Quick Summary

RDS vs Aurora—the blog compares and oversees the captivating clash between two databases. To make the fight enjoyable, we covered their features and similarities, dissected their differences with an in-depth comparison, and dived into their use cases, helping you determine which option best suits your needs. Whether you are a business owner or a developer, we have addressed every aspect to simplify the decision-making process between the two powerhouse database solutions.

Table of Contents


The backbone of your operation lies in the database you choose. If your platform handles a torrent of transactions, it must effortlessly manage the surge in demand. Aurora can step onto the stage, offering unparalleled speed and performance, which is tailor-made for high-velocity platforms. Conversely, if you’re navigating the delicate balance between adaptability and cost-effectiveness, RDS offers a compelling alternative, providing the agility to evolve without burning a hole through your pockets.

But how do you decide which database holds the key to unlocking your platform’s full potential? Our blog, RDS vs Aurora, serves as your compass in this complex query, walking you through the critical considerations. From workload dynamics to financial constraints, we’ll help you pinpoint the optimal solution for your unique needs.

Let’s get started – the basics await!

What is Amazon RDS?

Amazon RDS is a user-friendly broader relational database service designed to deploy and manage various database engines (like MySQL, PostgreSQL, MariaDB, Oracle, or SQL Server) in the cloud. It’s easy to set up, manage, and adjust according to personal needs. With RDS, routine tasks like setting up, configuring, and backing up databases are automated, saving you time and effort. You can quickly establish and customize a new database to meet your unique requirements.

Features of Amazon RDS

  • Quickly deploy production-ready relational databases using AWS Management Console, CLI, SDKs, or API calls.
  • RDA automates software patching, backups, provisioning, and maintenance, reducing administrative burden.
  • Access operational metrics, including resource utilization and performance insights, through Amazon CloudWatch and the RDS Console.
  • Simplify resource management and provisioning using AWS CloudFormation templates.
  • Receive alerts for over 40 database events via email or SMS using Amazon SNS.
  • Audit configuration changes and encrypt data for compliance and security.
  • Rapidly clone multi-terabyte database clusters for development, testing, and analytics.
  • Perform database updates with zero data loss by creating staging environments that mirror production.
  • Access analytics and machine learning capabilities on transactional data without complex ETL pipelines.
  • Optimize performance with General Purpose and Provisioned IOPS SSD storage, cache integration, and performance diagnostics.
  • Maximize data accessibility through automated backup mechanisms, point-in-time recovery capabilities, and Multi-AZ deployments.
  • Scale compute, storage, and read capacity dynamically to meet changing workload demands.
  • Pay only for resources used, with options for significant discounts through Reserved Instances and the AWS Free Tier.
  • Safely extend functionality with Trusted Language Extensions and integrate with AI and ML capabilities.
  • Seamlessly migrate databases using AWS Database Migration Service, Schema Conversion Tool, and native tools like Babelfish.

What is Amazon Aurora?

Amazon Aurora is a managed relational database engine crafted specially for the cloud and is compatible with MySQL and PostgreSQL. It’s like having a turbocharged version of these databases, offering faster performance and higher reliability. Aurora can handle larger datasets and has auto-scaling capacity, enabling flexible scaling whenever needed. It simplifies complex tasks like clustering and replication, making database management more effortless.

Features of Amazon Aurora

  • Aurora outperforms MySQL by up to five times and PostgreSQL by up to three times in data processing speed.
  • Aurora Serverless adjusts its capacity automatically, starting up, shutting down, and scaling according to your application’s demands.
  • You can effortlessly adjust the capacity of provisioned instances using either the Amazon RDS APIs or the AWS Management Console.
  • It automatically adjusts I/O and increases storage size as needed without manual intervention.
  • Creates up to 15 read replicas for increased throughput and reduced replica lag time.
  • It spans a single Aurora database across multiple AWS regions for global reads and disaster recovery.
  • Manages workload distribution by customizing and balancing across various instance sets with tailored endpoints.
  • Aurora encrypts data at rest and in transit, integrates with AWS IAM for resource-level permissions, and offers advanced auditing.
  • Multi-AZ deployments, automated failover, and continuous backups ensure fault tolerance and self-healing storage.
  • Pay-as-you-go pricing, flexible configuration options, and price predictability make Aurora cost-effective.

