Google App Engine vs Google Cloud Functions: What are the differences?
Developers describe Google App Engine as "Build web applications on the same scalable systems that power Google applications". 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. On the other hand, Google Cloud Functions is detailed as "A serverless environment to build and connect cloud services". Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running.
Google App Engine and Google Cloud Functions are primarily classified as "Platform as a Service" and "Serverless / Task Processing" tools respectively.
"Easy to deploy" is the primary reason why developers consider Google App Engine over the competitors, whereas "Serverless Applications" was stated as the key factor in picking Google Cloud Functions.
Snapchat, Accenture, and Movielala are some of the popular companies that use Google App Engine, whereas Google Cloud Functions is used by Kalibrr, Policygenius, and BetterCloud. Google App Engine has a broader approval, being mentioned in 482 company stacks & 345 developers stacks; compared to Google Cloud Functions, which is listed in 55 company stacks and 21 developer stacks.
What is Google App Engine?
What is Google Cloud Functions?
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With Cloud Endpoints you can create and deploy mobile backend in one hour or less. And it is free (until you need extra scale). I would not recommend to use Java - python is faster and has all new appengine features.
Pros: everything is in one place: task queue, cron, backend instances for data processing, datastore, mapreduce. Cons: you cannot easily move your code from GAE. Even with special 3rd party services.
With Cloud Endpoints you can create and deploy mobile backend in one hour or less.
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.
PaaS for back-end components, including external data ingestion APIs, front-end web service APIs, hosting of static front-end application assets, back-end data processing pipeline microservices, APIs to storage infrastructure (Cloud SQL and Memcached), and data processing pipeline task queues and cron jobs. Task queue fan-out and auto-scaling of back-end microservice instances provide parallelism for high velocity data processing.
checking a swap require a lot of cpu resource, roster normally come out same day of month, every month, at a particular time. Which make very high spike, our flag ship product, iSwap, with the capability looking swap possibility with 10000 other rosters base on user critieria, you need a cloud computing give you this magnitude of computing power. gae did it nicely, user friendly, easy to you, low cost.
App engine fills in the gaps in the increasingly smaller case where it's necessary for us to run our own APIs.
Running background triggers based on events provides a simple, scalable way to create complex interactions.
Very easy to make cloud computing of ML models , and use containers like Kubernetes.