diff --git a/output/openapi/elasticsearch-openapi.json b/output/openapi/elasticsearch-openapi.json
index 0e9cfc6281..59ad098906 100644
--- a/output/openapi/elasticsearch-openapi.json
+++ b/output/openapi/elasticsearch-openapi.json
@@ -17822,7 +17822,7 @@
           "inference"
         ],
         "summary": "Create an OpenAI inference endpoint",
-        "description": "Create an inference endpoint to perform an inference task with the `openai` service.\n\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.",
+        "description": "Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\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.",
         "operationId": "inference-put-openai",
         "parameters": [
           {
diff --git a/output/openapi/elasticsearch-serverless-openapi.json b/output/openapi/elasticsearch-serverless-openapi.json
index 655bd5617d..cf5e6513b0 100644
--- a/output/openapi/elasticsearch-serverless-openapi.json
+++ b/output/openapi/elasticsearch-serverless-openapi.json
@@ -9644,7 +9644,7 @@
           "inference"
         ],
         "summary": "Create an OpenAI inference endpoint",
-        "description": "Create an inference endpoint to perform an inference task with the `openai` service.\n\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.",
+        "description": "Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\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.",
         "operationId": "inference-put-openai",
         "parameters": [
           {
diff --git a/output/schema/schema-serverless.json b/output/schema/schema-serverless.json
index ecaa181942..7af5184703 100644
--- a/output/schema/schema-serverless.json
+++ b/output/schema/schema-serverless.json
@@ -4648,7 +4648,7 @@
           "visibility": "public"
         }
       },
-      "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service.\n\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.",
+      "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\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.",
       "docId": "inference-api-put-openai",
       "docUrl": "https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-service-openai.html",
       "name": "inference.put_openai",
@@ -27155,7 +27155,7 @@
           }
         ]
       },
-      "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service.\n\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.",
+      "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\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.",
       "examples": {
         "PutOpenAiRequestExample1": {
           "description": "Run `PUT _inference/text_embedding/openai-embeddings` to create an inference endpoint that performs a `text_embedding` task. The embeddings created by requests to this endpoint will have 128 dimensions.",
diff --git a/output/schema/schema.json b/output/schema/schema.json
index e84cb90589..156abefb79 100644
--- a/output/schema/schema.json
+++ b/output/schema/schema.json
@@ -9388,7 +9388,7 @@
           "visibility": "public"
         }
       },
-      "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service.\n\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.",
+      "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\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.",
       "docId": "inference-api-put-openai",
       "docUrl": "https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-service-openai.html",
       "name": "inference.put_openai",
@@ -150788,7 +150788,7 @@
           }
         ]
       },
-      "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service.\n\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.",
+      "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\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.",
       "examples": {
         "PutOpenAiRequestExample1": {
           "description": "Run `PUT _inference/text_embedding/openai-embeddings` to create an inference endpoint that performs a `text_embedding` task. The embeddings created by requests to this endpoint will have 128 dimensions.",
diff --git a/specification/inference/put_openai/PutOpenAiRequest.ts b/specification/inference/put_openai/PutOpenAiRequest.ts
index 886905600e..0d1c03b005 100644
--- a/specification/inference/put_openai/PutOpenAiRequest.ts
+++ b/specification/inference/put_openai/PutOpenAiRequest.ts
@@ -28,7 +28,7 @@ import { integer } from '@_types/Numeric'
 /**
  * Create an OpenAI inference endpoint.
  *
- * Create an inference endpoint to perform an inference task with the `openai` service.
+ * Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.
  *
  * 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.