Introduction

When it comes to cloud databases, Amazon Web Services (AWS) provides two widely used options: AWS RDS and AWS Aurora. Both services are highly capable, offering a rich set of features and the ability to scale with your business needs. However, choosing between them can be challenging.

To help you make an informed decision, we’ll compare AWS RDS and AWS Aurora, examining their infrastructure, architecture, features, performance, and more. While AWS Aurora is technically a part of the Amazon RDS family, it offers unique enhancements and optimizations that differentiate it from other RDS database engines. By the end of this article, you’ll have a clearer understanding of which solution best fits your cloud-based data storage requirements.

What is Amazon RDS?

Amazon Relational Database Service (Amazon RDS) offers easy-to-manage, high-performance, and highly scalable solution for setting up, running, and optimizing relational databases in the AWS cloud.

Amazon RDS allows you to deploy and operate a database without the need to configure physical infrastructure or manage software manually.

Amazon RDS provides the flexibility to select, deploy, and scale from up to eight relational database engines of your choice:

• Amazon Aurora (Aurora MySQL-Compatible or Aurora PostgreSQL-Compatible)
• MySQL
• MariaDB
• PostgreSQL
• Oracle (supports deeper OS and database customization with RDS Custom)
• IBM Db2
• Microsoft SQL Server
• Amazon RDS on AWS Outposts (for on-premises deployments)

Overall, Amazon RDS offers numerous advantages, such as:

• Management – Amazon RDS handles critical database administration tasks such as provisioning, patching, and backups, allowing you to focus on core business operations.
• Support for Multiple Database Engines – It supports eight widely used database engines, giving you flexibility where and how you deploy your databases.
• High Availability and Durability – With built-in automated failover and backups, Amazon RDS ensures your data remains accessible even in case of failure.
• Scalability – Amazon RDS enables seamless capacity scaling, making it easy to expand or reduce resources as your business needs evolve.
• Security – Built-in encryption and access control mechanisms help safeguard your data against unauthorized access.
• Automated Backups – Regular backups, including Point-in-Time Recovery, ensure your data can be restored in case of loss.

What is Amazon Aurora?

Amazon Aurora is one of the database engines available within Amazon RDS. However, unlike traditional RDS database engines, Aurora is built as a cloud-native, high-performance, and highly scalable database service that remains fully compatible with MySQL and PostgreSQL.

AWS designed Amazon Aurora to offer significantly enhanced performance, supporting up to three times the number of read replicas (a total of 15) compared to standard RDS, as well as delivering five times the throughput of MySQL and three times that of PostgreSQL.

Below are advantages of Amazon Aurora that you’ll definitely want to know:

• Fully Managed – Just like Amazon RDS, Amazon Aurora is a fully managed relational database service that eliminates the burden of manual tasks such as provisioning, patching, and optimization, allowing you to focus on your core applications while enjoying superior performance and scalability.
• Faster Performance – Aurora can achieve up to three times the throughput of PostgreSQL and up to five times that of MySQL, all while maintaining compatibility with most existing applications. This makes it an excellent choice for workloads that demand high speed and scalability.
• Enhanced Durability – With built-in support for multiple Availability Zones, Aurora ensures reliable data backups and replication. Its architecture is optimized for high availability, low latency, and the ability to handle intensive workloads with automatic failover capabilities.
• Serverless Option – Aurora Serverless allows you to use computing resources on demand, scaling automatically based on workload requirements. This eliminates the need for manual capacity planning and ensures cost efficiency.
• Greater Scalability – Aurora enables rapid scaling both up and down to match the needs of your applications more efficiently than standard RDS databases.
• High Availability – Designed for maximum uptime, Amazon Aurora offers an industry-leading 99.999% availability SLA (Service Level Agreement).
• Security – Aurora includes security features such as encryption at rest and in transit. Amazon Aurora also supports Kerberos authentication, allowing users to authenticate connections to your Aurora MySQL and PostgreSQL clusters.

AWS RDS vs Aurora: Similarities

Both Amazon RDS and Amazon Aurora are engineered to simplify database management and reduce operational overhead through extensive automation and pre-configured environments. Here are the key similarities:

• Pre-Configured Environments:
  • Both services provide ready-to-deploy setups that allow you to quickly launch and run your applications with minimal manual configuration. This is particularly valuable for teams without dedicated DBAs, as much of the heavy lifting—from provisioning to configuration—is handled by AWS.
• Automated Maintenance and Patching:
  • Routine administrative tasks such as patching, upgrades, and software updates are automatically managed. With configurable maintenance windows, you can schedule these operations during off-peak hours, ensuring that updates occur with little or no downtime.
• Continuous and Incremental Backups:
  • Both RDS and Aurora continuously back up your data to Amazon S3 using an automated, incremental backup process. This feature not only ensures data durability and enables point-in-time recovery but also eliminates the need for complex backup scripts or manual scheduling—all without impacting performance.
• Centralized Monitoring and Management:
  • Integrated with Amazon CloudWatch, both services provide detailed performance metrics and alerts. This centralized monitoring aids in maintaining the health and efficiency of your databases, further reducing the administrative burden.

