2025 SQL Performance Comparison: AWS RDS vs Google Cloud

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Key Features Comparison for 2025

Explore the differences between AWS RDS and Google Cloud SQL.

Organizations in 2025 often choose aws rds google cloud sql based on their unique requirements. Amazon web services offers amazon rds for those seeking a high-performance database service with rich querying power, automated administration, and robust scalability. Google cloud sql, preferred by teams building applications inside the google ecosystem, provides seamless integration with gcp cloudsql and excels in reliability. Key differences in database service comparison include engine support, automation, and automatic failover and replication. Decision makers rely on cloud provider comparison to match managed relational database services to their highly available needs. Real-world scenarios highlight cloudsql as a managed service for developers focused on cloud comparisons and database services that power a relational database in the cloud.

Overview

aws rds

Amazon Web Services offers aws rds as a fully managed relational database service. This managed database service supports a wide range of database engines, including Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and SQL Server. Users benefit from automated backups, point-in-time recovery, and Multi-AZ deployments for highly available applications. RDS integrates deeply with aws services such as EC2, Lambda, Redshift, and CloudWatch. The service provides advanced automation features, including support for Terraform, Ansible, and CloudFormation, which streamline DevOps workflows. Organizations choose aws rds for its mature ecosystem, robust security, and seamless scalability.

google cloud sql

Google cloud sql stands out as a fully managed relational database service within the google ecosystem. This managed service supports MySQL, PostgreSQL, and SQL Server, making it suitable for many cloud-native applications. Google cloud sql offers automated backups, asynchronous replication, and high availability configurations. Integration with gcp cloudsql enables smooth connectivity to Compute Engine, Kubernetes Engine, and BigQuery. The service provides management and monitoring tools through Cloud Monitoring and the Cloud Console. Teams building on google value cloudsql for its reliability, ease of use, and strong integration with other google cloud services.

Key Features

The following table compares the core features of aws rds and google cloud sql, highlighting their strengths as managed database services:

Both aws rds and google cloud sql deliver fully managed relational database service experiences. AWS rds offers broader engine support and deeper automation for DevOps teams. Google cloud sql excels in integration with google cloud and provides reliable options for cloud-native database services. Organizations should evaluate their needs for engine support, automation, and integration when choosing between these relational database services.

Supported Engines

aws rds engine options

Amazon Web Services delivers aws rds as a versatile database service. The platform supports a wide range of database engines, making it suitable for many types of applications. Users can choose from MySQL, PostgreSQL, MariaDB, Amazon Aurora, SQL Server, Oracle, and IBM Db2. This broad engine support allows organizations to select the best fit for their workloads. RDS provides flexibility for developers who need to migrate legacy systems or build new cloud-native solutions. The service also enables seamless integration with other aws offerings, which enhances the overall experience for teams managing complex environments.

google cloud sql engine options

Google cloud sql focuses on simplicity and reliability for cloud-native applications. The service supports three major database engines: PostgreSQL, MySQL, and SQL Server. These engines cover most use cases for modern web and mobile applications. Google cloud sql integrates tightly with gcp cloudsql, making it easy for developers to connect their databases to other google cloud services. The platform offers automated management and monitoring, which reduces operational overhead. Teams building on google benefit from a streamlined experience and consistent performance.

Comparison Table

The following table highlights the supported database engines for aws rds and google cloud sql. This comparison helps organizations understand which service aligns with their technical requirements.

AWS RDS stands out with its support for Oracle and MariaDB, which google cloud sql does not offer. This difference matters for organizations with legacy systems or specialized workloads. RDS provides more options for database services, while cloudsql focuses on the most popular engines for cloud-native applications.

Organizations comparing aws rds google cloud sql should consider engine support as a key factor. AWS offers a wider selection, which benefits teams with diverse requirements. Google cloud sql delivers a focused set of engines, ideal for streamlined deployments and integration with google services. Both database services provide robust options for relational database services, but the choice depends on the specific needs of the applications.

Performance

Scalability

AWS RDS delivers strong scalability for organizations that run demanding applications. The service supports automatic scaling, allowing teams to adjust resources as workloads grow. RDS enables horizontal scaling through read replicas and vertical scaling by upgrading instance types. Multi-AZ deployments in AWS RDS provide high availability and support seamless failover, which keeps applications running smoothly during peak times. AWS also offers Graviton3-based instances, which improve performance and efficiency for database workloads.

