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  1. Stackups
  2. Application & Data
  3. Relational Databases
  4. SQL Database As A Service
  5. Google Cloud SQL vs Heroku Postgres

Google Cloud SQL vs Heroku Postgres

OverviewDecisionsComparisonAlternatives

Overview

Google Cloud SQL
Google Cloud SQL
Stacks555
Followers580
Votes46
Heroku Postgres
Heroku Postgres
Stacks607
Followers314
Votes38

Google Cloud SQL vs Heroku Postgres: What are the differences?

Introduction: In this article, we will compare Google Cloud SQL and Heroku Postgres - two popular database services. We will outline the key differences between them, focusing on specific aspects.

  1. Pricing Models: Google Cloud SQL offers various pricing models, including pay-as-you-go and commitment-based plans, allowing users to choose the most suitable option. On the other hand, Heroku Postgres primarily offers a pay-as-you-go pricing model, with different tiers based on the desired features and performance.

  2. Infrastructure Management: Google Cloud SQL handles the management of the underlying infrastructure, including scaling, backups, and replication, relieving the users from this responsibility. In contrast, Heroku Postgres requires users to manage the infrastructure themselves, offering more control but also more administrative overhead.

  3. Integration with Ecosystem: Google Cloud SQL seamlessly integrates with other Google Cloud Platform services, such as Compute Engine and App Engine, enabling easy data access and utilization. Heroku Postgres, being a part of the Heroku platform, integrates well with Heroku's ecosystem, which includes seamless deployment, scaling, and monitoring of applications.

  4. Scaling Options: With Google Cloud SQL, users can easily scale their databases vertically by adjusting the machine type, as well as horizontally by utilizing read replicas. Heroku Postgres also supports vertical scaling but has limited options for horizontal scaling, making it more suitable for smaller or medium-sized applications.

  5. Data Insights and Analysis: Google Cloud SQL provides integration with Google's BigQuery, a powerful analytics data warehouse, allowing for advanced data analysis and insights. Heroku Postgres, while offering basic data analysis capabilities, may require additional configurations and tools to perform complex analytics tasks.

  6. High Availability and Disaster Recovery: Google Cloud SQL offers built-in high availability through automatic failover and replication to multiple zones, ensuring data durability and continuous operation. Heroku Postgres also provides high availability options but may require additional setup and configuration to achieve the same level of robustness.

In Summary, Google Cloud SQL and Heroku Postgres differ in terms of pricing models, infrastructure management, ecosystem integration, scaling options, data analysis capabilities, and high availability features. The choice between them depends on specific requirements, such as budget, control, application size, and analytical needs.

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Advice on Google Cloud SQL, Heroku Postgres

Jorge
Jorge

Jan 15, 2020

Needs advice

Considering moving part of our PostgreSQL database infrastructure to the cloud, however, not quite sure between AWS, Heroku, Azure and Google cloud. Things to consider: The main reason is for backing up and centralize all our data in the cloud. With that in mind the main elements are: -Pricing for storage. -Small team. -No need for high throughput. -Support for docker swarm and Kubernetes.

51.8k views51.8k
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Detailed Comparison

Google Cloud SQL
Google Cloud SQL
Heroku Postgres
Heroku Postgres

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.

Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed
High Availability;Rollback;Dataclips;Automated Health Checks
Statistics
Stacks
555
Stacks
607
Followers
580
Followers
314
Votes
46
Votes
38
Pros & Cons
Pros
  • 13
    Fully managed
  • 10
    SQL
  • 10
    Backed by Google
  • 4
    Flexible
  • 3
    Encryption at rest and transit
Pros
  • 29
    Easy to setup
  • 3
    Dataclips for sharing queries
  • 3
    Extremely reliable
  • 3
    Follower databases
Cons
  • 2
    Super expensive
Integrations
No integrations available
PostgreSQL
PostgreSQL
Heroku
Heroku

What are some alternatives to Google Cloud SQL, Heroku Postgres?

Amazon RDS

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Amazon Aurora

Amazon Aurora

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

Amazon RDS for PostgreSQL

Amazon RDS for PostgreSQL

Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades, storage management, replication for high availability and back-ups for disaster recovery. With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale from 10GB to 3TB of storage and from 1,000 IOPS to 30,000 IOPS.

ClearDB

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

ElephantSQL

ElephantSQL

ElephantSQL hosts PostgreSQL on Amazon EC2 in multiple regions and availability zones. The servers are continuously transferring the Write-Ahead-Log (the transaction log) to S3 for maximum reliability.

Azure SQL Database

Azure SQL Database

It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

Database Labs

Database Labs

We manage an optimized Postgres image. You focus on your core app, not on becoming a database administrator.

Cloud DB for Mysql

Cloud DB for Mysql

It is a fully managed cloud cache service that enables you to easily configure a MySQL database with a few settings and clicks and operate it reliably with NAVER's optimization settings, and that automatically recovers from failures.

Google Cloud SQL for PostgreSQL

Google Cloud SQL for PostgreSQL

With Cloud SQL for PostgreSQL, you can spend less time on your database operations and more time on your applications.

PlanetScaleDB

PlanetScaleDB

It is a fully managed cloud native database-as-a-service built on Vitess and Kubernetes. A MySQL compatible highly scalable database. Effortlessly deploy, manage, and monitor your databases in multiple regions and across cloud providers.

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