AWS Aurora vs RDS: Similarities

When we compare RDS vs Aurora, we see that both share advantages in simplifying database management and ensuring seamless operations.

1. Pre-configured Environments: Aurora and RDS offer ready-to-deploy setups, saving time in system administration and application deployment, even for teams without dedicated DBAs.

2. Flexible Operations: Aurora and RDS provide similar operational flexibility for upgrades and backups, catering to varying needs and skill levels.

3. Seamless Updates: Both services ensure that Amazon seamlessly applies updates and patches without causing downtime. Maintenance windows can be set for automated patching within specified timeframes.

4. Continuous Backup: Your data is always protected. Aurora and RDS continuously back up data to Amazon S3 in real-time, ensuring security without slowing down your applications. It removes the complexity of manual backups and designated backup times.

While these shared features offer undeniable convenience, consider potential considerations like vendor lock-in and the possibility of challenges arising from enforced updates or the need for client-side optimizations. A thorough evaluation of your needs will help you determine which services between RDS vs Aurora perfectly fit your application.

Amazon RDS vs Amazon Aurora: Comparison Table

Before we go into the significant differences, let’s compare the two databases using a comparison table.

Amazon RDS vs Amazon Aurora Comparison Table

RDS vs Aurora: In-Depth Comparison

Both databases, RDS vs Aurora, vary in several areas; Amazon Aurora is built for the cloud and supports serverless technology. Amazon RDS offers more customization options and a more comprehensive range of database engines. Let’s take a closer look at the other differences between them.

Performance Considerations

When comparing RDS vs Aurora, Aurora stands out for its remarkable performance, providing double the throughput of PostgreSQL and five times that of standard MySQL on similar hardware. Its unique storage architecture guarantees increased and sustained performance, ensuring business continuity. In Aurora, logs are written directly to storage without log buffers. Replication to replicas occurs asynchronously but solely for cached data(only for data already in memory). As replicas share the same storage cluster, lag remains minimal and consistent even during periods of increased workload.

On the other hand, RDS employs SSD storage, presenting users with a choice between two alternatives: one tailored for high-performance Online Transaction Processing (OLTP) applications and another geared towards cost-efficient general-purpose usage, adding cost-effectiveness to your needs.

Availability and Durability

Aurora surpasses RDS regarding both availability and durability, primarily because of its distinctive storage model and capability for continuous backups, ensuring a very low recovery point objective (RPO). Aurora inherently ensures data durability, maintaining multiple copies of your data that are always accessible. Even with just one server (compute node), every Aurora cluster includes six storage nodes distributed across three Availability Zones (AZs).

In contrast, achieving similar durability levels in RDS typically requires maximizing the utilization of read replicas. In comparing AWS Aurora vs RDS, these inherent features make Aurora a more solid option for availability and durability.


Aurora outperforms RDS thanks to its solid architecture. When a compute node fails in Aurora, recovery is swift. It can promptly initiate new read replicas with minimal delay. Additionally, other nodes can swiftly promote another replica without consensus if the primary writer fails. In Aurora, a shared state resides in the data nodes, enabling quick replacement of failed nodes.

On the other hand, recovering from failures might take longer in RDS if a computed node crashes. Restarting replicas and promoting a new writer could involve more time and coordination among nodes, as shared state management differs from Aurora’s streamlined approach. This difference in the RDS vs. Aurora debate underscores Aurora’s superior efficiency and resilience in managing failures.


Aurora simplifies storage scaling by automatically increasing capacity from a minimum of 10 GB up to 128 TiB in increments of 10 GB. This expansion occurs seamlessly without affecting database performance; you don’t need to allocate storage beforehand.

On the other hand, with RDS, storage auto-scaling responds to growing workloads by automatically scaling capacity up to 64 TiB (except for SQL Server, which scales up to 16 TiB) without any downtime. Simply specify your preferred maximum storage limit and let auto-scaling handle the rest automatically. When evaluating RDS vs Aurora, this difference point in Aurora stands out with its higher maximum storage capacity and smooth scaling process.

Migrate From AWS Aurora to AWS RDS or Vice Versa?

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Aurora allows effortless adjustment of memory and compute resources, offering up to 244 GiB of RAM and 32 vCPUs, which can be scaled up or down as needed. Aurora Auto Scaling dynamically manages Aurora Replicas based on workload changes, ensuring efficient resource usage.

