AI Workload

An AI workload is a defined piece of AI work that runs on computing infrastructure. Examples include training a model, fine-tuning it on company data, serving a chat assistant to employees, scoring transactions for fraud, processing a backlog of documents overnight and running an AI agent that completes multi-step tasks. Each workload has its own characteristics: how much compute it needs, whether it must respond instantly, how steady or spiky its demand is, how much data it moves and how sensitive that data is.

Thinking in terms of workloads is how organizations make sense of AI costs and infrastructure choices. Different workloads suit different models, hardware, pricing options and providers, and a choice that is economical for one can be wasteful for another. For IT financial management, classifying and measuring AI workloads is the foundation for allocating AI costs, choosing the right sourcing options and finding savings, in the same way that workloads are the basic unit of cloud cost management.

Definition of AI Workload

An AI workload is an application, job or process that consumes AI compute to produce a result. It includes the model, the infrastructure it runs on, the data it uses and the pattern of demand it serves.

Workloads are usually identified by purpose and owner, such as a customer support assistant run by the service team or a monthly forecasting model run by finance, so that their costs can be tracked and attributed. A single application may contain several workloads, such as a real-time chat feature and a nightly summarization job, which are best measured separately.

Types of AI Workload

Training and fine-tuning workloads create or adapt models. They need large amounts of compute for limited periods and can often tolerate scheduling and interruption.

Real-time inference workloads answer requests as they arrive and must be fast and available. Batch inference workloads process large volumes on a schedule and can wait.

Agentic workloads combine many model calls, tool uses and steps to complete tasks. Their costs vary widely from task to task and can be hard to predict. They often need limits on steps and spending to prevent runaway costs.

Workload Characteristics

Key characteristics include compute intensity, latency requirements, demand pattern, data volume and location, and sensitivity of the data processed.

Steady, high-volume workloads suit reserved capacity or self-hosting. Variable or low-volume workloads suit APIs or serverless services. Interruptible workloads can use spot capacity.

Workloads involving regulated or confidential data may need specific providers, regions or deployment models, which can affect cost. These constraints should be recorded with the workload so that placement decisions take them into account.

Importance of AI Workloads in IT Financial Management

AI spending only becomes manageable when it is broken down by workload. A single total for AI tells leaders little, while workload-level costs show which uses are expensive, which are growing and which deliver value.

Workload analysis also guides sourcing. Matching each workload to the right model, pricing option and infrastructure is one of the most effective ways to reduce AI costs.

As AI use spreads, the number of workloads grows quickly, and without a consistent way to classify them, costs become hard to explain. A common taxonomy of workload types makes reports comparable across teams and over time.

Allocation and Accountability

Attributing costs to workloads, and through them to owners, makes teams accountable for the AI they run. This is the basis for AI cost allocation through showback or chargeback.

Workload-level unit costs, such as cost per request or per task, allow comparison over time and against the value each workload produces.

Shared infrastructure costs, such as a GPU cluster serving many workloads, need allocation rules based on usage, similar to other shared cloud costs.

Placement Decisions

Workload characteristics determine where each workload should run: through an API, on rented GPUs, on owned hardware or on users' devices.

Placement should be revisited as workloads grow and change. A workload that started as a small API-based pilot may justify dedicated capacity once it reaches steady, high volume.

Consistent placement criteria help avoid decisions driven by habit or by which team built the workload first. They also make it easier to explain to leadership why different workloads run in different places.