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

Apache OpenWhisk vs IronWorker

OverviewComparisonAlternatives

Overview

IronWorker
IronWorker
Stacks39
Followers17
Votes0
Apache OpenWhisk
Apache OpenWhisk
Stacks58
Followers149
Votes7

Apache OpenWhisk vs IronWorker: What are the differences?

# Introduction
Apache OpenWhisk and IronWorker are both serverless computing platforms that allow developers to run code in response to events without the need to manage servers. However, there are key differences between these two platforms that developers should be aware of.

1. **Runtime Support**: Apache OpenWhisk offers runtime support for JavaScript, Python, Swift, Java, and PHP, while IronWorker supports only Ruby and Docker containers for running custom code.
2. **Scalability**: Apache OpenWhisk automatically scales based on the demand of incoming events, while IronWorker requires manual adjustment of resources for scaling.
3. **Pricing Model**: Apache OpenWhisk follows a pay-per-use pricing model, allowing users to pay only for the resources they consume, while IronWorker offers fixed monthly plans based on the number of containers and tasks.
4. **Community Support**: Apache OpenWhisk has a larger and more active community of developers contributing to the platform, providing a wider range of integrations and plugins, compared to IronWorker's smaller community.
5. **Deployment Flexibility**: Apache OpenWhisk can be deployed on various cloud providers and on-premises environments, offering more deployment options, whereas IronWorker is primarily dependent on its cloud-hosted platform for deployment.
6. **Management GUI**: Apache OpenWhisk provides a user-friendly web-based graphical user interface for managing functions and events, while IronWorker relies more on command-line interface for management tasks.

In Summary, Apache OpenWhisk and IronWorker differ in terms of runtime support, scalability, pricing model, community support, deployment flexibility, and management GUI.

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Detailed Comparison

IronWorker
IronWorker
Apache OpenWhisk
Apache OpenWhisk

IronWorker provides the muscle for modern applications by efficiently isolating the code and dependencies of individual tasks to be processed on demand. Run in a multi-language containerized environment with streamlined orchestration, IronWorker gives you the flexibility to power any task in parallel at massive scale.

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.

Containerized environment;High-scale processing;Flexible scheduling;Reliable and secure;Detailed monitoring and configuration;Multiple language support
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
39
Stacks
58
Followers
17
Followers
149
Votes
0
Votes
7
Pros & Cons
Pros
  • 0
    Fully on-premise deployable
  • 0
    Cloud agnostic
  • 0
    Ease of configuration
  • 0
    Can run Docker containers
  • 0
    Language agnostic
Pros
  • 4
    You are not tied to a provider. IBM available however
  • 3
    Still exploring... its just intresting
Integrations
No integrations available
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 IronWorker, 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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