Google Cloud SQL focuses on consistent scaling for web and mobile applications. The platform provides vertical scaling by resizing machine types and horizontal scaling through read replicas. Google Cloud SQL integrates with GCP CloudSQL, making it easy for developers to scale databases as their needs change. The service offers high availability configurations, which help maintain reliability during traffic spikes. Teams building on Google benefit from predictable performance and streamlined scaling options.

Tip: Organizations should evaluate their expected workload growth and choose a service that matches their scaling requirements.

Benchmarks

Recent benchmarks highlight differences in performance between AWS RDS and Google Cloud SQL. AWS RDS for PostgreSQL and MySQL optimizes read and write operations, which supports demanding applications that require fast data processing. RDS uses advanced features like Multi-AZ deployments and Graviton3 instances to deliver high throughput and low latency.

Google Cloud SQL maintains consistent performance for web and mobile applications. The platform focuses on reliability and predictable response times, which suits cloud-native workloads. CloudSQL offers automated management and monitoring, helping teams maintain optimal performance without manual intervention.

The following table summarizes key performance aspects:

Organizations comparing relational database services should consider both scalability and performance benchmarks. AWS RDS excels in optimization for demanding database services, while Google Cloud SQL provides reliable scaling for cloud-native applications.

Availability

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High Availability

Organizations depend on high availability to keep their applications running without interruption. AWS RDS and Google Cloud SQL both deliver robust solutions, but they use different approaches. AWS RDS uses synchronous replication, which relies on DRBD technology. Google Cloud SQL employs semisynchronous replication. Both database services offer automatic failover, ensuring that if the primary instance fails, a standby takes over quickly.

The following table compares high availability features:

AWS RDS provides strong consistency with synchronous replication, which benefits critical applications. Google Cloud SQL allows read scaling from failover replicas, supporting workloads that need extra read capacity. Both services drop connections during failover, but they quickly create new replicas to restore service.

Tip: Teams should assess their application’s tolerance for downtime and choose the database service that matches their availability needs.

Backup and Recovery

Reliable backup and recovery features protect data from loss and support business continuity. AWS RDS and Google Cloud SQL both offer automated and manual backups. AWS RDS stands out with point-in-time recovery and configurable retention periods. Google Cloud SQL supports automated and manual backups, but it does not provide point-in-time recovery.

Here is a comparison of backup and recovery features:

AWS RDS enables organizations to restore databases to any specific time, which helps recover from accidental changes. Google Cloud SQL offers reliable backup options, but teams cannot restore to a precise moment. Both relational database services support manual backups for added control.

Note: Regular backups and clear recovery plans help organizations maintain data integrity and minimize downtime.

Security

Encryption

Encryption plays a critical role in protecting sensitive data for organizations using relational database services. Both aws rds and google cloud sql offer robust encryption options, but they implement these features differently.

aws rds secures data at rest using the AES-256 algorithm, with key management handled by AWS Key Management Service (KMS). Data in transit is protected through SSL connections, ensuring that information remains confidential as it moves between applications and databases. rds also supports encryption for backups and snapshots, providing comprehensive coverage for all stored data.

google cloud sql encrypts data at rest using customer-managed keys, giving organizations more control over their encryption strategy. This approach allows teams to align security policies with internal requirements. Encryption in transit is also available, safeguarding data as it travels between cloudsql and connected services.

Tip: Teams should evaluate their compliance needs and internal policies before choosing an encryption strategy.

Compliance

Compliance with industry regulations is essential for organizations handling sensitive information. aws rds and google cloud sql both address major standards, but their approaches differ.

• aws rds uses identity and access controls through IAM policies and multi-factor authentication.

• Data encryption is enforced at rest and in transit using AWS KMS and TLS/SSL.

• Observing and logging are supported by AWS CloudTrail and CloudWatch, which help organizations meet audit requirements.

• Data storage and retention policies are managed through S3 bucket policies and automated retention settings.

• Regular audits and assessments are conducted using AWS Artifact and third-party evaluations.

google cloud sql supports compliance for GDPR and HIPAA by offering encryption, access controls, and audit logging. Integration with google services helps organizations maintain consistent security practices across their cloud environment.

Organizations should compare the compliance features of aws rds and google cloud sql to ensure their chosen relational database services meet regulatory requirements for their applications.

