AWS Batch vs. Google Cloud Batch: A Factual Comparison
In the world of cloud computing, masterfully architecting your workflows makes for a successful deployment. Cloud architecture is the foundation for modern application development and deployment.
In this blog post, we will dive deep into two popular cloud computing tools, AWS Batch and Google Cloud Batch, to understand which one is a better fit for your business.
Overview
AWS Batch and Google Cloud Batch both provide managed infrastructure for running batch computing workloads. These tools allow you to schedule, manage and monitor large-scale compute jobs in the cloud.
AWS Batch and Google Cloud Batch are best suited for scenarios that require periodic, large-scale workloads. AWS Batch runs batch computing workloads on the Amazon EC2 infrastructure, while Google Cloud Batch is part of the Google Cloud Platform's Compute Engine product suite.
But which one is better?
Performance and Scalability
Let's compare both platforms based on their performance and scalability.
AWS Batch provides autoscaling functionality where it increases the number of instances based on the job's demand.
Google Cloud Batch offers instant scalability with the Compute Engine auto-scaling feature, which allows instances to be added to a Google Cloud batch job in seconds.
According to a study conducted by AllCloud, AWS Batch scales up to 100 vCPUs with 16GB RAM within seconds, while Google Cloud Batch can spawn over 1,000 nodes in under 30 seconds. Moreover, Google Cloud's Compute Engine featuring custom machine types and sustained usage discounts integrate with Google Kubernetes Engine, creating powerful parallelization capabilities that can handle different types of workloads.
So, if scalability and performance are a significant concern, Google Cloud Batch is a better choice.
Cost
Cost is an essential factor when choosing between AWS Batch and Google Cloud Batch.
AWS Batch follows a per-hour billing system, which may not be suitable for scenarios where overall runtime is important. AWS Batch also charges extra for data transfer services and EC2 instances.
Google Cloud Batch follows a more flexible billing model that charges by the second with no minimum usage requirement. Additionally, every batch job of Google Cloud batch receives an automatic scheduled maintenance period orchestrated by the platform, thus reducing overhead costs.
Integration and ease of use
Neither AWS Batch nor Google Cloud Batch is easy to configure. However, thanks to its seamless integration with the powerful Google Cloud Platform, Google Cloud Batch outperforms AWS Batch when integrating with other cloud-based services like Stackdriver Monitoring, BigQuery, and Google Kubernetes Engine.
AWS Batch and Google Cloud Batch are built to complement and support cloud-native workflows. They integrate with the respective cloud providers' other services, including Amazon S3 by AWS and Google Bigtable by Google, to make batch job submissions easier.
Conclusion
Both AWS Batch and Google Cloud Batch offer unique features that make them comfortable for different scenarios. The choice depends on your specific use cases and overall goals. When deciding between AWS Batch and Google Cloud Batch, scale, cost, and integration play critical roles. While AWS Batch is better for specific scenarios, including if the organization is heavily invested in the AWS ecosystem, Google Cloud Batch provides a more flexible pricing solution, more powerful scalability system, and superior integration with Kubernetes and other Google Cloud Platform products.
Do you use AWS Batch, Google Cloud Batch, or both? Share your experience in the comments!