update ovhai cli link

MathieuBsqt · MathieuBsqt · commit 348ac163403f · 2025-07-28T16:12:04.000+02:00
diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_09_streamlit_speech_to_text_app/guide.en-gb.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_09_streamlit_speech_to_text_app/guide.en-gb.md
@@ -30,7 +30,7 @@ To deploy your app, you need:
 - An access to the [OVHcloud Control Panel](/links/manager).
 - An AI Deploy Project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account
 - A [user for AI Deploy](/pages/public_cloud/ai_machine_learning/gi_01_manage_users).
-- [The OVHcloud AI CLI](https://cli.bhs.ai.cloud.ovh.net/) **and** [Docker](https://www.docker.com/get-started) installed on your local computer, **or** only an access to a Debian Docker Instance on the [Public Cloud](/links/manager).
+- [The OVHcloud AI CLI](/pages/public_cloud/ai_machine_learning/cli_10_howto_install_cli) **and** [Docker](https://www.docker.com/get-started) installed on your local computer, **or** only an access to a Debian Docker Instance on the [Public Cloud](/links/manager).
 - To deploy your app, you must have the full code of the application, either by cloning the [GitHub repository](https://github.com/ovh/ai-training-examples/tree/main/apps/streamlit/speech-to-text), or by having followed our [blog article](https://blog.ovhcloud.com/how-to-build-a-speech-to-text-application-with-python-1-3/) that taught you how to build this app step by step.
 - If you want the diarization option (speakers differentiation), you will need an access token. This token will be requested at the launch of the application. To create your token, follow the steps indicated on the [model page](https://huggingface.co/pyannote/speaker-diarization). If the token is not specified, the application will be launched without this feature.
 
diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_14_img_segmentation_app/guide.en-gb.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_14_img_segmentation_app/guide.en-gb.md
@@ -137,7 +137,7 @@ Once your object containers are created, you will see them in the Object Storage
 
 #### 1.2 - Upload data via CLI
 
-To follow this part, make sure you have installed the [ovhai CLI](https://cli.bhs.ai.cloud.ovh.net/) on your computer or on an instance.
+To follow this part, make sure you have installed the [ovhai CLI](/pages/public_cloud/ai_machine_learning/cli_10_howto_install_cli) on your computer or on an instance.
 
 As in the Control Panel, you will have to specify the `region`, the `name of your container` and the `path` where your data will be located. The creation of an object container can be done with the following command:
 
diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_17_streamlit_whisper/guide.en-gb.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_17_streamlit_whisper/guide.en-gb.md
@@ -94,7 +94,7 @@ You can create your Object Storage bucket using either the UI (OVHcloud Control
 >>
 > **Using ovhai CLI**
 >>
->> To follow this part, make sure you have installed the [ovhai CLI](https://cli.bhs.ai.cloud.ovh.net/) on your computer or on an instance.
+>> To follow this part, make sure you have installed the [ovhai CLI](/pages/public_cloud/ai_machine_learning/cli_10_howto_install_cli) on your computer or on an instance.
 >>
 >> As in the Control Panel, you will have to specify the `datastore_alias` and the `name` of your bucket. Create your Object Storage bucket as follows:
 >>
diff --git a/pages/public_cloud/ai_machine_learning/endpoints_guide_04_billing_concept/guide.en-gb.md b/pages/public_cloud/ai_machine_learning/endpoints_guide_04_billing_concept/guide.en-gb.md
@@ -31,27 +31,23 @@ By following this model lifecycle process, OVHcloud ensures that customers are w
 
 ## Billing principles
 
-Here is the model billing overview for AI Endpoints.
-
-> [!primary]
->
-> In appreciation of their continued support, our **Beta testers will have the possibility to keep using their existing API access keys and create new ones and won't be billed until 31th May**. After this date, the pricing will be implemented for them and clearly outlined in the table below, which details the categories, models, and their respective pricing information:
->
+Here is the model billing overview for AI Endpoints:
 
