Aurora I/O-Optimized

Use Aurora I/O-Optimized where I/O dominates your Aurora bill to simplify and potentially reduce costs.

Why it matters

Aurora normally charges for instances, storage, and I/O operations. For I/O-heavy workloads, the I/O line item can become a large and noisy part of the bill. Aurora I/O-Optimized changes the model: you pay for instances and storage only, with no separate I/O charge, which can simplify cost forecasting and, for some workloads, reduce total cost.1

When it helps

Aurora I/O-Optimized is most likely to be a fit when:

  • Your Aurora clusters are write- or read-intensive, and I/O charges are a large fraction of total Aurora cost.
  • You want more predictable pricing with fewer moving parts (no per-I/O line item).
  • You plan to keep the workload running for long enough that configuration changes are worth the effort (1 month).

It is less likely to help when:

  • Your workload is light on I/O, so instance and storage already dominate cost.
  • You frequently change cluster configuration and do not want to track I/O-Optimized eligibility and switches.

Rule of thumb

  • If I/O costs > 25% of total Aurora costs → Choose I/O-Optimized
  • If I/O costs < 25% of total Aurora costs → Choose Standard
  • For example, if your monthly Aurora bill is about $10k and I/O is roughly $3k of that, I/O-Optimized is worth a serious evaluation.

Treat this as a heuristic, not an AWS guarantee—always validate with your own Cost Explorer data and the current Aurora pricing documentation.1

See IO costs on Cost Explorer

  • Use the same Cost Explorer filters described above (Service = Relational Database Service (RDS), Usage Type including Aurora:StorageIOUsage) to visualize how much of your Aurora spend comes from I/O over time.

Aurora I/O costs filtered by StorageIOUsage in Cost Explorer

How to implement

  1. Measure current I/O share

    • Use Cost Explorer and the billing console to understand how much of your Aurora spend comes from I/O versus instances and storage. In Cost Explorer, group by Usage Type, filter by Service = Amazon Relational Database Service, and look for usage types such as Aurora:StorageIOUsage to see I/O charges.
    • In CloudWatch, use the Aurora metrics ReadIOPS and WriteIOPS at the instance or cluster level to see how many I/O operations your workload is driving over time.
    • Focus on clusters where I/O is clearly a large portion of total cost; some practitioners use thresholds like “if I/O is above roughly a quarter of total Aurora cost, evaluate I/O-Optimized.”2

  2. Check engine and instance compatibility

    • Confirm that your Aurora engine version and instance classes support I/O-Optimized in the current region.

  3. Combine with storage optimization

    • While evaluating I/O-Optimized, also review your overall Aurora storage practices—cleaning up unused data and tuning queries can reduce I/O volume regardless of pricing model. See Optimize storage for complementary storage cost controls.

  4. Roll out carefully in production

    • Be aware that toggling between standard and I/O-Optimized is limited in frequency (once per month at the moment of writing this), so avoid rapid back-and-forth changes if the workload is not stable.
    • On NVMe-backed d instance classes (for example, r6gd, r6id), switching between Standard and I/O-Optimized requires a database engine restart, which introduces a brief period of downtime; plan a maintenance window and test the behavior in a lower environment first.3

Resources

Footnotes

  1. AWS Announces Amazon Aurora I/O-Optimized ↩ ↩2

  2. 20% reduction in AWS RDS spend by switching to Aurora I/O-Optimized ↩

  3. Downtime of switching storage configuration on Aurora PostgreSQL with NVMe-based reader instance ↩