merge updates to links

Author: nullfunc

blog/2025-04-11-mar-product-updates.md

@@ -25,7 +25,7 @@ Wow - another month has gone by, time flies when you're having fun!
 
 Let us share some important updates regarding what we achieved at Defang in March:
 
-**Managed LLMs:** One of the coolest features we have released in a bit is [support for Managed LLMs (such as AWS Bedrock) through the `x-defang-llm` compose service extension](https://docs.defang.io/docs/concepts/managed-llms/managed-language-models). When coupled with the `defang/openai-access-gateway` service image, Defang offers the easiest way to [migrate your OpenAI-compatible application to cloud-native managed LLMs](https://docs.defang.io/docs/tutorials/deploying-openai-apps) without making any changes to your code. Support for GCP and DigitalOcean coming soon.
+**Managed LLMs:** One of the coolest features we have released in a bit is [support for Managed LLMs (such as AWS Bedrock) through the `x-defang-llm` compose service extension](https://docs.defang.io/docs/concepts/managed-llms/managed-language-models). When coupled with the `defang/openai-access-gateway` service image, Defang offers the easiest way to [migrate your OpenAI-compatible application to cloud-native managed LLMs](https://docs.defang.io/docs/tutorials/deploy-openai-apps) without making any changes to your code. Support for GCP and DigitalOcean coming soon.
 
 **Defang Pulumi Provider:** Last month, we announced a preview of the [Defang Pulumi Provider](https://github.com/DefangLabs/pulumi-defang), and this month we are excited to announce that V1 is now available in the [Pulumi Registry](https://www.pulumi.com/registry/packages/defang/). As much as we love Docker, we realize there are many real-world apps that have components that (currently) cannot be described completely in a Compose file. With the Defang Pulumi Provider, you can now leverage [the declarative simplicity of Defang with the imperative power of Pulumi](https://docs.defang.io/docs/concepts/pulumi#when-to-use-the-defang-pulumi-provider).
 

docs/concepts/managed-llms/openai-access-gateway.md

@@ -9,7 +9,7 @@ sidebar_position: 3000
 Defang makes it easy to deploy on your favourite cloud's managed LLM service with our [OpenAI Access Gateway](https://github.com/DefangLabs/openai-access-gateway). This service sits between your application and the cloud service and acts as a compatibility layer.
 It handles incoming OpenAI requests, translates those requests to the appropriate cloud-native API, handles the native response, and re-constructs an OpenAI-compatible response.
 
-See [our tutorial](/docs/tutorials/deploying-openai-apps) which describes how to configure the OpenAI Access Gateway for your application
+See [our tutorial](/docs/tutorials/deploy-openai-apps) which describes how to configure the OpenAI Access Gateway for your application
 
 ## Docker Provider Services
 

docs/tutorials/deploying-openai-apps-aws-bedrock.mdx renamed to docs/tutorials/deploy-openai-apps-aws-bedrock.mdx

@@ -112,12 +112,7 @@ To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazo
 :::info
 **Choosing the Right Model**
 
-<<<<<<< HEAD:docs/tutorials/deploying-openai-apps-aws-bedrock-gcp-vertex.mdx
-- For **AWS Bedrock**, use a Bedrock model ID (e.g., `anthropic.claude-3-sonnet-20240229-v1:0`) [See available Bedrock models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
-- For **GCP Vertex AI**, use a full model path (e.g., `google/gemini-2.5-pro-preview-03-25`) [See available Vertex models](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/call-vertex-using-openai-library#client-setup).
-=======
 - For **GCP Vertex AI**, use a full model path (e.g., `google/gemini-2.5-pro-preview-03-25`) [See available Vertex models](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/call-vertex-using-openai-library#client-setup)
->>>>>>> eric-update-for-play-ground:docs/tutorials/deploying-openai-apps-gcp-vertex.mdx
 :::
 
 Alternatively, Defang supports model mapping through the openai-access-gateway. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to
@@ -172,8 +167,5 @@ You now have a single app that can:
 - Talk to **GCP Vertex AI**
 - Use the same OpenAI-compatible client code
 - Easily switch cloud providers by changing a few environment variables
-<<<<<<< HEAD:docs/tutorials/deploying-openai-apps-aws-bedrock-gcp-vertex.mdx
-=======
 :::
 
->>>>>>> eric-update-for-play-ground:docs/tutorials/deploying-openai-apps-gcp-vertex.mdx

docs/tutorials/deploying-openai-apps-gcp-vertex.mdx renamed to docs/tutorials/deploy-openai-apps-gcp-vertex.mdx

@@ -0,0 +1,15 @@
+---
+title: Deploy your OpenAI Apps
+sidebar_position: 45
+---
+
+# Deploy Your OpenAI Apps
+
+Defang currently supports LLM using AWS Bedrock and GCP Vertex AI. Follow the link below for your specific platform.
+
+- [AWS Bedrock](/docs/tutorials/deploy-openai-apps-aws-bedrock/)
+- [GCP Vertex AI](/docs/tutorials/deploy-openai-apps-gcp-vertex/).
+
+
+
+