Scaling memory and compute resources is straightforward for RDS but doesn’t offer dynamic scaling like Aurora. With RDS, you must manually adjust resources based on changing demands, as it lacks built-in support for automatic scaling. In this difference point between RDS and Aurora, Aurora’s dynamic scaling capabilities provide a significant advantage in managing fluctuating workloads efficiently.

Failover Cluster Endpoints

For Aurora, you have a cluster endpoint for writing queries and a reader endpoint for reading queries. The reader endpoint automatically balances the load across your read replicas. If there’s a failover, one read replica becomes the new master and removes it from the reader set.

As for RDS, there’s only a cluster endpoint for writing queries. During a failover, RDS switches this endpoint to the new master using a simple DNS change. However, for reading queries, you need to manage load balancing manually using the instance endpoints, as RDS doesn’t offer a built-in load balancer for read replicas. When comparing RDS vs Aurora, Aurora’s automated load balancing for read replicas simplifies database management significantly.


When we compare RDS vs Aurora for the backup point, Aurora automatically backs up your cluster volume and keeps restoring data throughout the backup retention period. These backups are continuous and incremental, allowing quick restoration to any point within the retention period without affecting database performance.

On the other hand, RDS creates automated backups during your instance’s backup window and stores them in Amazon S3. The backups comprise storage volume snapshots of the entire DB instance rather than individual databases following the specified retention period. While the backup is in progress, there might be interruptions in storage I/O, affecting database performance. Know more about Amazon Se Pricing here


Aurora typically costs about 20% more than RDS MySQL. Enabling read replicas incurs twice the expenses for Aurora. Additionally, Aurora is exclusively accessible on specific RDS instance sizes. Storage costs differ between Aurora and RDS MySQL. You choose the EBS volume type and size for RDS MySQL, ensuring compatibility with your instance type. With Aurora, IOPs are limited only by instance type, and you’re charged based on dataset size and requests per second.

In comparing AWS Aurora vs RDS, these distinctions are essential to consider as they directly impact your database solution’s overall cost, scalability, and performance.


While comparing AWS Aurora vs RDS, we learned that Aurora takes replication to new heights. You can provision up to 15 replicas, a significant upgrade from the five replicas allowed in RDS MySQL. All replicas are interconnected with Aurora, sharing the primary instance’s underlying volume. Consequently, replication processes are incredibly swift, with updates from the primary instance immediately propagated to all Aurora replicas, typically in milliseconds. Furthermore, failover in Amazon Aurora is seamlessly automated, ensuring zero data loss.

In contrast, while RDS MySQL offers the option to configure replica failover priorities, the replication speed and failover mechanism may not match Aurora’s seamless efficiency.

Architecture Design

The architectural approach differs significantly between AWS Aurora vs RDS. Aurora’s architecture is cloud-native, separating computing from storage. Data is stored in a shared cluster volume distributed across multiple storage nodes in different AWS Availability Zones. This setup ensures multi-AZ resilience and automatic storage scaling up to 128TiB. Aurora adopts a “pay for what you use” pricing model similar to Amazon S3.

On the other hand, RDS combines computing and storage within each node, following a more traditional database model adapted for the cloud. While RDS supports multiple database engines like MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server, Aurora is limited to MySQL and PostgreSQL compatibility. RDS automates database provisioning and management, offering flexibility for various use cases, including on-premises deployment with AWS Outposts and migration using the Database Migration Service.


In Aurora, various monitoring options are available, including automated monitoring and the ability to specify log types for publication to Amazon CloudWatch. However, it’s essential to note that with Aurora T2 instances, enabling performance schema may lead to memory depletion issues on the host.

Similar monitoring capabilities exist in RDS, allowing for automated monitoring setup and selecting log types to be sent to Amazon CloudWatch. Additionally, other monitoring tools can be utilized for metric collection. In comparing AWS Aurora vs RDS, these monitoring features play a crucial role in ensuring the health and performance of your database instances.

Security, Authentication, and Encryption

Similar to RDS, in Aurora, you can manage access permissions for your database. You do this by managing login permissions. You can use Amazon VPC to set up a private network for your database. With IAM policies, you can decide who can manage your Aurora resources. Security groups enable you to designate the EC2 instances or IP addresses permitted to establish connections with your database. And if you’re worried about data security, you can use TLS/SSL connections for most database engines Aurora supports. Aurora also supports Kerberos authentication, an extra layer of protection for MySQL and PostgreSQL clusters.

