive infrastructure scalability . Organizations might adopt tools like Kubernetes or a machine learning operations (MLOps) strategy to streamline workflows used to develop, deploy and monitor AI platform components with minimal human intervention.
Challenges of scalable AI infrastructure
Because AI scalability demands nuance, several challenges can arise.
Computing costs. AI is notorious for its computing demands, and implementing infrastructure locally can be onerous. While public cloud computing has emerged as a preferred AI platform, never underestimate the potential dangers of cloud computing costs, particularly when unused cloud resources are ignored.
Data management. AI systems require high-quality data. Unfortunately, real data is rarely pristine, and it can take time and talent to curate and organize meaningful data. Designing a network with appropriate bandwidth and latency to optimize data flows is also challenging. In addition, data silos are commonplace, and establishing unified data structures can require complex design work.
Talent. It takes skilled humans to design, train, deploy and manage scalable AI infrastructure. Businesses must assess their talent pool and evaluate whether additional staff with AI experience are needed.
Best practices for scalable AI infrastructure
There's no single approach to an IT infrastructure -- components and strategies vary depending on the organization's industry, size and strategic goals.
Today's scalable AI infrastructure is best represented using a hybrid cloud, a containerized modular AI software architecture, high-performance computing components, strong data security and model management using an AI-focused paradigm such as MLOps.
The following are 10 best practices for scalable AI infrastructure:
1. Use a cloud-native approach
A public cloud can provide extensive high-performance computing (HPC) resources on demand using a pay-as-you-go approach. This approach is ideal for ingesting vast data sources and completing training and testing tasks cost-effectively. Public clouds offer elastic compute resources and highly automated scaling features, enabling businesses to adopt an aggressive go-to-market strategy for their AI platforms without the time or cost of building complex infrastructure in-house.
2. Use containerized AI applications
Containers have revolutionized software design, enabling complex workloads to be built from smaller, independent modules that can be invoked and connected as needed. This approach ensures resource efficiency and scalability. Well-established tools like Docker and Kubernetes are available -- including as public cloud services -- for containerizing and orchestrating modular AI software systems.
3. Use HPC services
AI systems are best served using compute instances that provide advanced HPC components. GPUs, TPUs and NPUs are required to support the rapid, large-scale training and low-latency inference requirements of AI systems. Instances that incorporate GPUs, TPUs and NPUs can be more expensive than traditional CPU-based instances, but each task completion is more time- and cost-efficient.
4. Architect infrastructure to the edge
From autonomous vehicles to humanoid robots, AI is increasingly decentralized, gathering, storing and processing data closer to where it's created. AI software design must accommodate a distributed computing environment, enabling edge deployments to process data in real time, reducing latency and easing the load on centralized infrastructure. An edge architecture profoundly affects how resources are provisioned and scaled.
5. Establish an efficient AI lifecycle
The AI lifecycle is an iterative and structured process used to develop and manage AI systems. This complex environment can be error-prone and difficult to manage manually, making automation and orchestration key to achieving consistent, successful outcomes. Approaches such as MLOps can play a central role in AI lifecycle management, ensuring adequate resource provisioning, reliable scalability, proper testing, consistent deployment and ongoing AI performance monitoring.
6. Focus on storage and data management
AI is nothing without high-quality data. AI systems must have effective data ingestion mechanisms, data storage and protection processes and data retention policies. This requires scalable data storage resources, along with a comprehensive data management platform to provide insight into how much data is present, how it's used, its relative quality and whether it's protected. Public cloud providers offer data storage, but third-party tools might be needed to implement comprehensive data management.
7. Establish strong security and compliance
Security is critical to protect the data used to train and operate AI systems and ensure that only authorized users have system access. Strong access controls, data encryption and other strategies all play a role in security. Security is also a pivotal element of any regulatory compliance strategy, ensuring that sensitive information is safeguarded in accordance with prevailing regulations. While security itself isn't about infrastructure scalability, scaling without security can introduce vulnerabilities that put data, the AI system and the business at risk.
8. Establish strong AI governance
AI governance includes ethical standards, AI transparency goals, data bias mitigation, safeguards for data use and guardrails for infrastructure use. All these factors relate to fair and responsible AI use, but they also translate into scalability. First, global regulations are increasingly directed toward AI fairness and transparency -- businesses might soon need to prove AI operational behaviors and data quality with measures that will stand up to regulatory review. Second, AI governance also includes careful examination and enforcement of infrastructure provisioning and scaling to balance performance and reliability with risk and cost.
9. Monitor accuracy and automate learning
Monitoring underpins AI lifecycle workflows. AI developers can see how models drift and outcomes degrade as data changes over time. Monitoring can trigger resource scaling as performance requirements change. It can also trigger AI retraining cycles that need new quality data -- both of which demand careful resource provisioning and scaling to achieve. Automation can make AI performance optimization and retraining seamless while maintaining established cost constraints.
10. Watch cloud costs closely
Correlating public cloud costs to specific workloads, users, groups or departments can be a challenge. Without specialized tools and cost-optimization practices, cost control and containment can be difficult. A competent cross-disciplinary FinOps team can help identify and assign cloud costs and offer cost-mitigation strategies that make AI infrastructure scalability more cost-effective. FinOps practices can also help identify underused resources and services, providing additional cost management opportunities for the business.
Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.