Together, these features ensure that whether you choose Amazon RDS or Amazon Aurora, you benefit from a streamlined, secure, and highly available database solution that lets you focus more on developing your application and less on managing the underlying infrastructure.

AWS RDS vs Aurora: Differences

Let’s now explore the differences between Amazon RDS and Amazon Aurora. Although both are fully managed AWS database solutions, they diverge significantly in design and capabilities. RDS follows a traditional model with integrated compute and storage, while Aurora is built cloud-native with a decoupled architecture that separates these components. In the sections below, we’ll highlight these key distinctions—including architecture, performance and scalability, and backup and replication mechanisms—to help you determine which solution is the best fit for your workload.

Architecture

Aurora Architecture

Amazon Aurora is engineered specifically for the cloud by decoupling compute from storage. Its architecture features a shared cluster volume that spans six storage nodes distributed across three AWS Availability Zones. This design provides several key benefits:

• Automatic Storage Scaling:
  • Aurora automatically adjusts storage capacity in 10‑GB increments—from a minimum of 10 GB up to 128 TiB—eliminating the need for manual provisioning and reducing operational overhead. In comparison, standard RDS engines for MySQL, MariaDB, Oracle, and PostgreSQL scale up to 64 TiB (with SQL Server capped at 16 TiB)
• Enhanced Multi-AZ Resilience:
  • With data replicated six ways across three Availability Zones, Aurora’s distributed storage layer ensures high durability and availability, making failover processes exceptionally fast and minimizing downtime.
• Optimized Compute and Replication:
  • Compute nodes in Aurora process queries and interface with the shared storage. Because replication happens entirely within the storage layer, there is no need for synchronous replication between compute nodes. This approach not only boosts performance but also streamlines failover, as compute instances remain stateless and can be replaced without data loss.

RDS Architecture

Amazon RDS, on the other hand, follows a more conventional model where compute and storage are integrated within each database node. Key characteristics include:

• Traditional Instance Model:
  • RDS runs database engines on dedicated EC2 instances, with storage typically provided by Amazon EBS volumes. This model simplifies the setup by automating many administrative tasks—such as provisioning, patching, and backups—directly on these instances.
• Wide Database Engine Support:
  • While Aurora is limited to MySQL- and PostgreSQL-compatible engines, RDS supports a broader range of options, including MySQL, PostgreSQL, MariaDB, Oracle, and Microsoft SQL Server. Additionally, RDS extends its capabilities beyond the cloud with solutions like Amazon RDS on AWS Outposts for on-premises deployments, and it integrates seamlessly with AWS Database Migration Service (DMS) for moving legacy systems to the cloud.
• Simplified Management:
  • Even though RDS does not feature the cloud-native separation of compute and storage, it still delivers significant operational simplicity through automated backups, patching, and scaling options tailored to traditional workloads.

Performance and Scalability

Amazon RDS

Amazon RDS is designed to deliver robust performance with a wide variety of instance types, supporting configurations up to 32 vCPUs and 244 GiB of memory. This flexibility allows you to tailor the infrastructure to your specific workload requirements. Key performance features include:

• SSD-Backed Storage:
  • RDS uses SSD-backed storage options such as General Purpose (gp2/gp3) and Provisioned IOPS (io1/io2), which help ensure consistent IOPS performance based on the needs of your application.
• Independent Scaling:
  • For many workloads, you can scale CPU, memory, IOPS, and storage independently. However, note that when running RDS on AWS Outposts for on-premises deployments, these components are scaled as a bundle, which limits flexibility.
• Replica Limitations:
  • In RDS, each new read replica must copy the entire dataset from the primary before it can start processing queries. This process not only introduces latency but also generates additional replica traffic that can reduce throughput. To mitigate these challenges, RDS limits you to five read replicas per source instance, helping to manage the replication load efficiently.

Amazon Aurora

Amazon Aurora takes a cloud-native approach that emphasizes high performance and seamless scalability:

• Decoupled Compute and Storage:
  • Aurora’s architecture separates compute from storage, using a shared cluster volume that automatically scales in 10 GB increments from a minimum of 10 GB up to 128 TiB. This means that as your data grows, storage is provisioned dynamically without any disruption to compute resources.
• Efficient Read Replication:
  • Aurora’s design allows you to provision up to 15 read replicas that tap directly into the shared storage. Since new replicas connect to the pre-existing data without needing to copy it, replication is nearly instantaneous—with replication lag typically measured in milliseconds even under heavy write loads.
• Serverless Elasticity:
  • Aurora Serverless (especially in its v2 iteration) dynamically adjusts compute capacity on demand. This on-demand scaling ensures that you only pay for what you use, making it ideal for applications with fluctuating workloads.
• Optimized Write Path:
  • Aurora minimizes write amplification by sending only the redo log to the remote storage service rather than duplicating full data pages (avoiding the double-write penalty common in traditional systems). This improves both write throughput and overall efficiency.