Similar to Aurora, RDS allows you to control who accesses your database. You can also configure a private network using Amazon VPC. IAM policies help manage who can control your RDS resources. Security groups determine which EC2 instances or IP addresses can connect with your database. Plus, RDS offers encryption for your database instances and backups, ensuring your data is safe even when unused. Thus, between RDS vs Aurora, both provide security features at their best in the database to protect your database assets.


Amazon Aurora simplifies maintenance by automatically updating your database with the latest patches. It means you don’t need to worry about manually applying updates to keep your database secure and up-to-date.

In contrast to Amazon RDS, it ensures maintenance by regularly updating your databases with the latest patches during scheduled maintenance windows. This helps keep your database secure and running smoothly. Additionally, you can control the patching process for your RDS instances, allowing you to schedule updates according to your business needs and priorities. In comparing RDS vs Aurora, both platforms prioritize database security and reliability through automated patching mechanisms.

RDS vs Aurora: When To Opt For Which Database Service?

Here are various scenarios to help you determine which database service, RDS vs Aurora, suits your needs best and when to choose which:

Choose Amazon RDS If You:

  • Require assistance for multiple database engines, including MySQL, PostgreSQL, Microsoft SQL Server, MariaDB, Oracle, and RDS on Amazon Outposts.
  • Need a fully managed database solution with minimal changes to existing applications.
  • Greater control over underlying instances, including instance types and sizes, is required.
  • Prefer to retain transaction logs and backups/recovery data for a specified period.
  • Are deploying long-running database jobs that require high availability, scalability, and resilience across multiple Availability Zones.
  • Need a cost-effective multi-AZ deployment option.

Choose Amazon Aurora If You:

  • Need up to 5X better performance than conventional MySQL and up to 3X better than PostgreSQL.
  • Need lower latency and faster read replicas across multiple Availability Zones.
  • More read replicas (up to 15) are required than Amazon RDS.
  • Require robust automated backups and data recovery for up to five minutes.
  • Prefer a database that is faster, more durable, and cloud-native.
  • Have unpredictable database workloads and need a highly performant and cost-effective serverless option.

Ultimately, the decision between RDS vs Aurora is based on different business requirements and your applications’ specific performance, scalability, and compatibility needs.

RDS vs AWS Aurora: Use Cases

Here are various Amazon Aurora vs RDSuse cases depending on instances:

Ideal Use Cases for Aurora

  • Revamp enterprise applications to ensure high availability and optimal performance.
  • Construct scalable SaaS applications with adaptable instance and storage scalability.
  • Deploy globally distributed applications for multi-region scalability and resilience.
  • Embrace serverless computing to save up to 90% in costs with hands-off capacity management.

Ideal Use Cases for RDS

  • Cost-sensitive applications with moderate data processing requirements.
  • Existing applications already using MySQL, PostgreSQL, MariaDB, Oracle, or SQL Server.
  • Development and testing environments where priority is for flexibility and familiarity.
  • Applications with less stringent high availability requirements.


In conclusion, your choice between RDS vs Aurora should align with your business requirements and the specific needs of your applications. Consider opting for AWS Managed Services, as they can help you with simplified database management, automated backups, and seamless scaling, ensuring optimal performance and efficiency for your workload. Also, look out for each service’s unique features and capabilities in the Amazon Aurora vs. RDS comparison, such as Aurora’s superior performance and scalability versus RDS’s broader database engine support and cost-effectiveness. By carefully assessing these factors, you can select the most suitable database service for your workload, ensuring optimal performance and efficiency.

Frequently Asked Questions (FAQs)

Aurora is a fully managed cloud-based database service by AWS that supports open-source databases. RDS makes managing relational databases like MySQL, PostgreSQL, SQL Server, MariaDB, Oracle, and Aurora easier.

You can move your current MySQL or PostgreSQL databases to Amazon Aurora using several methods, including Amazon Database Migration, native backup and restore processes, or other database migration tools.

Amazon Aurora continuously backs your database to Amazon S3 with up to 35 days of point-in-time recovery. It also supports manual snapshots and multi-AZ replication for high availability, ensuring automatic failover to a standby instance if needed.

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