AWS Cost Efficiency

Unlock Hidden Savings: The Ultimate Guide to Amazon Aurora Cost Management - Part 1

Amazon Aurora Series Part 1

As of 2024, a report suggests over 4,954 companies worldwide have adopted Aurora for their relational database needs. Amazon Aurora is a high-performance, cloud-native managed relational database service offered by Amazon Web Services (AWS). It is faster, more scalable, and more reliable than traditional MySQL and PostgreSQL databases. Amazon Aurora is fully compatible with MySQL and PostgreSQL.

In this guide, you'll gain insights into the distinctions between Aurora and other RDS options, delve into Aurora's pricing models, and discover strategies to effectively lower Aurora costs.

Top Industries that use Amazon Aurora

Companies that use Amazon Aurora

Company Website Country Revenue Company Size
Infosys Ltd India >1000M >10000
Cornell University United States >1000M >10000
Blackfriars Insurance Brokers Ltd United Kingdom >1000M >10000
Blue Cross Blue Shield Association United States >1000M 1000-5000
Kohl’s, Inc. United States >1000M >10000


Amazon Aurora Versus Other RDS

Aurora's higher base cost is outweighed by its superior performance, higher availability, and automatic scaling, often resulting in lower overall costs compared to other RDS options. Features like I/O optimization and Aurora Serverless can significantly reduce expenses, making Aurora a cost-effective choice over time.

Customers running production workloads will save 10-20 % when migrating from AWS RDS for PostgreSQL and 50-70 % when migrating from AWS Aurora Serverless.

The table below provides a comprehensive comparison of Aurora and other RDS.

Amazon RDS Comparison

Feature Amazon Aurora Other RDS Services
Database Engine MySQL and PostgreSQL compatible MySQL, PostgreSQL, MariaDB, Microsoft SQL Server, Oracle
Cost Generally higher, but cost-effective for high-traffic applications Varies by engine, affordable for less demanding workloads
Performance High performance, faster read replicas, virtually unlimited storage scaling Varies by engine, generally good but might not match Aurora for high-demand applications
Availability High availability with automatic failover and multi-AZ deployments High availability with read replicas, slower failover and potentially more configuration
Durability Designed for data durability with continuous backups and low RPO Offers backups and point-in-time recovery, but durability may be less robust
Ease of use Straightforward setup for familiar MySQL or PostgreSQL users Wide range of engines, some with steeper learning curves

Aurora Pricing

Aurora provides pricing models for various business needs such as predictable, on-demand, pay-as-you-go, and reserves instances. Here is a snapshot of pricing to keep in mind as you configure Aurora for your unique business needs.

The following table provides a snapshot of pricing for various parameters in Aurora.

Amazon Aurora Serverless Pricing

Dimension Description Measure Pricing
Database instances Aurora Serverless measures database capacity in Aurora Capacity Units (ACUs) billed per second. Aurora Capacity Units (ACU) Billed per second
1 ACU has approximately 2 GiB of memory with corresponding CPU and networking resources.
Aurora Standard (per ACU hour): $0.12
Aurora I/O optimized (per ACU hour): $0.16
Storage Billed at per GB-month increments depending on Standard or I/O optimized configuration. Per GB-month Aurora Standard: $0.10 per GB-month
Aurora I/O Optimized: $0.225 per GB-month
I/O Aurora is designed to remove unnecessary I/O operations. If a query cannot be served from memory, you are charged for data pages retrieved from storage. Write I/O operations are counted in 4KB units. Per million requests Aurora Standard: $0.20 per 1 million requests
Aurora I/O Optimized: Included
Global database Pay for replicated write I/O operations between primary region and each secondary region Replicated write I/Os $0.20 per million replicated write I/Os
Backup storage There is no charge for backup storage of 100% of your database cluster and for snapshots created in the backup retention period. All other backups and snapshots are metered per GB-month Per GB-month $0.021 per GB-month
Snapshot or cluster export For all snapshots and cluster exports, usage is metered per gigabyte of snapshot data. Subsequent exports of data from the same snapshot are not incremental. Charge per GB of snapshot size $0.010
Data API Cost for HTTPS API for executing SQL queries against Aurora databases Number of requests (per month) First 1 billion requests: $0.35
Above 1 billion requests: $0.20

Always view the Amazon AWS Pricing page for current pricing slabs and sample scenarios. Charges vary by region. For example, backup storage cost is $0.021 per GB-month for US East (Ohio) and $0.023 per GB-month for Asia Pacific (Mumbai).

Strategies to Optimize Your Aurora Costs

1. Schedule DB On/off Based on Usage Patterns: Optimize Aurora usage by scheduling DB on/off based on usage patterns. For example, for batch processes like payroll, turn off the Aurora cluster when not in use. Identify low-traffic hours, such as off-peak website usage, and switch the DB off accordingly. In development/test environments, only activate the cluster as needed to save costs. When the Aurora cluster is inactive, charges are limited to storage, manual snapshots, and automated backups. Aurora clusters can be stopped for up to 7 days, automatically restarting afterward for maintenance updates.

