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AWS Batch
AWS Batch

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Google Cloud Run
Google Cloud Run

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

Developers describe AWS Batch as "Fully Managed Batch Processing at Any Scale". 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. On the other hand, Google Cloud Run is detailed as "Run stateless HTTP containers on a fully managed environment or in your own GKE cluster". 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.

AWS Batch and Google Cloud Run can be categorized as "Serverless / Task Processing" tools.

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What is AWS Batch?

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.

What is 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.
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        What tools integrate with AWS Batch?
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          What are some alternatives to AWS Batch and Google Cloud Run?
          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.
          Beanstalk
          A single process to commit code, review with the team, and deploy the final result to your customers.
          Airflow
          Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
          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.
          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.
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          Decisions about AWS Batch and Google Cloud Run
          Google Cloud Run
          Google Cloud Run
          Google Cloud Functions
          Google Cloud Functions

          I use Google Cloud Run because it's like bring your own docker image to Google Cloud Functions.

          I use it for building Dash Apps

          It creates a nice url for web apps, and I see it being the evolution of serverless if GCP can scale this up.

          My Real-Time Python App Example

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          How much does Google Cloud Run cost?
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