Integration

gcp cloudsql ecosystem

Google Cloud SQL integrates seamlessly with the Google Cloud ecosystem, supporting applications that rely on cloud-native services. Teams use Cloud Monitoring and Cloud Console to manage and monitor databases. Integration with other services such as Compute Engine, Kubernetes Engine, and BigQuery enables developers to build scalable solutions. CloudSQL supports ETL tools that help extract, transform, and load data efficiently. Popular tools include Datastream, which allows near real-time data synchronization between Google Cloud SQL and external databases. Migration from AWS RDS to Google Cloud SQL requires careful planning. Teams must ensure MySQL version compatibility, enable binary logs, and configure firewall settings for internal IP access. The GCP Data Migration Service (DMS) simplifies the migration process, covering both data and metadata with minimal downtime.

Tip: Choosing the right machine tier for the initial data dump phase can improve migration speed and reliability.

aws integration

AWS RDS offers deep integration with the AWS ecosystem, making it a preferred choice for organizations that use multiple AWS services. RDS connects with EC2, Lambda, Redshift, and CloudWatch, supporting a wide range of applications. Integration with other services is straightforward, allowing teams to automate database management and monitoring. ETL tools such as Integrate.io, AWS Glue, and Talend provide user-friendly interfaces and pre-built integrations for efficient data transformation. Migration and hybrid cloud options are robust. For PostgreSQL databases, the pglogical extension enables migration from Google Cloud SQL to AWS RDS and Aurora PostgreSQL, supporting versions 9.4 and higher. Hybrid cloud deployments use site-to-site VPN connections. Teams create a VPC in AWS, set up subnets in different availability zones, and establish a Virtual Private Gateway. They then configure the customer gateway device for GCP VPN, enabling secure communication between AWS and Google environments.

Organizations compare AWS RDS and Google Cloud SQL based on integration with other services, migration flexibility, and hybrid cloud capabilities. Both relational database services support modern applications, but the choice depends on the cloud ecosystem and specific integration needs.

Management

Automation

Automation plays a crucial role in managing modern database services. Both aws rds and google cloud sql offer a suite of automation features that simplify daily operations for teams. These features help organizations reduce manual intervention and minimize the risk of human error.

The following table highlights key automation capabilities for each service:

AWS rds automates backups, software installation, and failover processes. Teams can schedule instance updates and rely on the platform to handle routine maintenance. Google cloud sql also provides automated backups and manages failover events, ensuring high availability for applications. Both services deliver a fully managed experience, allowing developers to focus on building features rather than managing infrastructure.

Tip: Automation reduces operational overhead and helps maintain consistent performance for mission-critical workloads.

Monitoring

Effective monitoring ensures that database services remain healthy and performant. AWS rds and google cloud sql both offer native cloud monitoring solutions, but their approaches differ in usability and depth.

AWS provides rds users with detailed performance insights and configurable backup retention. The platform supports point-in-time restore, which allows teams to recover data quickly. Google cloud sql offers similar monitoring features, but some tasks, such as point-in-time restore, may require more steps. Both services support automatic backups and allow organizations to adjust retention periods based on their needs.

Cloudsql integrates with google’s monitoring tools, making it easy for teams to track database health across their cloud environment. AWS rds connects with AWS CloudWatch, providing real-time metrics and alerts. These monitoring tools help organizations detect issues early and maintain reliable applications.

Pricing

Pricing Models

Organizations evaluating aws rds and google cloud sql must understand the pricing models before making a decision. Both services use a pay-as-you-go approach, but the details differ based on features and usage patterns. The following table outlines the main pricing components for each service:

AWS rds offers a wide range of instance and storage types, which allows organizations to tailor costs to their workloads. Google cloud sql provides similar flexibility, but the available options may differ. Multi-AZ deployments improve uptime for both services, but they also increase costs. Backup storage, data transfer, and additional services can add to the total price, so teams should review these components carefully.

Cost Scenarios

When comparing aws rds and google cloud sql, real-world cost scenarios help organizations estimate expenses for their applications. The following points highlight typical costs for similar workloads:

• AWS rds (MySQL, db.t3.medium) starts at $0.068 per hour.

• Google cloud sql (MySQL, db-n1-standard-1) starts at $0.0685 per hour.

• Estimated monthly costs:

  • AWS total: $1,354.12

  • GCP total: $1,535.20

  • Cost difference: AWS is approximately $181.08 less expensive than GCP for the given workloads.

AWS provides a slightly lower starting price for comparable instances. Over a month, aws rds can offer significant savings for organizations running similar workloads. Google cloud sql remains competitive, especially for teams already invested in the google ecosystem. Cloudsql users should consider integration benefits alongside pricing. Teams must analyze their usage patterns, required features, and expected growth to choose the most cost-effective solution for their needs.

Note: Pricing can change based on region, instance type, and additional features. Always use the latest calculators from aws and google for accurate estimates.

