Choose compute platforms for AI agent jobs

Choosing compute for an AI agent starts with the work it performs. A short transformation, a GPU evaluation, and an interactive coding session have different requirements even when they belong to the same product.

Polyaxon can organize these workloads as jobs and services, with shared tracking and artifact practices. The decision is which execution model fits each step—not whether every component must use the same hosting model.

Classify the workload before the platform

A serverless provider can be useful for short, self-contained operations. Dedicated or reserved capacity can suit steady workloads. Neither label answers whether the environment supports your dependencies, data controls, or recovery requirements.

Avoid comparing platforms using only their smallest advertised execution price. The relevant unit is a successfully completed task under your constraints.

Use Polyaxon workload definitions as the contract

Define the image, command, inputs, resources, and outputs in a reviewed component. Use a job when the process should finish and a service when it must remain available.

For interactive execution, enable sandbox access on the service only when needed. The agent framework remains application code; the platform supplies the execution and operational context.

If part of the workflow calls an external compute provider, track that request and its result from a trusted Polyaxon controller or evaluation job. Do not imply that an arbitrary external runtime automatically inherits Polyaxon's access or artifact behavior.

Account for data movement and state

A lightweight function may become expensive or slow if it repeatedly downloads a large dataset. A long-lived service can avoid repeated preparation but consumes capacity while it remains running.

Place compute near approved data sources where possible, using configured connections. Evaluate residency and authorization separately from latency.

Keep durable task state outside a disposable process. Persist useful reports through the artifact workflow and define how a replacement execution reconstructs its inputs.

Compare capacity under realistic demand

Measure request arrival rate, useful execution time, waiting time, and concurrency. Include failed attempts and retries. A configuration that handles one request well may queue badly during a burst.

Commercial Polyaxon queues support routing, priorities, concurrency, and quotas. These controls help share capacity, but they do not create unlimited resources or guarantee instant startup.

Separate latency-sensitive services from large background evaluations when their contention harms the product. Confirm the improvement with workload measurements rather than assuming more pools always help.

Choose the smallest operational commitment that fits

List who owns patching, images, capacity planning, network controls, on-call response, and evidence retention for each option. Managed execution can reduce some responsibilities without eliminating application security or data governance.

A good architecture may combine multiple compute models. Polyaxon's value is keeping the ML and agent work reproducible and inspectable across those decisions, with a clear contract for every step.