Render Scaling

This skill covers how to scale Web Services, Private Services, and Background Workers on Render: manual instance counts, autoscaling with a Pro workspace or higher, compute plan choices, and platform limits. Deeper guidance lives under references/.

When a service has or might need an attached disk, read references/persistent-disks.md before choosing a scaling architecture.

When to Use

• Setting or changing instance count (Dashboard, CLI, API, or Blueprint)
• Configuring autoscaling (min/max, CPU and memory targets)
• Choosing vertical (plan) vs horizontal (more instances) scaling
• Understanding constraints (disks, static sites, cron/workflows, 100-instance cap)
• Cost implications of running more or larger instances
• Blueprint fields: numInstances, scaling, plan

Manual Scaling

• Set instance count from 1 to 100 via the Dashboard, CLI, or API.
• All instances share the same compute plan; you cannot mix plans on one service.
• Changes apply immediately: Render provisions new instances and deprovisions excess capacity as needed.

Autoscaling

Before recommending an autoscaling policy, establish the service type, workspace plan, whether the service is stateless or has a disk, the desired minimum and maximum instance counts, and whether CPU or memory is the limiting resource.

• Available only with a Pro workspace or higher.
• Configure minimum and maximum instances and targets for CPU and/or memory utilization (1–90% each).
• At least one metric must be enabled (CPU or memory). If both CPU and memory autoscaling toggles are off, autoscaling is disabled.
• If both manual instance settings and autoscaling are configured, autoscaling wins—manual count does not override the scaling policy in effect.

Autoscaling Formula

Render computes a candidate instance count from utilization vs target:

new_instances = ceil(current_instances * (current_utilization / target_utilization))

• When both CPU and memory targets are set, the platform uses the larger of the two new_instances values (the more conservative scale-out).

Scaling Constraints

Scale-Down Behavior

• Scale-up is immediate when utilization supports it.
• Scale-down waits a few minutes after conditions allow reduction (spike protection). This reduces flapping from brief load spikes.

Compute Plans

• In Blueprints, the compute plan is set with the plan field.
• See references/compute-plans.md for current terminology, canonical plan discovery, and guidance on scaling up versus out.

Cost Patterns

• Scaling changes the amount of billable compute by changing the number and size of running instances.
• Confirm current compute charges at Render pricing.
• Right-size by monitoring CPU and memory utilization (see render-monitor).

Sustained low CPU and memory across every instance indicates likely over-provisioning. Consult references/compute-plans.md, then test a smaller current compute plan, fewer manually scaled instances, or autoscaling that can scale down toward an appropriate utilization target (approximately 70% CPU is a common starting point for CPU-bound services). Change one dimension at a time when practical, and verify CPU, memory, latency, and errors under representative production traffic before keeping the change.

Blueprint Configuration

Manual instance count:

numInstances: 3

Autoscaling:

scaling:
  minInstances: 1
  maxInstances: 10
  targetCPUPercent: 70
  targetMemoryPercent: 80

Compute plan:

plan: 1c-2g

Do not rely on numInstances to cap autoscaling when a scaling block is present—autoscaling takes precedence. Preview behavior for scaling is detailed in references/autoscaling-guide.md.

References

Related Skills

• render-web-services — Web Service settings, disks, deploy lifecycle
• render-background-workers — Worker-specific configuration and scaling context
• render-blueprints — Full Blueprint schema and field reference
• render-monitor — Metrics, logs, and utilization for right-sizing

Current documentation retrieval

Whenever this skill directs you to consult current Render documentation:

1. Retrieve the linked Markdown document directly with an available URL-fetching tool or HTTP client, such as curl. Do not substitute web-search summaries for the document.
2. Confirm that retrieval succeeded and returned the expected document, then read its contents. Saving a file or printing its path is not sufficient.
3. If the request fails or your tool cannot read the Markdown response, open and read the linked HTML version instead.
4. If neither version can be retrieved, disclose that the current reference is unavailable and follow any topic-specific fallback in the skill. Use bundled guidance only for stable constraints, and do not guess at changeable platform details.

When a task requires multiple references, apply this workflow to each one and distinguish the documents you verified from those that remain unavailable.