Resource management and scaling

Manage your application resources efficiently with intelligent scaling and resource controls.

Application scaling

Control how many instances of your application run simultaneously:

• Scale from 1 to 10 instances based on your needs
• Each instance gets the full allocated CPU and memory
• Instances are distributed across the infrastructure for reliability
• Zero-downtime scaling - add or remove instances without interruption

Worker scaling

Worker services scale automatically with your main application:

• Workers inherit the instance count from the main application
• When you scale your app to 3 instances, workers also scale to 3
• This ensures consistent background job processing capacity
• Workers share volumes with the main application for data access

Service scaling

Database and cache services can be scaled independently:

• Services maintain their own replica counts
• Scale services based on connection needs
• Services persist data across scaling operations
• No data loss when scaling up or down

Grace period management

When your billing grace period expires, resources are automatically managed:

• Applications and services scale down to 0 replicas
• Data remains preserved in persistent volumes
• Resources can be restored when billing is resolved
• No data loss during grace period scaling

Manual scaling controls

You can manually scale your resources at any time:

1. Navigate to your application's Resources tab
2. Adjust the instance count slider
3. Review the cost impact
4. Deploy to apply the changes

Scaling best practices

When to scale up

• Response times are increasing under load
• CPU or memory usage consistently above 80%
• Queue processing is falling behind
• You're expecting increased traffic

When to scale down

• Resource usage consistently below 30%
• Traffic patterns show reduced demand
• Cost optimization is needed
• Development or staging environments during off-hours

Automatic scaling considerations

While manual scaling gives you full control, consider these patterns:

• Scale up before major deployments or events
• Use multiple instances for production workloads
• Single instances are fine for development environments
• Monitor metrics to inform scaling decisions

Resource allocation

Each instance receives guaranteed resources:

• CPU: Dedicated CPU allocation per instance
• Memory: Guaranteed memory per instance
• Network: Shared bandwidth across instances
• Storage: Shared persistent volumes across instances

Cost implications

Scaling affects your costs linearly:

• 2 instances = 2x the base cost
• 3 instances = 3x the base cost
• Services scale independently with their own costs
• Review costs before applying scaling changes

Use scaling strategically to balance performance needs with cost efficiency.