AWS Batch vs Google Cloud Functions

Overview

Google Cloud Functions

Stacks478

Followers479

Votes25

AWS Batch

Stacks84

Followers251

Votes6

AWS Batch vs Google Cloud Functions: What are the differences?

Pricing: AWS Batch has a pay-as-you-go pricing model where you pay for the compute resources used to run your batch jobs. In contrast, Google Cloud Functions follow a pay-per-use pricing structure, billing you only for the time your functions are executing.
Service Integration: AWS Batch is tightly integrated with other AWS services such as EC2, S3, and IAM, allowing seamless interaction between different AWS resources. On the other hand, Google Cloud Functions are more oriented towards event-driven computing and integrate well with other Google Cloud Platform services like Cloud Storage and Pub/Sub.
Scaling: AWS Batch allows for managing and scaling batch computing workloads efficiently by supporting job queues and compute environments. In comparison, Google Cloud Functions are serverless and automatically scale based on the incoming requests, eliminating the need for manual scaling configurations.
Environment Flexibility: AWS Batch provides flexibility in choosing the compute resources for running batch jobs, supporting both on-demand and spot instances. Conversely, Google Cloud Functions are limited to running on predefined environments provided by Google Cloud Platform, without the ability to customize the underlying infrastructure.
Deployment Process: AWS Batch focuses on running long-running batch processes or jobs, making it suitable for scenarios where tasks require extended processing times. In contrast, Google Cloud Functions are designed for short-lived, event-driven functions that respond to specific triggers or events, enabling rapid development and deployment of small pieces of code.
Monitoring and Management: AWS Batch offers comprehensive monitoring and management features through Amazon CloudWatch, allowing users to track job status, resource utilization, and performance metrics. Google Cloud Functions leverage Stackdriver Logging and Monitoring for similar purposes, providing visibility into function execution and system performance.

In Summary, AWS Batch and Google Cloud Functions differ in pricing, service integration, scaling capabilities, environment flexibility, deployment processes, and monitoring and management solutions.

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CLI (Node.js)

Manual

Advice on Google Cloud Functions, AWS Batch

Clifford

Software Engineer at Bidvest Advisory Services

Mar 28, 2020

Decided

Run cloud service containers instead of cloud-native services

Running containers means that your microservices are not "cooked" into a cloud provider's architecture.
Moving from one cloud to the next means that you simply spin up new instances of your containers in the new cloud using that cloud's container service.
Start redirecting your traffic to the new resources.
Turn off the containers in the cloud you migrated from.

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Detailed Comparison

Google Cloud Functions	AWS Batch
Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running	It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.
Statistics
Stacks 478	Stacks 84
Followers 479	Followers 251
Votes 25	Votes 6
Pros & Cons
Pros 7 Serverless Applications 5 Its not AWS 4 Simplicity 3 Free Tiers and Trainging 2 Simple config with GitLab CI/CD Cons 1 Node.js only 0 Typescript Support 0 Blaze, pay as you go	Pros 3 Containerized 3 Scalable Cons 3 More overhead than lambda 1 Image management
Integrations
Firebase Google Cloud Storage Stackdriver	No integrations available

What are some alternatives to Google Cloud Functions, AWS Batch?

AWS Lambda

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Serverless

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.

Apache OpenWhisk

OpenWhisk is an open source serverless platform. It is enterprise grade and accessible to all developers thanks to its superior programming model and tooling. It powers IBM Cloud Functions, Adobe I/O Runtime, Naver, Nimbella among others.

Cloud Functions for Firebase

Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.

Fission

Write short-lived functions in any language, and map them to HTTP requests (or other event triggers). Deploy functions instantly with one command. There are no containers to build, and no Docker registries to manage.

Related Comparisons

AWS Batch vs Google Cloud Functions: What are the differences?

Pricing: AWS Batch has a pay-as-you-go pricing model where you pay for the compute resources used to run your batch jobs. In contrast, Google Cloud Functions follow a pay-per-use pricing structure, billing you only for the time your functions are executing.
Service Integration: AWS Batch is tightly integrated with other AWS services such as EC2, S3, and IAM, allowing seamless interaction between different AWS resources. On the other hand, Google Cloud Functions are more oriented towards event-driven computing and integrate well with other Google Cloud Platform services like Cloud Storage and Pub/Sub.
Scaling: AWS Batch allows for managing and scaling batch computing workloads efficiently by supporting job queues and compute environments. In comparison, Google Cloud Functions are serverless and automatically scale based on the incoming requests, eliminating the need for manual scaling configurations.
Environment Flexibility: AWS Batch provides flexibility in choosing the compute resources for running batch jobs, supporting both on-demand and spot instances. Conversely, Google Cloud Functions are limited to running on predefined environments provided by Google Cloud Platform, without the ability to customize the underlying infrastructure.
Deployment Process: AWS Batch focuses on running long-running batch processes or jobs, making it suitable for scenarios where tasks require extended processing times. In contrast, Google Cloud Functions are designed for short-lived, event-driven functions that respond to specific triggers or events, enabling rapid development and deployment of small pieces of code.
Monitoring and Management: AWS Batch offers comprehensive monitoring and management features through Amazon CloudWatch, allowing users to track job status, resource utilization, and performance metrics. Google Cloud Functions leverage Stackdriver Logging and Monitoring for similar purposes, providing visibility into function execution and system performance.

In Summary, AWS Batch and Google Cloud Functions differ in pricing, service integration, scaling capabilities, environment flexibility, deployment processes, and monitoring and management solutions.

AWS Batch vs Google Cloud Functions

Overview