IBM Cloud Functions Reviews

48 Ratings
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Score 7.1 out of 101

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Reviews (1-6 of 6)

Priya Manivannan profile photo
Score 6 out of 10
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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.
  • Scalable
  • Easily triggered when needed.
  • Log auditing is easy with all the filtering.
  • Saves on the hassle of maintaining apps.
  • Needs to be consistent, had issues for a few days when it didn't work as expected.
When you have data computing that needs to be done in parallel and for daily jobs.
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Score 5 out of 10
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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.
  • Great substitute for a simple API calls to run non-complicated code.
  • Easy way to run Python/Java/Javascript to get something done.
  • File validation.
  • They are not good if you are doing repetitive calls multiple times a minute.
  • They are not good for long processes.
  • They are not 100% reliable yet, they have been release for GA, but they don't standup to being beat up.
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.
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Score 6 out of 10
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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.
  • Quick setup
  • Able to handle multiple languages
  • Easy to scale
  • Limit on the max number of concurrent calls
  • Instability of the platform
  • Limit on the total size of the deployment
IBM Cloud Functions is created for event-driven serverless computing. It is not made for large monolith applications.
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Badheka, Amit profile photo
Score 10 out of 10
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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.
  • ICF is a cost-effective solution when it comes to a cloud-based solution. We used Spring Boot Micro-services previously but it was costly because the application is continuously running and hence incurs operating costs even if the services are not used by the application.
  • It scales very well and without too many manual interventions from the developers or support team. This is very useful when we have applications catering to large user bases like a chatbot or payment wallet.
  • The ICF also works well in high demand data processing based on events (i.e. in a virtual agent platform) Whenever we receive a new chat request, the ICF can trigger sentiment analysis to analyze the chats.
  • Need more out of the box support and integration to receive events from services like MongoDB and NoSQL databases.
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.
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Score 8 out of 10
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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
  • Makes mobile scalability really user-friendly, easy language integration
  • Good for triggering IoT apps when certain criteria/validations are met
  • Billing can be a hassle, not the most responsive customer service/support team
  • Handles & executes most functionalities, but other platforms offer more scalability if you're seeking consistent and stable growth
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.
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Score 8 out of 10
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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.
  • Validate raw data files - check the validity of raw data input to the system, to make sure we analyze only the relevant data. The raw data stream rate is hard to be predicted, since it depends on real world activities.
  • Analyze raw data - analyzing of valid raw data, described above.
  • Insert data to LOCAL data base.
  • APIs - cloud functions are charged based on usage time and needed computing power, when response time is something you can't enforce, like when using 3rd party APIs, you might pay for just waiting for reply.
  • Services - when using function for services, make sure it is really needed ... sometimes a legacy VM service will do the best job.
  • Programming languages - not all languages are supported (but you can run binary files if needed).
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.
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IBM Cloud Functions Scorecard Summary

Feature Scorecard Summary

Scalability (6)
8.0

About IBM Cloud Functions

IBM Cloud Functions is a serverless programming platform based on Apache OpenWhisk. Developers use their favorite programming language to write code (“actions”) that responds to external events. Actions are hosted and executed in IBM Cloud, and scale on demand based on the number of events coming in. There are no servers or other infrastructure to provision and manage.

Actions respond to a variety of events. Typical events include periodic timers for batch job processing, HTTP-based API requests for implementing RESTful APIs using Functions, and responding to change events requests from IBM Cloud services like Cloudant and IBM Cloud Event Streams, and third-party events like Slack and GitHub state changes.

Because Cloud Functions is a serverless, event-driven platform, you don't need to explicitly provision servers. Developers working with chatbots, blockchain, AI, APIs, microservices, mobile, IoT, and many other apps can focus on writing app logic instead of worrying about auto-scaling, high availability, updates, and maintenance. Out of the box auto-scaling and load balancing means that you don't have to manually configure clusters, http plugins, and so on. IBM takes care of all of the hardware, networking, and software administration. All you have to do is provide the code.

Visit our Docs pages for pricing and support information.

IBM Cloud Functions Features

Platform-as-a-Service Features
Does not have featureEase of building user interfaces
Has featureScalability
Does not have featurePlatform management overhead
Does not have featureWorkflow engine capability
Does not have featurePlatform access control
Does not have featureServices-enabled integration
Does not have featureDevelopment environment creation
Does not have featureDevelopment environment replication
Does not have featureIssue monitoring and notification
Does not have featureIssue recovery
Does not have featureUpgrades and platform fixes
Additional Features
Has featureElastic load balancing
Has featureTemplate library of pre-written functions encapsulating common use cases
Has featureRuntime build pack support for NodeJS, Python 2.7, Python 3, Swift, Ruby, Java, and executable programs written in Go, C++, shell script, etc.
Has feature“Bring Your Own Container” runtime support – users can provide a docker container image for their function action(s).

IBM Cloud Functions Integrations

GitHub, Any 3rd party service where they support a webhook/trigger API (e.g. slack, twilio)

IBM Cloud Functions Competitors

AWS Lambda, Azure functions, Google functions

IBM Cloud Functions Technical Details

Operating Systems: Unspecified
Mobile Application:No
Supported Countries:United States, United Kingdom, Australia, Germany
Supported Languages: English, French, German, Italian, Japanese, Korean, Portugese/Brazil, Spanish, Chinese simplified & traditional