diff --git a/compiler/src/model/utils.ts b/compiler/src/model/utils.ts
index 769de32bfc..9f4d3e669e 100644
--- a/compiler/src/model/utils.ts
+++ b/compiler/src/model/utils.ts
@@ -667,7 +667,7 @@ export function hoistRequestAnnotations (
     } else if (tag === 'cluster_privileges') {
       const privileges = [
         'all', 'cancel_task', 'create_snapshot', 'grant_api_key', 'manage', 'manage_api_key', 'manage_ccr',
-        'manage_enrich', 'manage_ilm', 'manage_index_templates', 'manage_ingest_pipelines', 'manage_logstash_pipelines',
+        'manage_enrich', 'manage_ilm', 'manage_index_templates', 'manage_inference', 'manage_ingest_pipelines', 'manage_logstash_pipelines',
         'manage_ml', 'manage_oidc', 'manage_own_api_key', 'manage_pipeline', 'manage_rollup', 'manage_saml',
         'manage_security', 'manage_service_account', 'manage_slm', 'manage_token', 'manage_transform', 'manage_user_profile',
         'manage_watcher', 'monitor', 'monitor_ml', 'monitor_rollup', 'monitor_snapshot', 'monitor_text_structure',
diff --git a/output/openapi/elasticsearch-openapi.json b/output/openapi/elasticsearch-openapi.json
index 463b39949c..ccd6800566 100644
--- a/output/openapi/elasticsearch-openapi.json
+++ b/output/openapi/elasticsearch-openapi.json
@@ -15332,6 +15332,7 @@
           "inference"
         ],
         "summary": "Create an inference endpoint",
+        "description": "When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.\n\nIMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.\nFor built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.\nHowever, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.",
         "operationId": "inference-put",
         "parameters": [
           {
@@ -15424,6 +15425,7 @@
           "inference"
         ],
         "summary": "Create an inference endpoint",
+        "description": "When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.\n\nIMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.\nFor built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.\nHowever, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.",
         "operationId": "inference-put-1",
         "parameters": [
           {
diff --git a/output/openapi/elasticsearch-serverless-openapi.json b/output/openapi/elasticsearch-serverless-openapi.json
index 4057aac353..53b10bd877 100644
--- a/output/openapi/elasticsearch-serverless-openapi.json
+++ b/output/openapi/elasticsearch-serverless-openapi.json
@@ -8852,6 +8852,7 @@
           "inference"
         ],
         "summary": "Create an inference endpoint",
+        "description": "When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.\n\nIMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.\nFor built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.\nHowever, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.",
         "operationId": "inference-put",
         "parameters": [
           {
@@ -8944,6 +8945,7 @@
           "inference"
         ],
         "summary": "Create an inference endpoint",
+        "description": "When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.\n\nIMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.\nFor built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.\nHowever, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.",
         "operationId": "inference-put-1",
         "parameters": [
           {
diff --git a/output/schema/schema.json b/output/schema/schema.json
index 4ce4bc9393..cfb5e0b5f0 100644
--- a/output/schema/schema.json
+++ b/output/schema/schema.json
@@ -8548,9 +8548,14 @@
           "visibility": "public"
         }
       },
-      "description": "Create an inference endpoint",
+      "description": "Create an inference endpoint.\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.\n\nIMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.\nFor built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.\nHowever, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.",
       "docUrl": "https://www.elastic.co/guide/en/elasticsearch/reference/master/put-inference-api.html",
       "name": "inference.put",
+      "privileges": {
+        "cluster": [
+          "manage_inference"
+        ]
+      },
       "request": {
         "name": "Request",
         "namespace": "inference.put"
@@ -142374,7 +142379,7 @@
           }
         }
       },
-      "description": "Create an inference endpoint",
+      "description": "Create an inference endpoint.\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.\n\nIMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.\nFor built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.\nHowever, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.",
       "inherits": {
         "type": {
           "name": "RequestBase",
@@ -142412,7 +142417,7 @@
         }
       ],
       "query": [],
-      "specLocation": "inference/put/PutRequest.ts#L25-L44"
+      "specLocation": "inference/put/PutRequest.ts#L25-L54"
     },
     {
       "kind": "response",
diff --git a/specification/inference/put/PutRequest.ts b/specification/inference/put/PutRequest.ts
index be83f3ace8..3ddb505e53 100644
--- a/specification/inference/put/PutRequest.ts
+++ b/specification/inference/put/PutRequest.ts
@@ -23,10 +23,20 @@ import { RequestBase } from '@_types/Base'
 import { Id } from '@_types/common'
 
 /**
- * Create an inference endpoint
+ * Create an inference endpoint.
+ * When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
+ * After creating the endpoint, wait for the model deployment to complete before using it.
+ * To verify the deployment status, use the get trained model statistics API.
+ * Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
+ * Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
+ *
+ * IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
+ * For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.
+ * However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
  * @rest_spec_name inference.put
  * @availability stack since=8.11.0 stability=stable visibility=public
  * @availability serverless stability=stable visibility=public
+ * @cluster_privileges manage_inference
  */
 export interface Request extends RequestBase {
   path_parts: {