Apex vs Azure Functions Comparison | StackShare

Overview

Apex

Stacks336

Followers117

Votes0

GitHub Stars33

Forks56

Azure Functions

Stacks686

Followers705

Votes62

Apex vs Azure Functions: What are the differences?

What is Apex? Serverless Architecture with AWS Lambda. Apex is a small tool for deploying and managing AWS Lambda functions. With shims for languages not yet supported by Lambda, you can use Golang out of the box.

What is Azure Functions? Listen and react to events across your stack. 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.

Apex and Azure Functions belong to "Serverless / Task Processing" category of the tech stack.

Some of the features offered by Apex are:

Supports languages Lambda does not natively support via shim, such as Go
Binary install (useful for continuous deployment in CI etc)
Project level function and resource management

On the other hand, Azure Functions provides the following key features:

Easily schedule event-driven tasks across services
Expose Functions as HTTP API endpoints
Scale Functions based on customer demand

Apex is an open source tool with 7.82K GitHub stars and 567 GitHub forks. Here's a link to Apex's open source repository on GitHub.

Advice on Apex, Azure Functions

Mark

Nov 2, 2020

Needs adviceon

Microsoft Azure

Need advice on what platform, systems and tools to use.

Evaluating whether to start a new digital business for which we will need to build a website that handles all traffic. Website only right now. May add smartphone apps later. No desktop app will ever be added. Website to serve various countries and languages. B2B and B2C type customers. Need to handle heavy traffic, be low cost, and scale well.

We are open to either build it on AWS or on Microsoft Azure.

Apologies if I'm leaving out some info. My first post. :) Thanks in advance!

133k views133k

Comments

Detailed Comparison

Apex	Azure Functions
Apex is a small tool for deploying and managing AWS Lambda functions. With shims for languages not yet supported by Lambda, you can use Golang out of the box.	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.
Supports languages Lambda does not natively support via shim, such as Go;Binary install (useful for continuous deployment in CI etc);Project level function and resource management;Configuration inheritance and overrides;Command-line function invocation with JSON streams;Transparently generates a zip for your deploy;Function rollback support;Tail function CloudWatchLogs;Concurrency for quick deploys;Dry-run to preview changes	Easily schedule event-driven tasks across services;Expose Functions as HTTP API endpoints;Scale Functions based on customer demand;Develop how you want, using a browser-based UI or existing tools;Get continuous deployment, remote debugging, and authentication out of the box
Statistics
GitHub Stars 33	GitHub Stars -
GitHub Forks 56	GitHub Forks -
Stacks 336	Stacks 686
Followers 117	Followers 705
Votes 0	Votes 62
Pros & Cons
No community feedback yet	Pros 14 Pay only when invoked 11 Great developer experience for C# 9 Multiple languages supported 7 Great debugging support 5 Can be used as lightweight https service Cons 1 Poor support for Linux environments 1 Sporadic server & language runtime issues 1 Not suited for long-running applications 1 No persistent (writable) file system available
Integrations
AWS Lambda Golang	Azure DevOps Java Bitbucket Node.js Microsoft Azure GitHub Visual Studio Code JavaScript Azure Cosmos DB C#

What are some alternatives to Apex, Azure Functions?

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.

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.

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.