Aurora vs Aurora Serverless V2

Documentation Index

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Use this file to discover all available pages before exploring further.

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Overview

• Amazon Aurora (provisioned)
  • You provision DB instances (instance classes) and can scale compute by resizing instances or adding read replicas.
  • Storage autosizes automatically, but compute scaling is typically manual or scheduled unless you automate it.
  • Provides predictable capacity and control—preferred when you need deterministic performance and instance-level configuration.
• Aurora Serverless v2
  • Offers fine-grained, on-demand compute scaling that automatically matches capacity to workload demand.
  • Billing is based on actual consumed capacity (measured in fractional capacity units such as Aurora Capacity Units or equivalent).
  • Ideal for variable, spiky, or unpredictable workloads and teams that prefer reduced operational overhead.

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Feature Comparison

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Autoscaling and Capacity Management

• Provisioned Aurora
  • Compute capacity is tied to DB instance classes and replicas.
  • You control scaling through instance resizing, adding replicas, or automation like Amazon Aurora Auto Scaling for read replicas.
  • Best when you require predictable throughput, reserved capacity, or fine control over instance configurations.
• Aurora Serverless v2
  • Automatically adjusts compute in fine-grained increments to match workload demand, reducing latency from capacity changes.
  • Removes much of the guesswork in capacity planning and lowers operational overhead.
  • Cost-effective for intermittent or spiky workloads because you pay only for actual usage.

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Pricing and Cost Considerations

• Provisioned Aurora
  • Billed by instance class (vCPU and memory) plus storage and I/O; you pay for the full provisioned compute even when idle.
  • Often more cost-efficient for consistently heavy workloads or when using reserved instances.
• Aurora Serverless v2
  • Billed on actual compute capacity used (fractional units) along with storage and I/O charges.
  • Can reduce costs for workloads with variable or low utilization by avoiding payment for idle provisioned instances.

• For provisioned Aurora, use reserved instances or Savings Plans for predictable workloads.
• For Serverless v2, monitor usage patterns and set appropriate min/max capacity to avoid unexpected spikes.

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When to Choose Which

• Your workload is steady and predictable.
• You need strict control over instance types, network setup, or specialized configurations.
• You prefer reserved capacity or can optimize costs with long-term commitments.

• Workloads are variable, spiky, or hard to predict.
• You want automated, fine-grained scaling and lower operational overhead.
• You’re building prototypes, dev/test environments, or early-stage products where simplicity matters.

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Other Considerations

• Feature parity: Aurora Serverless v2 has broad feature support, but always validate specific extensions, tools, or networking requirements before migrating.
• Operational model: Serverless v2 reduces capacity-management tasks, allowing teams to focus on application development.
• Migration path: Many organizations move from legacy RDS engines → provisioned Aurora → (evaluate) Serverless v2 once usage patterns and cost/benefit align.

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Quick Decision Guide

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Links and References

• Amazon Aurora (provisioned)
• Aurora Serverless v2 documentation
• Aurora Auto Scaling (read replicas)
• AWS RDS Documentation

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Summary