Adds information about the importance of adaptive allocations

explore-analyze/elastic-inference/inference-api.md

@@ -9,15 +9,16 @@ products:
   - id: kibana
 ---
 
-# Integrate with third-party services
+# Inference integrations
 
-{{es}} provides a machine learning [inference API](https://www.elastic.co/docs/api/doc/elasticsearch/v8/operation/operation-inference-get-1) to create and manage inference endpoints to integrate with machine learning models provide by popular third-party services like Amazon Bedrock, Anthropic, Azure AI Studio, Cohere, Google AI, Mistral, OpenAI, Hugging Face, and more.
+{{es}} provides a machine learning [inference API](https://www.elastic.co/docs/api/doc/elasticsearch/v8/operation/operation-inference-get-1) to create and manage inference endpoints that integrate with services such as Elasticsearch (for built-in NLP models like [ELSER](/explore-analyze/machine-learning/nlp/ml-nlp-elser.md) and [E5](/explore-analyze/machine-learning/nlp/ml-nlp-e5.md)), as well as  popular third-party services like Amazon Bedrock, Anthropic, Azure AI Studio, Cohere, Google AI, Mistral, OpenAI, Hugging Face, and more.
 
-Learn how to integrate with specific services in the subpages of this section.
+You can create a new inference endpoint:
 
-## Inference endpoints UI [inference-endpoints]
+- using the [Create an inference endpoint API](https://www.elastic.co/docs/api/doc/elasticsearch/v8/operation/operation-inference-put-1)
+- through the [Inference endpoints UI](#add-inference-endpoints).
 
-You can also manage inference endpoints using the UI.
+## Inference endpoints UI [inference-endpoints]
 
 The **Inference endpoints** page provides an interface for managing inference endpoints.
 
@@ -33,7 +34,7 @@ Available actions:
 * Copy the inference endpoint ID
 * Delete endpoints
 
-## Add new inference endpoint
+## Add new inference endpoint [add-inference-endpoints]
 
 To add a new interference endpoint using the UI:
 
@@ -42,18 +43,33 @@ To add a new interference endpoint using the UI:
 1. Provide the required configuration details.
 1. Select **Save** to create the endpoint.
 
+If your inference endpoint uses a model deployed in Elastic’s infrastructure, such as ELSER, E5, or a model uploaded through Eland, you can configure [adaptive allocations](#adaptive-allocations) to dynamically adjust resource usage based on the current demand.
+
 ## Adaptive allocations [adaptive-allocations]
 
 Adaptive allocations allow inference services to dynamically adjust the number of model allocations based on the current load.
+This feature is only supported for models deployed in Elastic’s infrastructure, such as ELSER, E5, or models uploaded through Eland. It is not available for third-party services (for example, Alibaba Cloud, Cohere, or OpenAI), because those models are hosted externally and not deployed within your Elasticsearch cluster.
 
 When adaptive allocations are enabled:
 
 * The number of allocations scales up automatically when the load increases.
 * Allocations scale down to a minimum of 0 when the load decreases, saving resources.
 
-For more information about adaptive allocations and resources, refer to the trained model autoscaling documentation.
+### Allocation scaling behavior
+
+The behavior of allocations depends on several factors:
+
+- Deployment type (Elastic Cloud Hosted, Elastic Cloud Enterprise, or Serverless)
+- Usage level (low, medium, or high)
+- Optimization type ([ingest](/deploy-manage/autoscaling/trained-model-autoscaling.md#ingest-optimized) or [search](/deploy-manage/autoscaling/trained-model-autoscaling.md#search-optimized))
+
+For more information about adaptive allocations and resources, refer to the [trained model autoscaling](/deploy-manage/autoscaling/trained-model-autoscaling.md) documentation.
+
+::::{note}
+If you enable adaptive allocations and set the `min_number_of_allocations` to a value greater than `0`, you will be charged for the machine learning resources associated with your inference endpoint, even if no inference requests are sent.
 
-% TO DO: Add a link to trained model autoscaling when the page is available.%
+However, enabling adaptive allocations with a `min_number_of_allocations` greater than `0` helps ensure that the model remains available at all times, without delays due to scaling. This configuration may lead to higher resource usage and associated costs. Consider your workload and availability requirements when choosing the appropriate settings.
+::::
 
 ## Default {{infer}} endpoints [default-enpoints]