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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. AWS Firecracker vs Apache OpenWhisk

AWS Firecracker vs Apache OpenWhisk

OverviewComparisonAlternatives

Overview

Apache OpenWhisk
Apache OpenWhisk
Stacks58
Followers149
Votes7
AWS Firecracker
AWS Firecracker
Stacks6
Followers34
Votes0
GitHub Stars31.0K
Forks2.1K

AWS Firecracker vs Apache OpenWhisk: What are the differences?

Introduction

In this article, we will discuss the key differences between AWS Firecracker and Apache OpenWhisk. Both AWS Firecracker and Apache OpenWhisk are innovative serverless computing solutions that offer unique features and capabilities.

  1. Performance and Isolation: One of the key differences between AWS Firecracker and Apache OpenWhisk is the approach to performance and isolation. Firecracker is designed for running lightweight, secure, and fast microVMs. It provides a high level of isolation and security by using a combination of hardware virtualization (KVM) and software-defined virtualization. On the other hand, Apache OpenWhisk uses lightweight containers (Docker) to execute workloads. While containers offer good performance, they may not provide the same level of isolation as Firecracker's microVMs.

  2. Scaling and Resource Utilization: Another important difference is the scaling and resource utilization capabilities. AWS Firecracker is designed to be highly efficient in terms of resource utilization. It has low overhead, fast startup time, and can scale up or down quickly based on demand. It is optimized for scenarios where there is a need for running multiple isolated workloads simultaneously. In contrast, Apache OpenWhisk provides automatic scaling based on rules and triggers defined by the user. It can scale out to accommodate increased workload but may not be as efficient in terms of resource utilization as Firecracker.

  3. Language Support and Flexibility: AWS Firecracker supports a wide range of operating systems and programming languages. It provides flexibility in terms of choosing the right combination that suits the specific use case. Firecracker allows users to run any Linux-based OS and execute any compatible workload. Apache OpenWhisk, on the other hand, is built on open standards and supports multiple programming languages including Node.js, Python, and Java. It offers a higher level of abstraction and simplifies the development of serverless applications.

  4. Community and Ecosystem: Both AWS Firecracker and Apache OpenWhisk have active communities and ecosystems. However, Firecracker is primarily driven and maintained by AWS, which provides extensive documentation and support. The Firecracker community continues to grow, with contributions from AWS and other developers. Apache OpenWhisk, being an open-source project, has a diverse community of contributors and users. It benefits from the collective knowledge and experience of the community, and users can contribute to its development and improvement.

  5. Pricing and Hosting Options: AWS Firecracker is a service provided by Amazon Web Services and is available as part of AWS Lambda. The pricing for Firecracker is based on the usage of other AWS services. Apache OpenWhisk, being an open-source solution, is free to use and can be hosted on various platforms including on-premises servers, public cloud, or hybrid environments. This provides users with the flexibility to choose the hosting option that best suits their needs and budget.

  6. Integration and Interoperability: Another difference between AWS Firecracker and Apache OpenWhisk is the integration and interoperability capabilities. Firecracker is tightly integrated with other AWS services, such as AWS Lambda and AWS Fargate, allowing seamless integration as part of a larger serverless architecture. Apache OpenWhisk, being an open-source platform, provides integration options with various third-party services and platforms. It offers flexibility in terms of integrating with different tools and technologies to build comprehensive serverless solutions.

In summary, AWS Firecracker and Apache OpenWhisk differ in terms of performance and isolation, scaling and resource utilization, language support and flexibility, community and ecosystem, pricing and hosting options, and integration and interoperability capabilities. Users can choose the solution that aligns with their specific requirements and use cases.

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Detailed Comparison

Apache OpenWhisk
Apache OpenWhisk
AWS Firecracker
AWS Firecracker

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.

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.

Serverless functions;FaaS;Fine-grained resource consumption;Use any language;Containers as functions; service;Functions-as-a-Service;Function composition;Step Functions;Docker;Kubernetes;Open source community;Apache
-
Statistics
GitHub Stars
-
GitHub Stars
31.0K
GitHub Forks
-
GitHub Forks
2.1K
Stacks
58
Stacks
6
Followers
149
Followers
34
Votes
7
Votes
0
Pros & Cons
Pros
  • 4
    You are not tied to a provider. IBM available however
  • 3
    Still exploring... its just intresting
No community feedback yet
Integrations
Node.js
Node.js
Visual Studio Code
Visual Studio Code
JavaScript
JavaScript
Python
Python
npm
npm
Kubernetes
Kubernetes
Docker
Docker
Swift
Swift
Java
Java
Slack
Slack
No integrations available

What are some alternatives to Apache OpenWhisk, AWS Firecracker?

AWS Lambda

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

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

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

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

Google Cloud Functions

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

Knative

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

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

Nuclio

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

Cloud Functions for Firebase

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.

AWS Batch

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.

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