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

Apache OpenWhisk vs FaaS

OverviewComparisonAlternatives

Overview

FaaS
FaaS
Stacks5
Followers28
Votes1
Apache OpenWhisk
Apache OpenWhisk
Stacks58
Followers149
Votes7

Apache OpenWhisk vs FaaS: What are the differences?

## Introduction

1. **Deployment Model**: Apache OpenWhisk is an open-source serverless platform, while FaaS (Function as a Service) is a cloud computing model that allows users to develop and run functions without managing servers.
   
2. **Vendor Lock-in**: Apache OpenWhisk is not tied to a specific vendor, providing users with the flexibility to run their functions on different cloud platforms. In contrast, FaaS services provided by cloud vendors like AWS, Azure, and Google Cloud may lock users into their specific ecosystem.
   
3. **Pricing Structure**: Apache OpenWhisk is an open-source project and generally free to use, making it cost-effective for users. On the other hand, FaaS services come with pay-per-use pricing models, which can result in unpredictable costs based on the number of executions and resources consumed.
   
4. **Customization and Control**: Apache OpenWhisk offers more customization options and control over the underlying infrastructure, allowing users to tailor the environment to their specific needs. In comparison, FaaS services often abstract away the infrastructure details, limiting the level of customization available to users.
   
5. **Community Support and Ecosystem**: Apache OpenWhisk benefits from a vibrant open-source community that contributes to its development and provides support to users. FaaS services, while backed by large cloud providers, may offer extensive documentation and tools but might lack the same level of community support.
   
6. **Integration with External Services**: Apache OpenWhisk provides flexibility in integrating with various external services and tools through its ecosystem, enabling users to extend the platform's capabilities. In contrast, FaaS services may have native integrations with specific cloud services but could be limited in integrating with third-party tools and services seamlessly.

In Summary, Apache OpenWhisk and FaaS differ in deployment model, vendor lock-in, pricing structure, customization and control, community support, ecosystem, and integration with external services.

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Detailed Comparison

FaaS
FaaS
Apache OpenWhisk
Apache OpenWhisk

FaaS is a platform for building serverless functions on Docker Swarm Mode with first class metrics. Any UNIX process can be packaged as a function in FaaS enabling you to consume a range of web events without repetitive boiler-plate coding.

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.

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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
Stacks
5
Stacks
58
Followers
28
Followers
149
Votes
1
Votes
7
Pros & Cons
Pros
  • 1
    Simple way to build serverless applications
Pros
  • 4
    You are not tied to a provider. IBM available however
  • 3
    Still exploring... its just intresting
Integrations
Docker
Docker
Docker Swarm
Docker Swarm
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

What are some alternatives to FaaS, Apache OpenWhisk?

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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