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.
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Per 1 ms
IBM Cloud Foundry
Score 6.0 out of 10
N/A
IBM Cloud Foundry is an IBM version of the open-source platform designed for building, testing, deploying, and scaling applications. Enterprises can run Cloud Foundry in a public isolated environment, while natively integrating with other IBM Cloud services, such as AI, Blockchain, and IoT.
$0.07
Per GBH
Pricing
AWS Lambda
IBM Cloud Foundry
Editions & Modules
128 MB
$0.0000000021
Per 1 ms
1024 MB
$0.0000000167
Per 1 ms
10240 MB
$0.0000001667
Per 1 ms
Community Runtimes
$0.07
Per GBH
Offerings
Pricing Offerings
AWS Lambda
IBM Cloud Foundry
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
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AWS Lambda
IBM Cloud Foundry
Features
AWS Lambda
IBM Cloud Foundry
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
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.
IBM Cloud Foundry is a solid service from the IBM Cloud platform. It is easy to learn, and does not usually require you to make drastic changes to your existing applications. It is especially good for new applications that are cloud native, or micro-services, that can be easily updated and deployed. With its blue/green deployment, you can achieve 0 downtime for your customers.
AWS Lambda is a welcoming platform, supporting several languages, including Java, Go, PowerShell, Node.js, C#, Python, and Ruby. And if you need to deploy a Lambda function in another language, AWS offers a Runtime API for integration.
We really appreciate how AWS Lambda is always-on for our functions, with only a brief "cold-start" waiting period the first time a function is called after being dormant.
In addition to only generating costs when it's actually being used, AWS Lambda really puts the "serverless" in serverless architecture, offering turnkey scaleability and high availability for our code with zero effort on our part.
Intuitive user interface makes it easy for anyone to use, regardless of their professional background.
A lot of the services integrate well with external platforms, APIs, and programs, not just IBM services. A lot of the competitors in this space lack this ability.
Maybe it is just our contract in particular, but support and help is always made available.
The UI and Developer experience is not so great. IF you use an abstraction like Serverless Application Model (SAM), things get pretty easy, but it's still AWS UI/DX you're working with after that (which is to say, not their strength).
Documentation is always a mixed bag. Sometimes it's just easier to google your specific problem and see how others have solved it. This can be much faster than trying to find an example that may or may not be there in the documentation (which oftentimes has multiple versions and revisions).
Sometimes the API Connect GUIs don't cleanly disengage after attaching models or updating schema and it is hard to know what has been written successfully and which (if any) models or tables were missed. I shouldn't have to manually check through a list of 377 models to find the ones in and out of a list on either models, folder or database tables. Printing a summary even in logs which did a "diff" sort of thing between 'task-set' and 'task-completed' (referring to attaching models or updating schema as tasks here as 'tasks').
Provide access to Postgres Database in Sydney datacentre for Australia.
Clearer documentation around setting up a secure (referring to SSL and certificate setup here) server on eg, chubby1.au-sydney.mybluemix.net.
Allow a ramp in pricing onto the Blockchains. We will not be able to afford it until quite a few years into production, even if we launch successfully.
It is very easy to get started with AWS Lambda and create your first function. The user interface makes it easy to add AWS services to be inputs or outputs to the function, meaning it can be configured in many different ways for different needs. This makes it ideal for various scenarios in AWS.
As this is a product where a great part of errors can be at the source code level, AWS support team doesn't dive that further. I mean they don't evaluate problems more complex related to your code, [which] is totally understandable, but this make[s] debug process more tough and painful.
It's fine, it works as the others would have, except EC2. We are migrating back to EC2 for dedicated compute because we have scaled to a point where we have consistent traffic. The tradeoff of maintaining infrastructure in-house outweighs the benefits of moving quickly through our roadmap.
IBM Cloud Foundry is our first choice industry-standard platform as a service (PaaS) which has always provided us with quicker, simpler, and more consistent ways for the deployment of the cloud-native applications which in result saved us lots of time and money.
We have simplified log fiie ingestion using Lambda functions. The return has been less time worrying about getting logs from source to ingestion; one the process is in place the team is nearly 100% hands off.
We have begun taking a more API focused approach by using API Gateway as the interface to business processes and Lambda as the back end compute. Moving away from server based back ends places us on a path to reducing overall spend in compute costs.
Lambda functions allow us to easily interface with third party services through APIs. This simplifies access management since the function can be granted permissions and access to the function can be gated with API keys and other authentication methods.
This was the founding solution used to allow us to move in to and test out a cloud pipeline. This is what paved the way for a full production cloud solution to be possible.
Having Cloud Foundry at the base of our development and sandpit environment, segregated away from our standard on premise solution has moved away red tape and ensured an agile way forward.