AWS Lambda is a serverless computing platform that lets users run code without provisioning or managing servers. With Lambda, users can run code for virtually any type of app or backend service—all with zero administration. It takes of requirements to run and scale code with high availability.
$NaN
Per 1 ms
IBM Cloud Kubernetes Service
Score 7.9 out of 10
Mid-Size Companies (51-1,000 employees)
IBM Cloud Kubernetes Service is a
managed Kubernetes offering, delivering user tools and built-in security for rapid delivery of applications
that users can bind to cloud services related to IBM Watson®, IoT, DevOps
and data analytics. As a certified K8s provider, IBM Cloud Kubernetes
Service provides intelligent scheduling, self-healing, horizontal
scaling, service discovery and load balancing, automated rollouts and
rollbacks, and secret and configuration management. The Kubernetes…
AWS Lambda may be more suitable for large companies that want to continuously access similar functions at a higher number / frequency, but for small teams with limited budgets, IBM Cloud Functions is a competitive choice.
Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.
IBM Cloud Kubernetes Service also stands out in environments where the workloads vary continuously and require befitting scale. The product excels particularly in microservices structures, wherein the companies would harness the capacity for container orchestration and automated scaling. Still, it may face the challenges due to monolith applications that have not been originally developed for using container technology.
IBM has a strong focus on serverless and Kubernetes. This shows in the platform. Deploying containers to Kubernetes was very easy.
Deploying a Kubernetes cluster through the GUI is very easy and quick. On top of that, IBM Cloud offers a single node cluster for Free.
Container Registry is a very good product for managing container images. Integration with Kubernetes was seemless.
Portability. To transition from Google Cloud Kubernetes to IBM Cloud Kubernetes took almost no effort. We mostly use the CLI and the standard tools such as kubectl were present.
Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.
I constantly get this error even when everything is well configured prefect.exceptions.AuthorizationError: [{'path': ['auth_info'], 'message': 'AuthenticationError: Forbidden', 'extensions': {'code': 'UNAUTHENTICATED'}}]
Then sometimes the error disapear without changine anything, happened twice to me. Should there be an issue with the authentication service? Please let's improve or let users know why this may be happening.
Improve the UX in the browse console when removing many images at once
UX on the process of installing KeyCloack operator
We have our application running on a CentOS compartment on IBM Cloud Kubernetes Service. We have been utilizing the help since IBM Cloud initially dispatched. We liked the adaptability and versatility that IBM Cloud Kubernetes Service give us. Since we are tiny, the Kubernetes administration is just utilized at present inside my venture bunch.
I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.
We actually haven't had any real problems in our clusters recently and the results we have gotten from adopting IBM Cloud Kubernetes Service have been beyond even our greatest expectations. The community has helped optimize the use of the system and make it relatively simpler to use.
Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.
The self-guided support was solid, and there are plenty of online videos to guide first time users, but I think one area of improvement is a faster way to transfer a large quantity of files from our local machine to the cloud for storage (Aspera)
Online training is really an important resource for using these tools. IBM's help center is rich in useful information and tips. Also, external guides and tutorials are available (e.g. on youtube), but I followed only IBM ones and I had no difficulties.
Ease of use. Very intuitive. We have been looking for a product that allows us to orchestrate our docker containers in a way where it allows us to effectively scale our applications to production. It also provides us a way of monitoring all our infrastructure in a very clear concise way.
AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions
We mainly selected [IBM Cloud Kubernetes Service] because IBM fabric blockchain service is mostly compatible with it. To have all the infrastructure in a single cloud to get the best output we selected the [IBM Cloud Kubernetes Service].
IBM's CKS does not offers automatic autoscaling nor vertical scaling (automatic). Other services like Google Kubernetes Engine scales up and down very well
Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.
