clarify usage scope

Author: eric-urban

articles/cognitive-services/Speech-Service/custom-speech-overview.md

@@ -21,6 +21,9 @@ Out of the box, speech to text utilizes a Universal Language Model as a base mod
 
 A custom model can be used to augment the base model to improve recognition of domain-specific vocabulary specific to the application by providing text data to train the model. It can also be used to improve recognition based for the specific audio conditions of the application by providing audio data with reference transcriptions.  
 
+> [!NOTE]
+> You pay to use Custom Speech models, but you are not charged for training a model. Usage includes hosting of your deployed custom endpoint in addition to using the endpoint for speech-to-text. For more information, see [Speech service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/).
+
 ## How does it work?
 
 With Custom Speech, you can upload your own data, test and train a custom model, compare accuracy between models, and deploy a model to a custom endpoint.

articles/cognitive-services/Speech-Service/how-to-custom-speech-deploy-model.md

@@ -17,6 +17,9 @@ zone_pivot_groups: speech-studio-cli-rest
 
 In this article, you'll learn how to deploy an endpoint for a Custom Speech model. With the exception of [batch transcription](batch-transcription.md), you must deploy a custom endpoint to use a Custom Speech model.  
 
+> [!NOTE]
+> You pay to use Custom Speech models, but you are not charged for training a model. Usage includes hosting of your deployed custom endpoint in addition to using the endpoint for speech-to-text. For more information, see [Speech service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/).
+
 You can deploy an endpoint for a base or custom model, and then [update](#change-model-and-redeploy-endpoint) the endpoint later to use a better trained model.
 
 > [!NOTE]

articles/cognitive-services/Speech-Service/how-to-custom-speech-train-model.md

@@ -19,7 +19,7 @@ zone_pivot_groups: speech-studio-cli-rest
 In this article, you'll learn how to train a custom model to improve recognition accuracy from the Microsoft base model. The speech recognition accuracy and quality of a Custom Speech model will remain consistent, even when a new base model is released.
 
 > [!NOTE]
-> You pay to use Custom Speech models, but you are not charged for training a model.
+> You pay to use Custom Speech models, but you are not charged for training a model. Usage includes hosting of your deployed custom endpoint in addition to using the endpoint for speech-to-text. For more information, see [Speech service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/).
 
 Training a model is typically an iterative process. You will first select a base model that is the starting point for a new model. You train a model with [datasets](./how-to-custom-speech-test-and-train.md) that can include text and audio, and then you test. If the recognition quality or accuracy doesn't meet your requirements, you can create a new model with additional or modified training data, and then test again.