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Azure Functions vs Serverless: What are the differences?

Developers describe Azure Functions as "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. On the other hand, Serverless is detailed as "The most widely-adopted toolkit for building serverless applications". 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.

Azure Functions and Serverless can be primarily classified as "Serverless / Task Processing" tools.

"Pay only when invoked" is the top reason why over 7 developers like Azure Functions, while over 10 developers mention "API integration " as the leading cause for choosing Serverless.

Serverless is an open source tool with 30.9K GitHub stars and 3.43K GitHub forks. Here's a link to Serverless's open source repository on GitHub.

Droplr, Plista GmbH, and Hammerhead are some of the popular companies that use Serverless, whereas Azure Functions is used by Property With Potential, OneWire, and Veris. Serverless has a broader approval, being mentioned in 117 company stacks & 44 developers stacks; compared to Azure Functions, which is listed in 30 company stacks and 22 developer stacks.

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What is 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.

What is 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.
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      What are some alternatives to Azure Functions and Serverless?
      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.
      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.
      Google Cloud Functions
      Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running
      Apex
      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.
      Zappa
      Zappa makes it super easy to deploy all Python WSGI applications on AWS Lambda + API Gateway. Think of it as "serverless" web hosting for your Python web apps. That means infinite scaling, zero downtime, zero maintenance - and at a fraction of the cost of your current deployments!
      See all alternatives
      Decisions about Azure Functions and Serverless
      Kestas Barzdaitis
      Kestas Barzdaitis
      Entrepreneur & Engineer · | 12 upvotes · 63.4K views
      atCodeFactorCodeFactor
      Google Cloud Functions
      Google Cloud Functions
      Azure Functions
      Azure Functions
      AWS Lambda
      AWS Lambda
      Docker
      Docker
      Google Compute Engine
      Google Compute Engine
      Microsoft Azure
      Microsoft Azure
      Amazon EC2
      Amazon EC2
      CodeFactor.io
      CodeFactor.io
      Kubernetes
      Kubernetes
      #SAAS
      #IAAS
      #Containerization
      #Autoscale
      #Startup
      #Automation
      #Machinelearning
      #AI
      #Devops

      CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

      CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

      AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

      It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

      The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

      In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

      Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

      See more
      Nitzan Shapira
      Nitzan Shapira
      at Epsagon · | 11 upvotes · 108.3K views
      atEpsagonEpsagon
      AWS Lambda
      AWS Lambda
      GitHub
      GitHub
      Java
      Java
      Go
      Go
      Node.js
      Node.js
      npm
      npm
      Serverless
      Serverless
      Python
      Python

      At Epsagon, we use hundreds of AWS Lambda functions, most of them are written in Python, and the Serverless Framework to pack and deploy them. One of the issues we've encountered is the difficulty to package external libraries into the Lambda environment using the Serverless Framework. This limitation is probably by design since the external code your Lambda needs can be usually included with a package manager.

      In order to overcome this issue, we've developed a tool, which we also published as open-source (see link below), which automatically packs these libraries using a simple npm package and a YAML configuration file. Support for Node.js, Go, and Java will be available soon.

      The GitHub respoitory: https://github.com/epsagon/serverless-package-external

      See more
      Michal Nowak
      Michal Nowak
      Co-founder at Evojam · | 7 upvotes · 64.5K views
      atEvojamEvojam
      Azure Functions
      Azure Functions
      Firebase
      Firebase
      AWS Lambda
      AWS Lambda
      Serverless
      Serverless

      In a couple of recent projects we had an opportunity to try out the new Serverless approach to building web applications. It wasn't necessarily a question if we should use any particular vendor but rather "if" we can consider serverless a viable option for building apps. Obviously our goal was also to get a feel for this technology and gain some hands-on experience.

      We did consider AWS Lambda, Firebase from Google as well as Azure Functions. Eventually we went with AWS Lambdas.

      PROS
      • No servers to manage (obviously!)
      • Limited fixed costs – you pay only for used time
      • Automated scaling and balancing
      • Automatic failover (or, at this level of abstraction, no failover problem at all)
      • Security easier to provide and audit
      • Low overhead at the start (with the certain level of knowledge)
      • Short time to market
      • Easy handover - deployment coupled with code
      • Perfect choice for lean startups with fast-paced iterations
      • Augmentation for the classic cloud, server(full) approach
      CONS
      • Not much know-how and best practices available about structuring the code and projects on the market
      • Not suitable for complex business logic due to the risk of producing highly coupled code
      • Cost difficult to estimate (helpful tools: serverlesscalc.com)
      • Difficulty in migration to other platforms (Vendor lock⚠️)
      • Little engineers with experience in serverless on the job market
      • Steep learning curve for engineers without any cloud experience

      More details are on our blog: https://evojam.com/blog/2018/12/5/should-you-go-serverless-meet-the-benefits-and-flaws-of-new-wave-of-cloud-solutions I hope it helps 🙌 & I'm curious of your experiences.

