AWS Batch vs AWS Firecracker

Overview

AWS Batch

Stacks84

Followers251

Votes6

AWS Firecracker

Stacks6

Followers34

Votes0

GitHub Stars31.0K

Forks2.1K

AWS Batch vs AWS Firecracker: What are the differences?

<Write Introduction here>

Deployment: AWS Batch is a fully managed service for running batch computing workloads, while AWS Firecracker is a virtualization technology that enables you to run containers in lightweight microVMs.
Instance Management: In AWS Batch, you manage instances while AWS Firecracker abstracts the concept of instances, allowing you to focus on containers and functions.
Granularity: AWS Batch is designed for long-running batch processes, whereas AWS Firecracker is suited for short-lived containers and functions.
Isolation: AWS Firecracker provides stronger isolation between workloads by using lightweight virtual machines, while AWS Batch offers more flexibility in terms of instance types and configurations.
Performance: AWS Firecracker is optimized for speed and efficiency in running lightweight workloads, while AWS Batch can handle a broader range of batch computing workloads.
Cost: AWS Firecracker may offer cost savings due to its lightweight nature and efficient resource utilization compared to AWS Batch, which may incur additional costs for managing instances.

In Summary, AWS Batch and AWS Firecracker differ in deployment, instance management, granularity, isolation, performance, and cost, catering to different types of workloads and use cases.

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Detailed Comparison

AWS Batch	AWS Firecracker
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.	Firecracker is an open source virtualization technology that is purpose-built for creating and managing secure, multi-tenant container and function-based services that provide serverless operational models. Firecracker runs workloads in lightweight virtual machines, called microVMs, which combine the security and isolation properties provided by hardware virtualization technology with the speed and flexibility of containers.
Statistics
GitHub Stars -	GitHub Stars 31.0K
GitHub Forks -	GitHub Forks 2.1K
Stacks 84	Stacks 6
Followers 251	Followers 34
Votes 6	Votes 0
Pros & Cons
Pros 3 Containerized 3 Scalable Cons 3 More overhead than lambda 1 Image management	No community feedback yet

What are some alternatives to AWS Batch, AWS Firecracker?

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.

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

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.

Related Comparisons

AWS Batch vs AWS Firecracker: What are the differences?

<Write Introduction here>

Deployment: AWS Batch is a fully managed service for running batch computing workloads, while AWS Firecracker is a virtualization technology that enables you to run containers in lightweight microVMs.
Instance Management: In AWS Batch, you manage instances while AWS Firecracker abstracts the concept of instances, allowing you to focus on containers and functions.
Granularity: AWS Batch is designed for long-running batch processes, whereas AWS Firecracker is suited for short-lived containers and functions.
Isolation: AWS Firecracker provides stronger isolation between workloads by using lightweight virtual machines, while AWS Batch offers more flexibility in terms of instance types and configurations.
Performance: AWS Firecracker is optimized for speed and efficiency in running lightweight workloads, while AWS Batch can handle a broader range of batch computing workloads.
Cost: AWS Firecracker may offer cost savings due to its lightweight nature and efficient resource utilization compared to AWS Batch, which may incur additional costs for managing instances.

In Summary, AWS Batch and AWS Firecracker differ in deployment, instance management, granularity, isolation, performance, and cost, catering to different types of workloads and use cases.

AWS Batch vs AWS Firecracker

Overview