Data Backups and Replication

• Replication:
  • Aurora:
    • Supports up to 15 replicas.
    • All replicas share the same underlying cluster volume, meaning any update made on the primary is instantly available to all replicas—often within milliseconds.
    • This shared-storage design eliminates the need to copy data separately to each replica, greatly enhancing replication efficiency.
  • RDS:
    • Supports up to 5 read replicas.
    • In RDS MySQL, each replica must first copy the entire dataset before it can serve queries, which can introduce significant latency and increase replica traffic, potentially reducing overall throughput.
• Failover:
  • Aurora:
    • Automatic failover is built into the service, ensuring that if the primary instance fails, one of the replicas is quickly and seamlessly promoted—with no data loss.
    • You can configure failover priorities for your replicas, giving you control over which replica takes over during a failure.
    • Aurora also supports Multi-AZ backups, which further enhance recovery times and data protection by leveraging its distributed storage system.
  • RDS:
    • Failover setup in RDS is generally manual (unless using Multi-AZ deployments).
    • In a Multi-AZ configuration, RDS synchronously replicates the primary’s data to a standby instance across another Availability Zone, ensuring high availability.
    • While the standby can serve read traffic, failover for the primary must be triggered and may involve slightly higher latency due to the need to replace the failed compute node.
• Backups:
  • Both Aurora and RDS support automated backups and Point-in-Time Recovery (PITR) to restore your database to any moment within the configured retention period.
  • RDS:
    • Automated backups and manual snapshots are stored in Amazon S3.
    • Backups are typically user-initiated and retained until you explicitly delete them.
  • Aurora:
    • Continuously and incrementally backs up the entire cluster volume without interrupting database operations.
    • Enables PITR for up to 35 days (or as configured), allowing restoration to any specific second within that period—even recovering data as recent as the last five minutes.
    • Offers the unique ability to backtrack—rewinding the database to a previous state without the need to restore from a backup snapshot—by continuously scanning and self-healing its storage.

Aurora’s advanced backup and replication capabilities—featuring continuous, incremental backups and near-instant failover—make it the preferred choice for mission-critical applications that demand minimal downtime and uncompromised data integrity. Meanwhile, Amazon RDS offers a robust, versatile solution that reliably supports a wide range of traditional workloads with comprehensive management features.

Security

Both Amazon Aurora and Amazon RDS incorporate a robust, multi-layered security model designed to protect your data throughout its lifecycle. Here are the key security features common to both services:

• Access Control:
  • You can manage who logs in to your database by using methods such as AWS Identity and Access Management (IAM) policies and IAM database authentication. This allows you to define precise permissions and roles for database management tasks.
• Network Isolation:
  • By launching your DB instances within an Amazon VPC, you ensure that your databases reside in a logically isolated section of the AWS Cloud. This setup allows you to control network access via security groups—firewall-like rules that permit or deny inbound and outbound traffic based on IP addresses or EC2 instance IDs.
• Encryption in Transit:
  • Secure Socket Layer (SSL) and Transport Layer Security (TLS) protocols can be enabled to encrypt data as it moves between your applications and the database. This is available for DB engines such as MySQL, PostgreSQL, MariaDB, Oracle, and Microsoft SQL Server.
• Enhanced Authentication Options:
  • In addition to IAM-based authentication, Amazon Aurora offers Kerberos authentication, which is particularly useful for enterprise environments that rely on centralized authentication systems.
• Encryption at Rest:
  • Both RDS and Aurora support encryption of data stored in DB instances and snapshots using AWS Key Management Service (KMS). This ensures that your data remains secure even if physical storage media are compromised.

Together, these features form a comprehensive security framework that helps protect your databases from unauthorized access, ensures data privacy, and supports compliance with regulatory standards.

Monitoring

Both Amazon RDS and Amazon Aurora integrate seamlessly with Amazon CloudWatch to provide you with real-time monitoring and alerting. However, Aurora’s cloud-native design offers a higher level of granularity and additional insights compared to traditional RDS setups.

Granularity of Metrics

• Common Metrics:

Both services track essential performance indicators such as CPU utilization, memory usage, storage space, and I/O activity.

