Adds information about cooldown periods for trained model autoscaling in Serverless

deploy-manage/autoscaling.md

@@ -41,6 +41,10 @@ The available resources of self-managed deployments are static, so trained model
 
 Trained model autoscaling automatically adjusts the resources allocated to trained model deployments based on demand. This feature is available on all cloud deployments (ECE, ECK, ECH) and {{serverless-short}}. See [Trained model autoscaling](/deploy-manage/autoscaling/trained-model-autoscaling.md) for details.
 
+::::{note}
+{applies_to}`serverless: ga` In {{serverless-short}}, trained model deployments remain active for 24 hours after the last inference request. After that, they scale down to zero. When scaled up again, they stay active for 5 minutes before they can scale down. These cooldown periods prevent unnecessary scaling and ensure models are available when needed.
+::::
+
 Trained model autoscaling supports:
 * Scaling trained model deployments
 

deploy-manage/autoscaling/trained-model-autoscaling.md

@@ -22,7 +22,7 @@ There are two ways to enable autoscaling:
 * through APIs by enabling adaptive allocations
 * in {{kib}} by enabling adaptive resources
 
-For {{serverless-short}} projects, trained model autoscaling is automatically enabled and cannot be disabled.
+{applies_to}`serverless: ga` For {{serverless-short}} projects, trained model autoscaling is always enabled and cannot be turned off. Trained model deployments remain active for 24 hours after the last inference request before scaling down to zero. When scaled up again, they stay active for 5 minutes before they can scale down. These cooldown periods prevent unnecessary scaling and ensure models are available when needed.
 
 ::::{important}
 To fully leverage model autoscaling in {{ech}}, {{ece}}, and {{eck}}, it is highly recommended to enable [{{es}} deployment autoscaling](../../deploy-manage/autoscaling.md).

deploy-manage/cloud-organization/billing/elasticsearch-billing-dimensions.md

@@ -47,6 +47,10 @@ You can control costs using the following strategies:
 
     * When starting or updating a trained model deployment, [Enable adaptive resources](../../autoscaling/trained-model-autoscaling.md#enabling-autoscaling-in-kibana-adaptive-resources) and set the VCU usage level to **Low**.
     * When using the inference API for {{es}} or ELSER, [enable `adaptive_allocations`](../../autoscaling/trained-model-autoscaling.md#enabling-autoscaling-through-apis-adaptive-allocations).
+
+    ::::{note}
+    {applies_to}`serverless: ga` In {{serverless-short}}, trained model deployments scale down to zero only after 24 hours without any inference requests. After scaling up, they remain active for 5 minutes before they can scale down again. During these cooldown periods, you will continue to be billed for the active resources.
+    ::::
 
 * **Indexing Strategies:** Consider your indexing strategies and how they might impact overall VCU usage and costs: