Managing AI workloads at scale

Moving applications from pilot programs into full production is where operations get complicated. A financial services firm, for instance, often runs a fraud detection model scoring transactions in real time alongside a customer‑facing assistant. Each of these scenarios has different latency requirements, data residency needs and compliance controls.

Today, most enterprise organizations in production are managing many systems like this all at once. The challenges do not get easier as more systems are added; instead, they multiply. In an IBM Institute for Business Value (IBV) study, 77% of executives say that they need to adopt AI quickly to remain competitive. However, only 25% strongly agree that their organization’s IT infrastructure can support scaling AI across the enterprise.

Model training and AI inference come with different demands. Training requires massive compute running continuously across clusters of specialized processors. Inference, by contrast, runs in production, where latency and availability matter more than raw processing power.

According to McKinsey, inference workloads are projected to make up more than half of all AI compute by 2030. This shift means that running models efficiently is becoming as pressing a business concern as building them.¹

Given all of this complexity, managing AI workloads at scale is not just a technology problem. It is an operational one. The organizations that do this work well are the ones that establish best practices across infrastructure, governance and day‑to‑day operations.

Compared to traditional workloads, AI workloads typically process unstructured data (for example, images, text, audio) that introduces complexities standard IT infrastructure was not designed for.

Compute becomes the most strained resource. Training and inference require graphic processing units (GPUs) and Tensor Processing Units (TPUs), which are more expensive to provision and operate than standard compute. As a result, cost predictability and resource allocation become ongoing challenges.

Data center infrastructure adds another layer. Training AI workloads requires high-density compute clusters with advanced cooling, while inference workloads need low-latency access to data and applications. Both put pressure on older data center infrastructure. Energy constraints are also increasingly influencing scalability, making power availability an important concern.

Performance requirements also become more complex. Delays and bottlenecks cannot bog down production applications and inference workloads need to be positioned close to the data and applications they serve, such as edge settings.

Governance also brings complexity at the organizational and compliance levels. As systems multiply, maintaining consistent data lineage, audit trails and cybersecurity controls gets harder. Data residency and data sovereignty requirements that vary by region add extra challenges, particularly for organizations operating across borders.

Finally, operational expertise creates a skill gap for many enterprises. Running workloads in production requires expertise across data engineering, machine learning operations (MLOps) and infrastructure management that many organizations are still developing.

Managing multiple systems at different lifecycle stages also goes beyond traditional IT workflows. Many teams are not prepared for the complexity that follows.

The challenges associated with AI workloads are real, but they are manageable. The following operational practices, applied consistently, can make a significant difference in managing AI workloads at scale.

Not every AI workload has the same requirements, and treating them as if they do leads to wasted resources and performance problems. Matching compute, storage and networking to what each workload needs keeps costs predictable.

Workload schedulers help teams allocate compute efficiently and avoid over-purchasing GPU resources across production environments.

When every team deploys differently, troubleshooting becomes siloed.

Consistent AI data pipelines, shared monitoring tools and common operational processes reduce that friction as the number of systems grows. New teams get up to speed faster and quality is easier to maintain.

Compliance requirements are much easier to build in from the start than to add later.

Organizations that treat data residency, audit trails and access controls as day‑one requirements rather than an afterthought are the ones that rework less when regulations shift. For organizations working across regions, where requirements can differ significantly, this work is essential.

Running AI in production requires people who understand data engineering, model operations and infrastructure management. In most organizations, that combination is still being built.

Whether through hiring, training or external partnerships, closing that gap early keeps operations running smoothly and costs in check.

A model that performs well at launch will not necessarily perform well six months later. Data shifts, conditions change and accuracy drifts in ways that are not always obvious until a business outcome is affected.

Observability tools like Prometheus and IBM Turbonomic® are a critical part of any production deployment, tracking latency, throughput and model performance over time. Retraining schedules and routine maintenance reviews keep small problems from becoming larger ones.

One system is manageable. Ten systems across multiple environments, with different teams, vendors and compliance requirements, is a far more complex scenario.

The organizations that scale without constant crisis are the ones that put structure in place early. That means clear processes, defined ownership and the tooling to maintain visibility across all AI workloads running in production.

Meeting the challenges of managing scalable AI workloads requires specialized AI infrastructure. This infrastructure includes hardware, software, networking and storage resources that can support building, training and deploying large AI models, such as large language models (LLMs), across data centers and AI data centers. From model deployment through ongoing monitoring, each stage has distinct infrastructure needs.

