Amazon RDS vs Aurora: Which is Right For Your Data Workload?

Key Differences Between Amazon RDS and Aurora

Both RDS and Aurora simplify database administration, but they have key differences in performance, cost, scalability, availability, and fault tolerance. Let’s break them down in simple terms.

Performance

RDS provides solid performance, but it depends on your chosen database engine. Since RDS supports multiple engines, such as MySQL, PostgreSQL, and SQL Server, performance varies based on the engine’s capabilities and configurations.

Aurora, on the other hand, is designed for high performance. Because of its cloud-native architecture, it can deliver up to five times the throughput of MySQL and three times that of PostgreSQL. It automatically optimizes read and write speeds, making it ideal for high-demand applications.

Cost

RDS tends to be more cost-effective for smaller workloads, as you only pay for the instance type and storage you choose. It’s a great option if you don’t need extreme scalability or ultra-high availability.

Aurora can be more expensive because of its advanced architecture and high availability features. However, it performs better at a lower cost than running equivalent high-performance RDS instances. Aurora may save money in the long run if your application requires high-speed transactions and scalability.

Scalability

With RDS, scaling is manual or requires setting up auto-scaling, and performance depends on the instance size you choose. You may need to plan for capacity increases as your workload grows.

Aurora automatically scales storage up to 128 TB without manual intervention. It also supports replicas for read scaling, allowing you to easily handle a growing number of users.

Availability and fault tolerance

RDS offers high availability through multi-AZ deployments, which means it can create standby replicas in another availability zone. However, failover can take a minute or more.

Aurora is designed with fault tolerance in mind. It automatically replicates data across three Availability Zones and can handle instance failures with almost no downtime. It also supports the Aurora Global Database, allowing for near-instant replication across different regions for disaster recovery.

The following table summarizes the main differences between Amazon RDS and Aurora, which we just reviewed:

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Performance and Benchmarking

Performance is a key factor when choosing between RDS and Aurora. Let’s examine their performance in different workloads and explore ways to optimize each one.

Complex queries and OLTP systems

RDS performs well for most transactional (OLTP) applications, especially for small to medium workloads. However, its performance is limited by the instance type and storage configuration.

As mentioned before, Aurora is optimized for high-throughput transactions. Due to its distributed storage and parallel query execution, it offers 5x the performance of MySQL and 3x the performance of PostgreSQL. It is ideal for applications with complex joins and frequent updates.

Are you unsure whether your workload is OLTP or OLAP? This OLTP vs. OLAP guide can help you choose the right database solution.

Read-heavy workloads (data warehousing and reporting)

RDS supports read replicas to handle additional read traffic, but replication lag can be a concern, especially for MySQL and PostgreSQL.

Aurora provides faster and more efficient read replicas, with replication delays as low as milliseconds. It can scale up to 15 read replicas, making it an excellent choice for analytics and dashboards with high read demand.

High-concurrency applications (web and mobile apps)

RDS can handle concurrent connections, but performance may degrade as traffic increases, especially in lower-tier instances.

Aurora is built for high concurrency, supporting millions of transactions per second with automatic scaling and connection pooling. This makes it ideal for SaaS platforms, real-time applications, and high-traffic websites.

Optimizing performance on both platforms

Optimizing your database setup is crucial. Here are some best practices to ensure top performance.

Query tuning

A well-optimized query can make a huge difference in database performance. 

One of the first steps I’d suggest is proper indexing—by indexing columns frequently used in queries, you can significantly speed up search operations. If your queries involve multiple columns, composite indexes can enhance performance by allowing the database to filter results more efficiently.

It's also important to write queries with optimization in mind: 

• Instead of using sql SELECT *, specify only the columns you need.
• Additionally, running sql EXPLAIN ANALYZE can help identify slow queries and provide insights into how they are executed, allowing you to fine-tune them.
• As a general rule, minimizing joins and subqueries where possible can also prevent performance bottlenecks and keep your database running smoothly.

Read this SQL query optimization guide for more practical tips to boost database performance!

Choosing the right instance type

With RDS, it is important to choose an instance that provides sufficient CPU, memory, and disk throughput to match your workload demands. Under-provisioning can lead to slow queries and performance bottlenecks, while over-provisioning may result in unnecessary costs.

Aurora offers additional flexibility, especially with Aurora Serverless, which automatically scales up or down based on demand—making it a great option for unpredictable workloads. 