2. Right-size Your Aurora Instances: To optimize performance and cost in Amazon Aurora, choosing the right instance type is crucial. Different instance types, like db.r6g for memory-intensive workloads and db.m6g for general purposes, cater to various needs. Proper sizing is essential to avoid underperformance from under-provisioning or excessive costs from overprovisioning. Analyzing data with CloudWatch metrics or performance insights helps find the right balance. For non-production environments, consider using burstable-performance or T instance classes to optimize costs.

3. Use Reserved Instances: For predictable workloads in 24/7 production applications, using reserved instances for your Aurora DB can yield significant cost savings over on-demand pricing. Analyze usage patterns to match your needs with reserved instance options, offering lower costs for predictable workloads while retaining flexibility for unexpected spikes. Reserved instances offer payment options like all upfront, partial upfront, or no upfront, with potential savings of up to 69% compared to on-demand rates, especially for steady-state usage.

4. Use Graviton Processors: Using Aurora databases in conjunction with Graviton-based processors provide a 35% better price performance for your Aurora instance. This means more processing power and performance for every dollar spent. Due to the performance improvements provided by Graviton, you can downsize your instance class while maintaining the same level of performance, leading to further cost savings. The price-performance with Graviton varies based on the size of the Aurora database. This is useful if you have large databases because of the dual advantage of a performance boost as well as cost reduction.

5. Use Caching: Frequently accessed data is prime for caching but be mindful of update frequency. Data that changes periodically requires more frequent refreshing, incurring costs. For instance, a static list of cities accessed often is a suitable caching candidate. A carefully planned caching policy not only cuts bills but also enhances database performance.

6. Switch to I/O Optimized Configuration: If your Aurora workloads are read and write intensive, switching to an Aurora I/O optimized configuration can provide significant cost savings. This configuration does away with per I/O charges and you pay only for the compute and storage resources used by your database. Consider a simplified example. Let us say the current monthly bill for your Aurora cluster is $500. Assume the charges are for storage, compute, and IOPS for simplification. Based on AWS pricing, assume $200 is for compute resources and storage. This leaves $300 for IOPS. Switching to I/O optimized configuration will easily shave off the $300 from your total Aurora bill per month.‍

Factors to consider before switching to I/O optimized

- If your I/O spends is below 25% of our total bill, it might be cost-efficient to stay on the current billing plan.

- Reserved instances provide discounted pricing. Switching to I/O optimized configuration along with reserved instances may not provide a cost advantage.

By evaluating your current and projected I/O operations, you can choose to switch to I/O optimized to leverage the benefits of reduced costs.

7. Use Aurora Serverless: Consider Aurora Serverless for your database needs. It is Amazon's auto-scaling Aurora solution, eliminating manual instance management by automatically adjusting compute resources based on demand. With pay-per-second billing, you are only charged for resources used, reducing wastage for fluctuating workloads. Plus, it minimizes management overhead, handling provisioning and monitoring. However, assess if it suits your needs: steady high usage might favor traditional provisioning, and Serverless sacrifices some control over configurations. Also, predicting costs for highly erratic workloads can be challenging.

8. Optimize Storage: Optimizing storage can lead to significant cost reductions in your cloud bill. While exact percentages may vary depending on your specific use case and data storage requirements, implementing storage optimization techniques such as data archiving and partitioning can typically result in cost savings ranging from 20% to 50% or more. By identifying and archiving infrequently accessed data and partitioning large tables, organizations can effectively manage storage costs while improving performance and scalability.

9. Optimize Queries: Optimize queries in Amazon Aurora by utilizing the Performance Insights tool to visualize load and identify areas for improvement. Monitor queries to ensure optimal design and performance, using features like the Top SQL tab to analyze running queries. Avoid long-running queries to prevent excessive costs from storage, data transfer, and IOPs. For instance, optimize queries like scanning entire tables by creating specialized tables for faster retrieval, reducing unnecessary I/O operations. Implement query monitoring and set thresholds to identify and address prolonged query durations efficiently.


In conclusion, optimizing costs for Amazon Aurora MySQL and PostgreSQL entails a holistic approach that balances performance, scalability, and efficiency. While Aurora offers superior features compared to traditional databases, such as automatic scaling and high availability, understanding pricing models, right-sizing instances, and implementing proactive strategies like scheduling based on usage patterns are crucial for cost optimization. Additionally, optimizing queries, leveraging caching, and implementing data archiving and partitioning strategies play a vital role in reducing resource usage and storage costs. By adopting a comprehensive approach and continuously refining strategies, organizations can maximize ROI and achieve cost-effective operations with Amazon Aurora.

What’s Next?

Optimizing Aurora MySQL and Aurora PostgreSQL Costs!

Now that we have covered the basics of Aurora cost optimization, we will discuss how you can further optimize your Aurora MySQL or Aurora PostgreSQL databases to reduce your cloud bills.

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