AWS Aurora Vs RDS: 5 Critical Factors

By: Published: May 18, 2021

Database-as-a-service (DBaaS) cloud services give users an opportunity to use databases without having to configure physical infrastructure and without installing any software. This brings a lot of convenience to many individuals and companies. Since organizations are in need of reducing operational costs, DBaaS has become the best alternative to data storage in most organizations. 

Amazon is the leading provider of DBaaS cloud services. Since DBaaS is in high demand, individuals and companies always find themselves stuck between Aurora and RDS. When figuring out which solution best fits your organization, there are many factors that should be considered. In this article, we will be discussing the differences between Amazon Aurora and RDS. 

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Amazon Aurora Vs RDS

RDS is a hosted database service with multiple flavors of relational database management systems including PostgreSQL, Aurora, MariaDB, and others. Although there are performance benefits that enterprises enjoy for hosting databases on RDS, Aurora comes with even newer tricks. In this article, we will be discussing how Aurora compares with RDS:

1. AWS Aurora Vs RDS: Performance

Subpar performance is not what any user wants for his/her database application. So first we will talk about the very crucial performance factor for the 2 databases. Amazon Aurora gives two times the throughput provided by PostgreSQL or five times the throughput provided by standard MySQL running on similar hardware. 

RDS uses SSDs in its database services for better IO throughput. With Amazon RDS, you can choose between two SSD-backed storage options, that is, one that is optimized for high-performance OLTP applications and another one for cost-effective general-purpose use. 

2. AWS Aurora Vs RDS: Compatibility with Database Engines

Databases are used to gather complete data put together from multiple sources so that Analysts can get a complete view of an organization’s progress. So, it is important to understand the different types of databases that are compatible with Aurora and RDS.

When migrating your on-premises databases to the cloud or from RDS to Amazon Aurora, you should meet your service-level-agreements (SLAs). All steps should be performed with no or minimal downtime. 

Aurora is compatible with two DBMSs namely PostgreSQL and MySQL. It is compatible with PostgreSQL 9.6.1 and MySQL 5.6. This means that you can run your existing database tools and applications on Aurora without any modifications. 

On the other hand, Amazon RDS requires you to use AWS Database Migration Service to migrate from EC2-hosted or on-premises databases such as PostgreSQL, SQL Server, or MySQL to it. 

3. AWS Aurora Vs RDS: Storage Auto-Scaling

AWS Aurora Vs RDS: Storage Auto-Scaling
Image Source: Avi Networks

One of the main reason why Cloud Storage has seen a increased usage inclination rate is because of the ease of Auto-Scaling. Auto-Scaling lets you scale up or down as per your organizations requirements. Cloud helps you save a lot or resources and time with the Auto-Scaling feature.

Amazon Aurora can grow its storage from its minimum of 10 GB to a maximum of 64 TB depending on your database usage. This is done in increments of 10 GB without any impact on the database performance. You are not required to provide the storage in advance. This means that the DBA doesn’t have to predict and provision the storage needed for uninterrupted business operations. 

On the other hand, RDS requires DBAs to provision even up to 6 TB of storage on-the-fly with no downtime. However, note that Aurora can only support the InnoDB storage engine, and it automatically converts tables from other storage engines into InnoDB. 

4. AWS Aurora Vs RDS: Scalability

As a DBA, you must provision scalability while ensuring there is no downtime. Both Amazon Aurora and RDS allow you to scale the memory and compute resources down or up, up to a maximum of 244 GiB of RAM and 32 vCPUs. You can complete your compute scaling operations in just a few minutes with a few clicks. 

5. AWS Aurora Vs RDS: Replication

AWS Aurora Vs RDS: Replication
Image Source: Tutorial Ride

Aurora supports two replica types, with the replicas sharing a similar underlying volume with the primary instance. Updates from the primary instance are made visible to all Aurora replicas. Aurora allows you to provision up to 15 replicas, and the replication is done in milliseconds. In Aurora, failover is done automatically to prevent data loss. 

On the other hand, RDS only allows five replicas, and the process of replication is slower compared to Amazon Aurora. In RDS, failover is done manually which can lead to last-minute data loss. 

Understanding the Limitations of AWS Aurora and RDS

Both Amazon Aurora and RDS have limitations. The following are the limitations of Amazon Aurora:

  1. Its small and medium sized instances are only suitable for testing or development due to their limited number of resources and connections. 
  2. Some suboptimal or odd configuration values cannot be changed. 
  3. A minimum of 5 minutes charge is applied every time the database is active, making it a bit expensive. 

The following are the limitations of Amazon RDS:

  1. Patching causes downtime. 
  2. It doesn’t support scale-out for the write workloads. 
  3. The user has to perform some administrative tasks. 
  4. A downtime is needed to perform scaling operations. 
  5. There is no guarantee for zero data loss. 
  6. It doesn’t support automated compression management. 


In this article, you have learned about AWS Aurora and RDS. You have also learned to differentiate Aurora and RDS with the help of 5 key factors. In the end, this article also highlights the limitations of Aurora and RDS. Now you can consciously decide which database is best suitable for your use case.

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Nicholas Samuel
Technical Content Writer, Hevo Data

Skilled in freelance writing within the data industry, Nicholas is passionate about unraveling the complexities of data integration and data analysis through informative content for those delving deeper into these subjects. He has written more than 150+ blogs on databases, processes, and tutorials that help data practitioners solve their day-to-day problems.

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