Plan your compute

Match workload requirements, operational control and cost to the right capacity.

A useful compute decision starts with the workload: what it requires, how long it runs, and how much control you need over its placement.

Consideration	What to establish
Hardware	CPU, RAM, disk, GPU model and VRAM requirements
Runtime	Startup time, model downloads, sustained load and idle periods
Location	Region preferences and dependencies close to the workload
Capacity	Instance bounds, available servers and shared-grid quotas
Operations	Which organization owns and pays for it; Developers operate apps, admins manage grids and servers
Cost	Serverless usage, dedicated-server charges and external infrastructure costs

Choose a starting point

Serverless is a direct path when you want the platform to place the workload onto compatible capacity.

Dedicated cloud servers give you explicit server capacity to organize into grids. Their lifecycle is separate from the apps using them.

Bring your own compute lets you use existing infrastructure. You remain responsible for the infrastructure's availability and its external costs.

Measure before expanding

Begin with realistic resource requirements. Inspect logs and usage once the app is running, then adjust the configuration using measured behavior. More instances or different hardware can change both capacity requirements and spend.

For shared grids, check quotas and allocation policy before increasing the app's requirements. A resource being visible does not necessarily mean you can schedule additional workloads onto it.

Plan the shutdown too

Stop applications that should no longer run. Separately review dedicated machines and outstanding server orders, and release capacity you no longer need. An organization admin can check Cost to understand remaining charges.

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