Why migrate to Amazon Aurora I/O-Optimized?

TL;DR: Migrating from Amazon Aurora Standard to Aurora I/O-Optimized can reduce your costs by up to 65%, eliminate the unpredictability of I/O workloads, and improve the performance of your critical databases. Best of all: the transition is simple and downtime-free.

Optimizing costs and improving performance doesn’t have to be a complex project. At Teracloud, we’ve verified with real data that migrating to Amazon Aurora I/O-Optimized can reduce your database costs by up to 65% without affecting the availability of your critical applications.

The invisible problem in Aurora Standard

In many Aurora Standard workloads, I/O costs become unpredictable. Every read and write generates additional charges, inflating your monthly bill unexpectedly. For engineering and finance teams, this means uncertain budgets and limited visibility into the true cost of operating a production database.

Solution: Aurora I/O-Optimized

With Aurora I/O-Optimized, AWS changes the pricing model: instead of paying per I/O operation, you get a fixed price

that includes performance and predictability. This allows you to scale confidently during traffic spikes and simplifies financial management for I/O-intensive workloads.

What Are I/O Operations?

Each time your database reads from or writes to disk, an I/O operation occurs:

• Read: Retrieving a client’s history.

• Write: Logging a new real-time transaction.

In Aurora Standard, these operations are charged per million. High-intensity workloads can quickly drive costs out of control.

Aurora Standard vs. I/O-Optimized

Feature	Aurora Standard	Aurora I/O-Optimized
Cost Model	Pay-per-use	Fixed price
Instance Price	Lower initial cost	Higher (~30% more)
Storage Price	Lower initial cost	Higher (~125% more)
I/O Operations Cost	Charged per million requests	Included in price

While instance and storage costs are higher in I/O-Optimized, total costs can be much lower if I/O spending is significant. Plus, this model offers predictability for high-intensity workloads. The difference is in the cost model, not functionality:

• Aurora Standard: Lower initial cost, I/O charged separately → unpredictable costs.

• Aurora I/O-Optimized: Higher instance/storage costs (30–125%), but all I/O included → predictable costs.

If your I/O spending exceeds 25% of total costs, Aurora I/O-Optimized becomes the more efficient choice.

When to Use Aurora I/O-Optimized

AWS recommends I/O-Optimized if your I/O costs represent 25% or more of total Aurora Standard spending.

Why 25%?

Above this threshold, the savings from eliminating I/O charges outweigh the increase in instance and storage costs. This is a guideline; always validate with your actual usage data.

Our Case: From Variable Costs to Clear Savings

In one of our clusters, I/O costs were the main expense. On August 23, we migrated to I/O-Optimized, and the results were immediate: I/O costs disappeared from the breakdown, replaced by a lower, predictable fixed cost.

Result: Up to 65% projected monthly savings, directly impacting annual operational cost reduction.

The Challenge: I/O Costs

A cost breakdown of a specific database cluster showed that I/O operations were the main expense. The daily cost table illustrates this trend. After migrating to I/O-Optimized, these costs vanished, replaced by predictable fixed charges, showing the direct impact of the migration.

Financial Impact: Comparative Cost Analysis

Before vs. After

We projected monthly costs for our cluster under both storage models using real usage data. Results:

• Aurora Standard (Monthly): Estimated cost under pay-per-use with I/O included (historical consumption).

• Aurora I/O-Optimized (Monthly): Estimated fixed-cost model with I/O included (projected).

Conclusion: Potential 65% Savings! This migration offers substantial cost reduction, lowering annual operational database expenses—a clear business case for adopting Aurora I/O-Optimized.

Operational Validation

Proof of Concept: Zero Downtime

Critical for production environments: continuity. We tested whether changing the storage mode caused downtime.

1. Initial Setup: Deployed Aurora cluster in Standard mode.

2. Load Simulation: Ran a script to maintain a constant connection.

3. Transition: Switched to I/O-Optimized on August 22.

4. Continuous Monitoring: Tracked connection and performance in real time.

Result: Zero downtime. The connection was never interrupted, proving that migration can occur in production without affecting critical applications.

Strategic Next Steps 

Recommendations:

1. Migrate Additional Clusters: Target Aurora clusters with high I/O usage exceeding 25% of total costs.

2. Identify Ideal Workloads: Prioritize OLTP and real-time applications with volatile I/O for maximum benefits.

3. Continuous Monitoring: Post-migration tracking to validate savings and performance long-term.

Final thoughts

This migration represents a strategic opportunity to reduce operational costs significantly without compromising application availability or performance.

FAQ

Key difference between Aurora Standard and I/O-Optimized? 

Aurora Standard charges per million I/O operations, creating unpredictable costs. Aurora I/O-Optimized includes all I/O in a fixed price, offering predictability.

When should I migrate to Aurora I/O-Optimized? 

When I/O costs exceed 25% of total Aurora Standard spending. Beyond this point, I/O-Optimized is usually more cost-efficient.

Does performance change with I/O-Optimized?

No. Functionality and performance remain the same. The change is only in the cost model.

Does migration cause downtime?

No. Real tests showed migration without service interruption, safe for production environments.

Which workloads benefit most? 

OLTP workloads, real-time applications, and high read/write systems gain the most cost advantage.

Data Engineer

Nicolas de María