Infrastructure components for managing AI workloads at scale include the following elements:

• Hybrid cloud
• Compute resources
• Microservices
• Containerization and Kubernetes
• Data pipelines and storage
• MLOps and GenAIOps

Today, organizations rely on hybrid multicloud environments that combine public cloud services from different vendors and stretch across multiple cloud providers’ infrastructures.

No single IT environment can handle every AI workload requirement. Public cloud platforms, hosted by providers like Amazon Web Services (AWS), Microsoft Azure, IBM Cloud® and Google Cloud, offer on-demand GPU capacity that suits large-scale training workloads in hyperscale settings. That said, inference workloads tied to sensitive customer data often need to stay on-premises or in a private cloud to meet regulatory requirements.

Hybrid cloud gives organizations the flexibility to make such placement decisions based on what each workload needs, while enabling cloud-native technologies to run consistently across environments.

AI workloads, particularly model training, require specialized compute resources, including GPUs (such as NVIDIA) and TPUs, rather than the central processing units (CPUs) that power traditional enterprise systems.

Frameworks like TensorFlow and PyTorch are built to run on this hardware and are commonly used to benchmark performance before moving workloads into production. Tracking GPU utilization and resource utilization across both training and inference cycles is essential for maintaining cost efficiency as deployments grow.

Many organizations are building or transforming AI applications to use microservices architecture, which breaks applications into smaller, loosely coupled components focused on specific business functions.

This approach makes AI workloads easier to develop, scale and update independently and is an important part of containerization for deployment across hybrid environments.

Understanding how to manage containerized AI workloads at scale starts with containers, which are the de facto compute units of modern cloud-native infrastructure. Containers package applications and their dependencies into portable units through a container runtime like Docker, which is an open source platform that runs consistently across environments.

The container orchestration platform Kubernetes can automate how those containers are deployed, scaled and managed across Kubernetes clusters, which consist of nodes such as physical or virtual machines. This approach ensures that AI workloads are distributed and resources are used efficiently through load balancing and autoscaling.

Each workload runs in its own pod, which Kubernetes schedules and monitors. An AI workload built and tested in one environment can move to another without being rebuilt when systems need to shift between on-premises and cloud.

Building and training models requires fast access to large datasets through reliable data pipelines. Systems communicate through application programming interfaces (APIs), making consistent data routing, access and integration across environments essential as the number of models grows.

AI inference workloads need low-latency connections to the data and AI applications that they serve in production. As the number of systems in an organization grows, so does the difficulty of managing data movement, maintaining quality and making sure that the right data reaches the right model. This technology is where AI storage comes in.

MLOps applies DevOps principles to the machine learning (ML) and AI lifecycle, including building, testing, deploying and monitoring models through CI/CD pipelines. This lifecycle includes model serving through ongoing production management.

Without machine learning operations (MLOPs), models drift, retraining falls behind and teams lose visibility into key performance metrics, such as latency and throughput.

GenAIOps extends MLOps to generative AI, where foundation models, RAG systems and AI agents introduce new operational patterns. A traditional chatbot that retrieves information is relatively straightforward to monitor. An agentic AI system, such as a virtual agent, can operate in a nonlinear way, choosing between actions and making adjustments across multiple systems and endpoints.

Managing scalable AI workloads delivers a range of benefits for businesses running complex AI systems:

• Centralized visibility and control: Observability across AI deployments and performance makes it easier to manage at scale and catch problems before they affect business. Portability across environments also means that teams are not locked into a single infrastructure approach as needs change.

• Operational efficiency: Consistent tooling and deployment practices across teams keep operations running reliably as the number of AI systems grows, ensuring core functionality remains stable as new capabilities are added.

• Cost control: Without proper orchestration, GPU resources get wasted or over-purchased. Managing resources supports cost-effectiveness as AI deployments grow.

• Speed to market: Teams with the right processes are able to work faster. What once took months can happen in weeks when the right AI infrastructure and processes are in place.

• Compliance and governance: As regulations evolve, organizations with AI workload management in place are better positioned to meet data residency, audit and transparency requirements.

• Innovation at scale: The right infrastructure and operational practices free teams to focus on innovation rather than troubleshooting problems.

Managing AI workloads at scale plays out differently across industries, yet core challenges including cost, compliance and reliability are in play throughout each sector.

Here are a few examples.