IBM Cloud Functions Reviews & Insights

Our IBM Cloud Functions [connects] various services by picking up JSON data from buckets at a certain time interval, modifying it, and saving it to another bucket. We also have functions that connect to NLU, functions that connect to API endpoints, etc.

IBM Cloud Functions [is] not the worse product on the IBM cloud. I decided to write this review as I thought it would be balanced. I would still use functions to set up a serverless architecture where execution time is pretty quick and the code is relatively simple.
I wouldn't use IBM Cloud Functions for async calls obviously, as costs could be higher. The functions documentation is lacking in terms of CI/CD, and there are unexplainable errors occurring - like the network connection that I mentioned. So I wouldn't just rely on IBM Cloud Functions too much for the entire system, but make sure it's diversified.

We have several python functions that we had set up as API calls, but we have changed them to be cloud functions instead. We are using these on different applications for several different clients. These perform operations such as validating files, computing a value based off of a previously set up machine learning model and more complex report/database queries that take more than a second or two.

These functions are good if you are trying to diminish the overhead of API maintenance and support. Not too difficult to build and deploy. Good for doing model inference, or data validation. This is not good for doing tasks that take a long time. Not good for high compute and not good for things you are doing multiple times a minute.

We use IBM Cloud Functions in multiple client-facing projects. It allows us to quickly perform small tasks and checks without starting up complex infrastructure.

IBM Cloud Functions is created for event-driven serverless computing. It is not made for large monolith applications.

We use it to compute the identity of the type of file being uploaded and also do a bunch of computing when required. Also, we use it for daily jobs.

When you have data computing that needs to be done in parallel and for daily jobs.

We are using IBM Cloud Functions (ICF) to create enterprise level platforms that work with IBM Watson Cognitive services to do event-based processing. We have developed a platform to create Watson Assistant based virtual agents which use IBM Cloud Functions to fetch data from back-end systems or DB.

IBM Cloud Functions are well suited for:
1. Lightweight micro-service development for cloud-based applications
2. Event-based data processing that requires dynamic scaling
3. Cost reductions where application does not require to use functions all the time
4. ICF is good where you need integration with many external service providers

ICF may not be a good solution where an application is not modularized in fine-grained services or functions.

We use IBM Cloud Functions on a case by case basis for our app development teams. It helps our teams and developers scale on mobile and easily with swift integration

Apache OpenWhisk has some limitations when it comes to scalability. Other platforms like Amazon Web Services might be better-suited for businesses looking to leverage schedules or sources to launch events, but IBM's Cloud Functions are still great for smaller companies looking for similar features at a more competitive price point.

IBM Clound Functions are used to validate and analyze raw data that its arrival time and size hard to be predicted, since it depends on real world activities.

Cloud functions are best for usage for infrequent or unpredicted events when you care about performance scalability and cost. Cloud functions are less appropriate when we running time depends on 3rd party and the functions might be waiting for a response for long periods, and are actually idling.