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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IBM Cloud Managed Istio
Score 8.7 out of 10
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The IBM Cloud Kubernetes Service provides the Managed Istio installation add on, designed to provide additonal control over clusters and the microservices they comprise via automatic updates and lifecycle management of control plane components, and integration with platform logging and monitoring tools.
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AWS Lambda
IBM Cloud Managed Istio
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128 MB
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1024 MB
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10240 MB
$0.0000001667
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AWS Lambda
IBM Cloud Managed Istio
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AWS Lambda
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AWS Lambda
IBM Cloud Managed Istio
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.
It is a perfect application when you have multiple users and/or developers and you want to create rules and standards for access and the ability to change access levels. The reporting of usage is good, but adding more granularity into the metrics that have been used to measure are not specific enough. They provide detail but drilling into the detail would be more appropriate i.e. access to the baseline data.
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.
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).
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.
1) One-stop solution for all the complex network connectivity managed by [IBM Cloud Managed Istio]. 2) Nice dashboard to understand the flow. 3) Easy to troubleshoot the service if any one of the APIs is not responding. 4) We can integrate it with Azure Kubernetes service and other cloud provider Kubernetes services. 5) Monitoring the health of the pods can be easily configured and alerts can be triggered very accurately with this tool.
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.