Provisioned Aurora instances are the better choice for applications requiring consistently high performance, ensuring stable and reliable database operations.

Managing Costs with RDS and Aurora

Cost is a major factor when choosing between these two services. While both offer managed database solutions, their pricing models differ significantly based on storage, computing, and scalability. 

Let’s explore strategies to manage costs and compare pricing for different workloads.

Choose the right instance type

• Avoid over-provisioning. Select an instance size that fits your workload and scale up only when necessary.
• Use AWS Compute Savings Plans or Reserved Instances for predictable workloads to reduce costs.

Optimize storage costs

• In RDS, choose General Purpose (gp2 or gp3) storage for low-cost applications and Provisioned IOPS (io1 or io2) only when high performance is needed.
• In Aurora, storage auto-scales in 10 GB increments, which helps manage costs, but be mindful of unused capacity.

Leverage auto-scaling and serverless options

• Aurora Serverless automatically adjusts capacity based on demand, making it cost-effective for intermittent workloads.
• RDS Auto Scaling allows dynamic resizing of read replicas to handle traffic spikes without over-provisioning.

Use read replicas and caching efficiently

• Instead of scaling up a single large database, distribute read-heavy traffic using read replicas.
• Implement Amazon ElastiCache to reduce database load and minimize query costs.

Keep an eye on costs

• Enable AWS Performance Insights to identify slow queries that consume excessive resources.
• Use AWS Cost Explorer to track spending trends and set budget alerts to avoid surprises.

Cost comparison based on workload

The cost of using RDS or Aurora can vary significantly based on your specific workload requirements. Some applications prioritize low-cost solutions with moderate performance, while others demand high availability and scalability, justifying a higher price. 

Let’s explore how pricing compares in different scenarios.

Use Cases for Amazon RDS

RDS is perfect for applications that need a reliable, managed database without extreme performance demands. It’s a great fit if you want the flexibility to choose your database engine while offloading administrative tasks like backups, patching, and scaling. Here are some RDS use cases:

• Small to medium-sized applications – Web applications, mobile apps, and internal business tools that require a relational database but don’t have massive scaling needs.
• Traditional enterprise applications – Applications using SQL Server or Oracle that need managed hosting with minimal setup.
• Cost-sensitive projects – Startups and smaller teams that want a cost-effective database solution without paying for advanced performance features.
• Applications with predictable workloads – If your database usage is relatively stable, manual or scheduled scaling in RDS can be cost-effective.

If your project uses SQL Server, this introductory course is a great way to understand its key features and capabilities on RDS.

Use Cases for Amazon Aurora

Aurora is ideal for applications that require high performance, automatic scaling, and built-in fault tolerance. It’s designed for workloads requiring high throughput and availability while still being compatible with MySQL and PostgreSQL. Some common use cases:

• High-traffic applications – SaaS platforms, e-commerce websites, and online gaming applications that require fast response times and can’t afford downtime.
• Big data and analytics – Applications that need to process large amounts of data efficiently, such as real-time analytics and business intelligence tools.
• Mission-critical applications – Banking systems, healthcare platforms, and global services that need near-instant failover and multi-region replication.
• Auto-scaling environments – Applications with fluctuating traffic, such as social media platforms or seasonal e-commerce stores, where Aurora’s auto-scaling storage and read replicas can optimize performance and cost.

Pros and Cons of Amazon RDS vs. Aurora

To summarize, each of these services has its strengths and trade-offs. Below is a breakdown of their key pros and cons to help you decide which is the best fit for your needs.

Conclusion

Deciding between RDS and Aurora comes down to what your application needs in terms of workload, budget, and performance requirements. By understanding their differences, you can make an informed decision that best suits your application’s needs.

RDS is a cost-effective, easy-to-use option for applications with moderate workloads, while Aurora is optimized for high performance, scalability, and availability.

If you’re running small to medium applications with standard database needs, RDS offers a managed experience with lower costs. However, Aurora is the better investment if your application requires high throughput, minimal replication lag, and seamless scaling.

I hope this guide helped clarify the differences between these two powerful AWS services and helped you better determine which one is right for your project.

If you're looking to deepen your understanding of how RDS, Aurora, and other AWS services work together in real-world cloud environments, check out AWS Cloud Technology and Services—a hands-on course to build your cloud skills from the ground up.