• Aurora-Enhanced Metrics:

Aurora goes further by providing cluster-level metrics that give you a deeper understanding of overall performance. Examples include:

• AuroraReplicaLag: Monitors the replication lag across all nodes, ensuring you can quickly detect and address delays.
• BufferCacheHitRatio: Measures how often data is served from memory rather than disk, helping you gauge caching effectiveness.
• VolumeWriteIOPs: Offers insights into the efficiency of storage operations, allowing you to optimize I/O performance.

Logging Capabilities

Both Amazon Aurora and Amazon RDS support an extensive range of logging features that are essential for monitoring performance, troubleshooting issues, and ensuring compliance. These capabilities include:

• ‍General Logs: Capture comprehensive details on user activity and query execution.‍
• Slow Query Logs: Identify queries that take longer than expected, which is crucial for performance tuning.‍
• Audit Logs: Provide detailed records for security tracking and compliance.‍
• Error Logs: Quickly pinpoint system or query errors to facilitate effective troubleshooting.

For more detailed information on monitoring capabilities, see the AWS Aurora Monitoring Overview .

While both services offer robust logging options, Aurora’s cloud-native architecture can deliver enhanced granularity and additional cluster-level insights. This extra depth may benefit in-depth performance analysis and compliance monitoring, though RDS’s logging features remain fully capable for traditional workloads.

Pricing

When planning your AWS database deployments, understanding the pricing differences between Amazon Aurora and Amazon RDS is crucial.

• Instance Cost Differences:
  • Aurora instances typically cost about 20% more than equivalent RDS MySQL instances. Moreover, if you choose to add Aurora read replicas, the overall cost of your Aurora cluster can potentially double. Keep in mind that Aurora is available only on specific RDS instance sizes, so it’s important to check the pricing details on the Aurora Pricing Page and the RDS MySQL Pricing Page.
• Storage Pricing: Storage pricing structures differ between the two services.
  • For RDS MySQL, you must manually select the type and size of your EBS volume, ensuring that the provisioned EBS IOPS align with the capabilities of your chosen instance type. Failing to optimize this can result in underutilized or overprovisioned IOPS.
  • In Aurora, IOPS are limited solely by the instance type. This means that to boost IOPS performance, you generally need to upgrade the instance type. Additionally, Aurora charges based on the actual data used in 10GB increments.
• Billing Model:
  • Aurora charges are calculated at $0.10 per GB-month for storage and $0.20 per 1 million requests, reflecting the dynamic nature of its auto-scaling storage. This pay-for-what-you-use approach can lead to higher costs under heavy workloads. In contrast, RDS MySQL’s storage costs depend on the chosen EBS type and size, often offering a more predictable pricing model for steady workloads.

For more insights on managing database costs, you can check our blog on RDS Pricing here.

Track Your RDS and Aurora Databases with Cloudchipr

Once you begin using Amazon RDS and Aurora, it’s crucial to keep a close watch on your database usage and associated costs. Cloudchipr provides a user-friendly dashboard with real-time cost analytics, giving you precise insights into how much each of your managed database instances—whether RDS or Aurora—is costing you.

Within the Live Usage & Management section, you’ll receive a comprehensive overview of your databases, including essential details such as database type, status, node count, encryption status, and utilization metrics. Additionally, Cloudchipr’s Commitments feature lets you track the utilization and spending on Reserved Instances if you’ve chosen that payment model for your databases. Plus, you can set up Automation Workflows to stop databases in your development environment when they aren’t needed, reducing unnecessary costs.

With these tools and automated alerts, Cloudchipr empowers you to identify cost-saving opportunities, optimize resource allocation, and maintain full control over your AWS database environment across both RDS and Aurora.

Start your 14-day free trial today—explore its features and experience the difference for yourself!

Conclusion

Both Amazon RDS and Amazon Aurora offer robust, fully managed database solutions on AWS—but they cater to different needs and priorities. Amazon RDS stands out for its versatility, supporting a wide range of database engines and offering flexible, predictable pricing that works well for cost-sensitive or less demanding workloads. On the other hand, Amazon Aurora is engineered for high performance and scalability, boasting cloud-native features like decoupled compute and storage, rapid replica provisioning, and near-instant replication. This makes Aurora an excellent choice for mission-critical, high-transaction applications that demand low latency and seamless scalability.

Ultimately, the best choice depends on your specific workload, performance requirements, and budget. Evaluate your needs carefully—if you require a diverse set of database engines and granular control over instance configurations, RDS might be the right fit. However, if your goal is to achieve exceptional throughput, minimal replication lag, and effortless scaling, Aurora’s advanced architecture could provide the competitive edge you need.

Tools like Cloudchipr can further simplify this decision by offering real-time cost analytics, tracking Reserved Instance commitments, and automating workflows to optimize your environment. With these insights, you can ensure your AWS database infrastructure remains both robust and cost-effective as your business grows.

No matter which solution you choose, you’re well-equipped to leverage AWS’s powerful database services to drive your business forward in the cloud era.