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docs/concepts/managed-llms/managed-language-models.md

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In order to leverage cloud-native managed language models from your Defang services, all you need to do is add the `x-defang-llm` extension to the service config and Defang will configure the approprate roles and permissions for you.
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:::tip
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Ensure you have the necessary permissions to access the model you intend to use. To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) or [GCP Vertex AI model permissions](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-partner-models?hl=en#grant-permissions).
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Ensure you have the necessary permissions to access the model you intend to use. To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) or [GCP Vertex AI model access](https://cloud.google.com/vertex-ai/generative-ai/docs/control-model-access).
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:::
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## Example

docs/tutorials/deploying-openai-apps-aws-bedrock-gcp-vertex.mdx

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Choose the correct `MODEL` depending on which cloud provider you are using.
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Ensure you have the necessary permissions to access the model you intend to use.
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To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) or [GCP Vertex AI model permissions](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-partner-models?hl=en#grant-permissions).
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To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) or [GCP Vertex AI model access](https://cloud.google.com/vertex-ai/generative-ai/docs/control-model-access).
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:::info
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**Choosing the Right Model**

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