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 Code Engine
Score 8.6 out of 10
N/A
IBM Cloud Code Engine is a fully managed, serverless platform that unifies the deployment of containers and applications including web apps, microservices, event-driven functions, or batch jobs. This serverless compute service aims to remove the burden of building, deploying, and managing workloads in Kubernetes so users can focus on writing code and not on the infrastructure that is needed to host it. With IBM Cloud Code Engine users can run any workload…
Having only some hands-on with the other services, The cloud engine is considerably simpler to operate, with a very clear interface and management. Each application can be easily configured and altered without needing to know anything about different subscription tiers or the …
Scenarios where AWS Lambda is well suited: 1. When we need to run a periodic task few times in a day or every hour, we may deploy it on AWS Lambda so it would not increase load on our server which is handling client requests and at the same time we don't have to pay for AWS Lambda when it is not running. So, overall we only pay for few function invocations. 2. When some compute intensive processing is to be done but the number of requests per unit of time fluctuates. For example, we had deployed an AWS Lambda for processing images into different sizes and storing them on AWS S3 once user uploads them. Now, this is something that may happen few times every hour on a particular day or may not happen even once on other days. To handle this kind of tasks AWS Lambda is a better choice as we don't have to pay for the idle time of the server and also we don't have to worry about scaling when the load is high. Scenarios where AWS Lambda is not appropriate to use: 1. When we expect a large request volume continuously on the server. 2. When we don't want latency even in case of concurrent requests.
It would be a good solution for running serverless applications. Because infrastructure setup and maintenance expenses can be avoided, the investment will pay for itself. The time to value is short, allowing IT to respond to business demands quickly. It aided us in customizing security as well as operating a personal project using to autoscale up and down approach. Also, because there isn't much hassle, items can be pushed into production as soon as possible. Simply push a container, create an application, and you're ready to go. But, It is less suited when you have a static machine or need to keep data in some way and do not want to utilize network storage or a database.
Lambda provides multiple methods for triggering functions, this includes AWS resources and services and external triggers like APIs and CLI calls.
The compute provided my Lambda is largely hands off for operations teams. Once the function is deployed, the management overhead is minimal since there are no servers to maintain.
Lambda's pricing can be very cost effective given that users are only charged for the time the function runs and associated costs like network or storage if those are used. A function that executes quickly and is not called often can cost next to nothing.
Putting a significant portion of your codebase into AWS Lambda and taking advantage of the high level of integration with other AWS services comes with the risk of vendor lock-in.
While the AWS Lambda environment is "not your problem," it's also not at your disposal to extend or modify, nor does it preserve state between function executions.
AWS Lambda functions are subject to strict time limitations, and will be aborted if they exceed five minutes of execution time. This can be a problem for some longer-running tasks that are otherwise well-suited to serverless delivery.
The present lack of tools like manuals and videos aggravates the difficulties in effectively merging various technologies.
While containerizing is still a fresh and complex concept with numerous benefits IBM Cloud Code Engine which is developed on containerizing could be difficult for customers with less experience.
Furthermore smaller companies cannot afford these technologies and legal barriers impede the adoption process even if the solutions offer speedy deployment.
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.
IBM Cloud Code Engine is good for developers to create a microservice based on typescript and nodejs. The tool can help mange the project very well. It decrease the difficulty for developement and increase deloping speed. However, the code engine UI on IBM cloud does not contain enough features it can support.
I have not needed support for AWS Lambda, since it is already using Python, which has resources all over the internet. AWS blog posts have information about how to install some libraries, which is necessary for some more complex operations, but this is available online and didn't require specific customer support for.
Azure Functions is another product that provides lambda functionality, but the documentation for some of Azure's products is quite hard to read. Additionally, AWS Lambda was one of the first cloud computing products on a large cloud service that implemented lambda functions, so they have had the most time to develop the product, increase the quality of service, and extend functionality to more languages. Amazon, by far, has the best service for Lambda that I know.
What impresses me most about IBM Cloud Code Engine is the container workload management capability and the Cloud services and dataflow monitoring functionalities. Data security and network security control via IBM Cloud Code Engine is quite excellent and very responsive data integration functions and the first deployment is not very technical.
I was able to perform a lot of processing on data delivered from my website and little or no cost. This was a big plus to me.
Programming AWS Lambda is quite easy once you understand the time limits to the functions.
AWS Lambda has really good integration with the AWS S3 storage system. This a very good method of delivering data to be processed and a good place to pick it up after processing.