About render-scaling
A skill for operators managing Render services, workers, and scaling decisions. It covers manual and automatic instance scaling, compute plans, platform constraints, and cost considerations.
Key features
- 01Set instance counts from 1 to 100
- 02Configure CPU and memory autoscaling
- 03Choose vertical or horizontal scaling
- 04Check disk and service-type constraints
- 05Review compute plans and cost patterns
- 06Configure scaling in Blueprints
Use cases
- 01Adjust capacity for a Render service
- 02Set autoscaling limits and utilization targets
- 03Assess whether disk-attached services can scale out
- 04Right-size instances using CPU and memory metrics
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ render-oss/ skills --skill render-scalingAfter installing, type/render-scalingin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
references/autoscaling-guide.mdcompute-plans.mdpersistent-disks.mdDetails
From the project READMERender 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_instancesvalues (the more conservative scale-out).
Scaling Constraints
| Constraint | Behavior |
|---|---|
| Per service | Maximum 100 instances |
| Persistent disk | Cannot scale to multiple instances—disk-attached services stay single-instance |
| Static sites | Not scalable (served by CDN) |
| Cron jobs & Workflows | Scaling model does not apply (different execution model) |
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
planfield. - See
references/compute-plans.mdfor 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
| Topic | File |
|---|---|
| Compute plans and vertical vs horizontal scaling | references/compute-plans.md |
| Persistent disk constraints and storage alternatives | references/persistent-disks.md |
| Enabling autoscaling, targets, min/max, mistakes, previews | references/autoscaling-guide.md |
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:
- 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. - Confirm that retrieval succeeded and returned the expected document, then read its contents. Saving a file or printing its path is not sufficient.
- If the request fails or your tool cannot read the Markdown response, open and read the linked HTML version instead.
- 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.