Pros and Cons

aws rds pros and cons

Amazon Web Services delivers aws rds as a robust managed database solution. Organizations often select rds for its wide range of features and seamless integration with aws services. The platform supports multiple database engines, including Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and SQL Server. Teams benefit from automated backups, disaster recovery, and performance metrics that simplify database management.

Pros of aws rds include reduced infrastructure management, automatic backups and patching, and disaster recovery. The service integrates smoothly with aws, allowing organizations to focus on building applications rather than maintaining databases. However, rds presents some limitations. Users have restricted control over the operating system and lack root access. Certain operations require downtime, and there is no built-in auto-tuning or performance guarantees. Advanced features may increase costs, and scalability can be limited for large workloads.

Note: Multi-AZ deployments in rds help address single points of failure, but teams should plan for scaling strategies.

google cloud sql pros and cons

Google cloud sql provides a fully managed relational database service within the google ecosystem. The platform excels in reliability and high availability, making it a strong choice for cloud-native applications. Cloudsql offers automated backups and monitoring, which reduce operational overhead for development teams.

Google cloud sql simplifies database management with automated processes and strong integration with google cloud services. Teams benefit from scalability and high availability, which support growing applications. The service, however, supports fewer database engines compared to aws rds. Organizations may face vendor lock-in, and costs can rise as workloads expand. Cloudsql remains ideal for teams invested in the google ecosystem, but those requiring broader engine support may prefer aws rds.

Tip: Evaluate long-term needs for database engine flexibility and integration before choosing between aws rds google cloud sql.

Use Cases

When to Choose aws rds

Organizations often select aws rds when they require a managed relational database service that supports a wide range of engines. Enterprises use rds for MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Amazon Aurora. This flexibility allows teams to migrate legacy systems or build new applications without changing their preferred database technology.

Businesses deploy rds to launch new database servers quickly. They enhance reliability and uptime for transactional workloads, such as online transaction processing (OLTP) systems. Teams customize configurations for different engines and set up automatic failover and backups. This approach reduces downtime and protects critical data.

Many companies integrate rds with NoSQL databases to support low-latency, high-traffic scenarios. For example, an e-commerce platform may use rds for order management and DynamoDB for session storage. This combination delivers speed and reliability.

AWS rds offers significant scalability. Enterprises adjust compute and storage resources with minimal downtime. This capability is essential for organizations facing variable workloads or rapid growth. They handle unexpected traffic spikes without service interruption.

Common use cases for aws rds include:

• Running mission-critical applications that require high availability and disaster recovery.

• Migrating on-premises databases to the cloud for easier management and cost savings.

• Supporting analytics workloads by integrating rds with data warehousing solutions.

• Building web and mobile applications that need reliable, scalable databases.

Tip: Teams seeking broad engine support, advanced automation, and seamless integration with aws services often choose rds for their database needs.

When to Choose google cloud sql

Google cloud sql fits organizations that build applications inside the google ecosystem. Teams use cloudsql for MySQL, PostgreSQL, and SQL Server databases. This managed service simplifies setup and management, allowing developers to focus on building features rather than maintaining infrastructure.

Cloudsql provides a cloud-based alternative to local databases. Developers use it to avoid the complexity of managing hardware and software updates. Many applications running on google cloud services leverage cloudsql for their database storage.

The following table highlights common enterprise use cases for google cloud sql:

Teams often choose google cloud sql for microservices architectures. They benefit from decentralized data management and easy scaling. Cloudsql integrates with Compute Engine, Kubernetes Engine, and BigQuery, supporting modern cloud-native applications.

Organizations select google cloud sql when they need:

• Reliable database storage for applications built on google cloud.

• Simplified management and automated backups.

• Seamless integration with other google services.

• A managed solution for web and mobile applications that require consistent performance.

Note: Developers who prioritize ease of use, strong integration with google cloud, and reliable scaling often prefer cloudsql for their projects.

Industry experts highlight key differences between aws rds and google cloud sql:

• aws rds supports more database engines and integrates with aws services.

• google cloud sql emphasizes high availability and seamless scalability for cloudsql users.

Cloud solution architects recommend these next steps:

1. Deploy independent instances for each application part.

2. Implement primary-secondary setups for disaster recovery.

3. Evaluate active-active clusters if needed.

4. Establish secure networking and synchronize database versions.

5. Use automation and unified monitoring tools.

Organizations should assess integration, cost, and scalability before choosing google or aws.

Recommended reading

• SQL Optimization Course
• SQL optimization knowledge
• Database industry knowledge

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