Add memory overhead doc (#2895)

docs/sources/configure-client/aws-lambda.md

@@ -35,17 +35,17 @@ For more details, refer to the [Building Extensions for AWS Lambda blog post](ht
 
 ## Set up the Pyroscope Lambda extension
 
-To set up the Pyroscope Lamnda extension, you need to: 
+To set up the Pyroscope Lamnda extension, you need to:
 
 1. Configure your Lamda function
-1. Set up your environment variables 
+1. Set up your environment variables
 1. Integrate the Pyroscope SDK
 
 ### Configure your Lambda function
 
 Configure your Lambda function to use the extension. Find the latest release on our [releases page](https://github.com/grafana/pyroscope-lambda-extension/releases).
 
-### Set up the environment variables 
+### Set up the environment variables
 
 Configure the extension with the following environment variables:
 

docs/sources/configure-client/memory-overhead.md

@@ -0,0 +1,41 @@
+---
+title: Pyroscope memory overhead
+menuTitle: Memory overhead
+description: Learn about memory overhead for the Pyroscope client.
+weight: 200
+---
+
+# Pyroscope memory overhead
+
+Pyroscope has very low memory overhead, usually less than 50 MB per pod.
+
+## How the profiler works
+
+Stacktraces are captured at regular intervals (~100Hz).
+Memory allocations and lock contention events are sampled.
+
+These stacktraces and memory allocation events are temporarily stored in memory.
+
+The stored profiling data is periodically (default is every 15 seconds) sent to the server.
+
+Memory overhead consists of:
+
+* The temporary storage of profiles in memory is the primary contributor to memory overhead.
+* Memory usage typically scales up sublinearly with the number of CPUs.
+
+## What happens if the Pyroscope backend is down?
+
+The guiding principle of Pyroscope clients is to never cause the user application to crash.
+
+Profiles are uploaded using multiple threads. The default value is `5` and can be adjusted using the `Threads` variable.
+
+If the backend is down or slow, the profiler discards new profiles to prevent running out of memory.
+
+## Real-world example
+
+The exact overhead can vary based on the application, so direct testing is recommended.
+
+At Grafana Labs we continuously profile all of our workloads.
+With all profiling types enabled (CPU, alloc, goroutine, mutex, block), the observed memory overhead per pod is typically less than 50 MB.
+
+The overhead is often so minimal that it becomes challenging to accurately measure.