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What is Google Cloud Functions?

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running
Google Cloud Functions is a tool in the Serverless / Task Processing category of a tech stack.

Who uses Google Cloud Functions?

Companies
74 companies reportedly use Google Cloud Functions in their tech stacks, including Policygenius, OTOBANK Inc., and Kalibrr.

Developers
89 developers on StackShare have stated that they use Google Cloud Functions.

Google Cloud Functions Integrations

Firebase, Google Cloud Storage, Stackdriver, Cloud Functions for Firebase, and faast.js are some of the popular tools that integrate with Google Cloud Functions. Here's a list of all 9 tools that integrate with Google Cloud Functions.

Why developers like Google Cloud Functions?

Here’s a list of reasons why companies and developers use Google Cloud Functions
Google Cloud Functions Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Google Cloud Functions in their tech stack.

Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer · | 12 upvotes · 32.2K 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.

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Tim Nolet
Tim Nolet
Founder, Engineer & Dishwasher at Checkly · | 5 upvotes · 9.1K 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/

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Google Cloud Functions
Google Cloud Functions
Google Cloud Run
Google Cloud Run

I use Google Cloud Run because it's like bring your own docker image to Google Cloud Functions.

I use it for building Dash Apps

It creates a nice url for web apps, and I see it being the evolution of serverless if GCP can scale this up.

My Real-Time Python App Example

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AWS Lambda
AWS Lambda
Google Cloud Functions
Google Cloud Functions

I use Google Cloud Functions because it's the AWS Lambda equivalent on GCP. It's not as mature compared to lambda because it doesn't have VPC enablement unless done through VPC Service Controls which can be pretty cumbersome.

Although it feels bare bones compared to lambda, it still gets the job done when you want backend tasks done via serverless.

Example Use Case

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Shaun Persad
Shaun Persad
Maker at CommentBox.io · | 1 upvotes · 1.9K views
atCommentBox.ioCommentBox.io
Google Cloud Functions
Google Cloud Functions

Running background triggers based on events provides a simple, scalable way to create complex interactions. Google Cloud Functions

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Matt Welke
Matt Welke
Software Developer at GroupBy Inc. · | 1 upvotes · 1.9K views
Google Cloud Functions
Google Cloud Functions

Useful for personal projects where I have extremely low traffic. Scalability not taken advantage of for personal projects because of lack of funds.

I use it in a more microservice style. A recent use case was subscribing to a free tier of a 3rd party geolocation service (take lat and long, return address), and exposing an endpoint to trigger it, so that I could centralize credentials for the 3rd party service and reuse the endpoint across all proofs of concept and personal projects.

Used equally as often as AWS Lambda, depending on where the rest of the tech stack is hosted. Google Cloud Functions

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Google Cloud Functions Alternatives & Comparisons

What are some alternatives to Google Cloud 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 App Engine
Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
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.
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.
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.
See all alternatives

Google Cloud Functions's Followers
116 developers follow Google Cloud Functions to keep up with related blogs and decisions.
Foudre Tower
Nick Rockwell
Jihwan Oh
Mohamma76685757
Wittawat Karpkrikaew
EPSA Dev
Leon Kiefer
Muhammad Shahid
Himansu Sekhar
upgradesebastian