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
Azure App Service
Score 8.8 out of 10
N/A
The Microsoft Azure App Service is a PaaS that enables users to build, deploy, and scale web apps and APIs, a fully managed service with built-in infrastructure maintenance, security patching, and scaling. Includes Azure Web Apps, Azure Mobile Apps, Azure API Apps, allowing developers to use popular frameworks including .NET, .NET Core, Java, Node.js, Python, PHP, and Ruby.
$9.49
per month
Pricing
AWS Lambda
Azure App Service
Editions & Modules
128 MB
$0.0000000021
Per 1 ms
1024 MB
$0.0000000167
Per 1 ms
10240 MB
$0.0000001667
Per 1 ms
Shared Environment for dev/test
$9.49
per month
Basic Dedicated environment for dev/test
$54.75
per month
Standard Run production workloads
$73
per month
Premium Enhanced performance and scale
$146
per month
Offerings
Pricing Offerings
AWS Lambda
Azure App Service
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
Additional Details
—
Free and Shared (preview) plans are ideal for testing applications in a managed Azure environment. Basic, Standard and Premium plans are for production workloads and run on dedicated Virtual Machine instances. Each instance can support multiple applications and domains.
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Community Pulse
AWS Lambda
Azure App Service
Features
AWS Lambda
Azure App Service
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.
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.
I enjoy the fact that Azure App Service can be managed by the Azure portal and a fully graphical user interface, as well as from two different flavors of command-line interface, i.e. Azure CLI and Azure Powershell. By utilizing the Azure Cloud Shell, we are able to switch between Azure Bash (CLI) and Powershell at any given moment and manage the Azure App Service settings from devices of any form-factor (Web based management).
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.
We had an issue where we deployed too large of a resource and didn't notice until the bill came through. They were very understanding and saw we weren't utilizing the resources so they issued a generous refund in about 4 hours. Very fast, friendly, and understanding support reps from my experience.
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.
In terms of deploying your apps, Azure App Service provides a solid foundation. You may use either the Azure command-line interface or the Web Portal to administer these apps. As a whole, I find You may easily deploy your apps to Azure App Service to be a really difficult platform for novices to get their feet wet. First and foremost, I'd look at Heroku's user interface and the way it abstracts compute units.
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.