AWS Lambda is a compute service that lets you run code without provisioning or managing servers.

Hi Guys, this is Abi from GokceDB and in this video, you are going to learn 7 limitations of Lambda in AWS. Let's get into it.

1. Your Lambda function can only be assigned a memory size ranging from 128 to 10,240 MB. For reference, at 1,769 MB, a function has the equivalent of one virtual CPU.

2. The maximum time your Lambda function can run is 15 minutes. This makes Lambda unsuitable for long-running workloads.

3. For every synchronous invocation of a Lambda function, the payload size is capped at 6 MB, while for each asynchronous invocation, it is limited to 256 KB.

4. You can use environment variables to adjust your function's behavior without updating the code. However, the size of the environment variables is limited to 4 KB.

5. Lambda layers provide a convenient way to package dependencies that you can use with your Lambda functions. However, you can only add up to five layers to your Lambda function.

6. As your function receives more requests, Lambda automatically scales up to between 500 and 3000 execution environments to handle the burst in traffic, depending on the region. Although, you can contact AWS Support to request an increase to your default burst concurrency.

7. The maximum deployment Lambda package size is 50 MB for zip files archives and 250 MB for unzipped. This limit applies to all the files you upload, including layers and custom runtimes.

In summary, even though AWS Lambda is not suited for long-running tasks, it lets you run code for workloads lasting less than 15 minutes, without thinking about servers or clusters.

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