diff --git a/.github/workflows/continous-integration.yml b/.github/workflows/continous-integration.yml
index b50753b1dffb..587ad2ad26e4 100644
--- a/.github/workflows/continous-integration.yml
+++ b/.github/workflows/continous-integration.yml
@@ -290,6 +290,7 @@ jobs:
       - name: Prevent race condition in poetry build
         # More context about race condition during poetry build can be found here:
         # https://github.com/python-poetry/poetry/issues/7611#issuecomment-1747836233
+        if: needs.changes.outputs.backend == 'true'
         run: |
           poetry config installer.max-workers 1
 
diff --git a/docs/docs/monitoring/load-testing-guidelines.mdx b/docs/docs/monitoring/load-testing-guidelines.mdx
index ff40486853b5..a794d73639da 100644
--- a/docs/docs/monitoring/load-testing-guidelines.mdx
+++ b/docs/docs/monitoring/load-testing-guidelines.mdx
@@ -12,12 +12,26 @@ In order to gather metrics on our system's ability to handle increased loads and
 In each test case we spawned the following number of concurrent users at peak concurrency using a [spawn rate](https://docs.locust.io/en/1.5.0/configuration.html#all-available-configuration-options) of 1000 users per second.
 In our tests we used the Rasa [HTTP-API](https://rasa.com/docs/rasa/pages/http-api) and the [Locust](https://locust.io/) open source load testing tool.
 
+
 |        Users             |               CPU                            |      Memory   |
 |--------------------------|----------------------------------------------|---------------|
 | Up to 50,000             |         6vCPU                                |      16 GB    |
 | Up to 80,000             |         6vCPU, with almost 90% CPU usage     |      16 GB    |
 
 
+### Some recommendations to improve latency
+- Sanic Workers must be mapped 1:1 to CPU for both Rasa Pro and Rasa Action Server
+- Create `async` actions to avoid any blocking I/O
+- `enable_selective_domain: true` : Domain is only sent for actions that needs it. This massively trims the payload between the two pods.
+- Consider using compute efficient machines on cloud which are optimized for high performance computing such as the C5 instances on AWS.
+  However, as they are low on memory, models need to be trained lightweight.
+
+
+|        Machine                 |               RasaPro                          |      Rasa Action Server                          |
+|--------------------------------|------------------------------------------------|--------------------------------------------------|
+| AWS C5 or Azure F or Gcloud C2 |   3-7vCPU, 10-16Gb Memory, 3-7 Sanic Threads   |    3-7vCPU, 2-12Gb Memory, 3-7 Sanic Threads     |
+
+
 ### Debugging bot related issues while scaling up
 
 To test the Rasa [HTTP-API](https://rasa.com/docs/rasa/pages/http-api) ability to handle a large number of concurrent user activity we used the Rasa Pro [tracing](./tracing.mdx) capability