      See more
      Julien DeFrance
      Julien DeFrance
      Principal Software Engineer at Tophatter · | 2 upvotes · 14.4K views
      atSmartZipSmartZip
      Amazon SageMaker
      Amazon SageMaker
      Amazon Machine Learning
      Amazon Machine Learning
      AWS Lambda
      AWS Lambda
      Serverless
      Serverless
      #FaaS
      #GCP
      #PaaS

      Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

      Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

      Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

      Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

      See more
      Aviad Mor
      Aviad Mor
      CTO & Co-Founder at Lumigo · | 5 upvotes · 10.4K views
      atLumigoLumigo
      Serverless
      Serverless
      CircleCI
      CircleCI
      AWS Lambda
      AWS Lambda

      Our backend is serverless based, with many AWS Lambda , with CI/CD, using CircleCI and Serverless. This allows to develop with awesome agility and move fast. Since we update our lambdas daily, we needed a way to make sure we did not run into AWS's max limit of versions per lambda. We use the open source in link below to clear them out and stay clear of the limit.

      See more
      Aliadoc Team
      Aliadoc Team
      at aliadoc.com · | 5 upvotes · 90.9K views
      atAliadocAliadoc
      Bitbucket
      Bitbucket
      Visual Studio Code
      Visual Studio Code
      Serverless
      Serverless
      Google Cloud Storage
      Google Cloud Storage
      Google App Engine
      Google App Engine
      Cloud Functions for Firebase
      Cloud Functions for Firebase
      Firebase
      Firebase
      CloudFlare
      CloudFlare
      Create React App
      Create React App
      React
      React
      #Aliadoc

      In #Aliadoc, we're exploring the crowdfunding option to get traction before launch. We are building a SaaS platform for website design customization.

      For the Admin UI and website editor we use React and we're currently transitioning from a Create React App setup to a custom one because our needs have become more specific. We use CloudFlare as much as possible, it's a great service.

      For routing dynamic resources and proxy tasks to feed websites to the editor we leverage CloudFlare Workers for improved responsiveness. We use Firebase for our hosting needs and user authentication while also using several Cloud Functions for Firebase to interact with other services along with Google App Engine and Google Cloud Storage, but also the Real Time Database is on the radar for collaborative website editing.

      We generally hate configuration but honestly because of the stage of our project we lack resources for doing heavy sysops work. So we are basically just relying on Serverless technologies as much as we can to do all server side processing.

      Visual Studio Code definitively makes programming a much easier and enjoyable task, we just love it. We combine it with Bitbucket for our source code control needs.

      See more
      Tim Nolet
      Tim Nolet
      Founder, Engineer & Dishwasher at Checkly · | 5 upvotes · 21.4K views
      atChecklyHQChecklyHQ
      Node.js
      Node.js
      Google Cloud Functions
      Google Cloud Functions
      Azure Functions
      Azure Functions
      Amazon CloudWatch
      Amazon CloudWatch
      Serverless
      Serverless
      AWS Lambda
      AWS Lambda

      AWS Lambda Serverless Amazon CloudWatch Azure Functions Google Cloud Functions Node.js

      In the last year or so, I moved all Checkly monitoring workloads to AWS Lambda. Here are some stats:

      • We run three core functions in all AWS regions. They handle API checks, browser checks and setup / teardown scripts. Check our docs to find out what that means.
      • All functions are hooked up to SNS topics but can also be triggered directly through AWS SDK calls.
      • The busiest function is a plumbing function that forwards data to our database. It is invoked anywhere between 7000 and 10.000 times per hour with an average duration of about 179 ms.
      • We run separate dev and test versions of each function in each region.

      Moving all this to AWS Lambda took some work and considerations. The blog post linked below goes into the following topics:

      • Why Lambda is an almost perfect match for SaaS. Especially when you're small.
      • Why I don't use a "big" framework around it.
      • Why distributed background jobs triggered by queues are Lambda's raison d'être.
      • Why monitoring & logging is still an issue.

      https://blog.checklyhq.com/how-i-made-aws-lambda-work-for-my-saas/

      See more
      Praveen Mooli
      Praveen Mooli
      Technical Leader at Taylor and Francis · | 11 upvotes · 168.8K views
      MongoDB Atlas
      MongoDB Atlas
      Amazon S3
      Amazon S3
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon RDS
      Amazon RDS
      Serverless
      Serverless
      Docker
      Docker
      Terraform
      Terraform
      Travis CI
      Travis CI
      GitHub
      GitHub
      RxJS
      RxJS
      Angular 2
      Angular 2
      AWS Lambda
      AWS Lambda
      Amazon SQS
      Amazon SQS
      Amazon SNS
      Amazon SNS
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose
      Amazon Kinesis
      Amazon Kinesis
      Flask
      Flask
      Python
      Python
      ExpressJS
      ExpressJS
      Node.js
      Node.js
      Spring Boot
      Spring Boot
      Java
      Java
      #Data
      #Devops
      #Webapps
      #Eventsourcingframework
      #Microservices
      #Backend

      We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

      To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

      To build #Webapps we decided to use Angular 2 with RxJS

      #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

      See more
      Interest over time
      Reviews of Azure Functions and Serverless
      Review ofAzure FunctionsAzure Functions

      Poor developer experience

      How developers use Azure Functions and Serverless
      Avatar of Yonas B.
      Yonas B. uses Azure FunctionsAzure Functions

      I used Azure functions as part of an integration service when creating a bulk insert module in azure.

      Avatar of betterPT
      betterPT uses ServerlessServerless

      We use AWS Lambda / Serverless as a Facade for out integrations with EMRs.

      Avatar of JimmyCode
      JimmyCode uses ServerlessServerless

      Oh yeah! We run on lambdas.

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