 | Category        | Model            | Price ($)  | Price (€)  | Unit Price |
 | -------------- | --------------- | ------ | ------ | ---------- |
-| Large Language Model (LLM)            | Llama 3.3 70B Instruct          | 0.70   | 0.67   | per 1M tokens          |
-| Large Language Model (LLM)            | Llama 3.1 70B Instruct          | 0.70   | 0.67   | per 1M tokens          |
-| Large Language Model (LLM)            | Mixtral 8x7B Instruct v0.1         | 0.65   | 0.63   | per 1M tokens          |
+| Large Language Model (LLM)            | Llama 3.3 70B Instruct          | 0.74   | 0.67   | per 1M tokens          |
+| Large Language Model (LLM)            | Llama 3.1 70B Instruct          | 0.74   | 0.67   | per 1M tokens          |
+| Large Language Model (LLM)            | Mixtral 8x7B Instruct v0.1         | 0.70   | 0.63   | per 1M tokens          |
 | Large Language Model (LLM)            | Mistral-Nemo-Instruct-2407          | 0.14   | 0.13   | per 1M tokens          |
-| Large Language Model (LLM)            | Llama 3.1 8B Instruct          | 0.10   | 0.10   | per 1M tokens          |
-| Large Language Model (LLM)            | Mistral 7B Instruct v0.3          | 0.10   | 0.10   | per 1M tokens          |
-| Reasoning LLM   | DeepSeek R1         | Free   | Free   | per 1M tokens          |
-| Reasoning LLM   | DeepSeek R1 Distill Llama 70B          | 0.70   | 0.67   | per 1M tokens          |
-| Code LLM        | Qwen2.5 Coder 32B Instruct          | 0.90   | 0.87   | per 1M tokens          |
-| Code LLM        | Mamba Codestral 7B v0.1          | 0.20   | 0.19   | per 1M tokens          |
-| Visual LLM      | Qwen2.5 VL 72B Instruct         | 0.95   | 0.91   | per 1M tokens          |
-| Visual LLM      | Llava Next Mistral 7B          | 0.30   | 0.29   | per 1M tokens          |
+| Large Language Model (LLM)            | Llama 3.1 8B Instruct          | 0.11   | 0.10   | per 1M tokens          |
+| Large Language Model (LLM)            | Mistral 7B Instruct v0.3          | 0.11   | 0.10   | per 1M tokens          |
+| Reasoning LLM   | DeepSeek R1 Distill Llama 70B          | 0.74   | 0.67   | per 1M tokens          |
+| Reasoning LLM   | Qwen3 32B          | 0.09   | 0.08   | per 1M tokens          |
+| Code LLM        | Qwen2.5 Coder 32B Instruct          | 0.96   | 0.87   | per 1M tokens          |
+| Code LLM        | Mamba Codestral 7B v0.1          | 0.21   | 0.19   | per 1M tokens          |
+| Visual LLM      | Mistral Small 3.2 24B Instruct 2506          | 0.10   | 0.09   | per 1M tokens          |
+| Visual LLM      | Qwen2.5 VL 72B Instruct         | 1.01   | 0.91   | per 1M tokens          |
+| Visual LLM      | Llava Next Mistral 7B          | 0.32   | 0.29   | per 1M tokens          |
 | Embeddings     | BGE Multilingual Gemma2          | 0.01   | 0.01   | per 1M tokens          |
 | Embeddings     | BGE-M3          | 0.01   | 0.01   | per 1M tokens          |
 | Embeddings     | BGE Base EN v1.5          | 0.01   | 0.005   | per 1M tokens          |
diff --git a/pages/public_cloud/ai_machine_learning/training_tuto_01_train_your_first_model/guide.en-gb.md b/pages/public_cloud/ai_machine_learning/training_tuto_01_train_your_first_model/guide.en-gb.md
@@ -48,7 +48,7 @@ You will follow different steps to export your data, train your model and save i
 
 This step is optional because you may load some open datasets through libraries, commands, etc., so you will not need to upload your own data to the cloud. 
 
-On the other hand, you can upload your data (dataset, python and requirements files, etc.) to the cloud, in the Object Storage. This can be done in two ways: either from the [OVHcloud Control Panel](https://www.ovh.com/manager/#/public-cloud/) or via the [ovhai CLI](https://cli.bhs.ai.cloud.ovh.net/).
+On the other hand, you can upload your data (dataset, python and requirements files, etc.) to the cloud, in the Object Storage. This can be done in two ways: either from the [OVHcloud Control Panel](https://www.ovh.com/manager/#/public-cloud/) or via the [ovhai CLI](/pages/public_cloud/ai_machine_learning/cli_10_howto_install_cli).
 
 **This data can be deleted at any time.**
 
@@ -73,7 +73,7 @@ Once your object container is created, you will see it in the Object Storage lis
 
 #### 1.2 - Upload your data via CLI
 
-To follow this part, make sure you have installed the [ovhai CLI](https://cli.bhs.ai.cloud.ovh.net/) on your computer or on an instance. 
+To follow this part, make sure you have installed the [ovhai CLI](/pages/public_cloud/ai_machine_learning/cli_10_howto_install_cli) on your computer or on an instance. 
 
 As in the Control Panel, you will have to specify the `region`, the `name of your container` and the `path` where your data will be located. The creation of your object container can be done by the following command:
 
@@ -93,7 +93,7 @@ This `.zip` file can now be accessed from all OVHcloud AI products, either with
 
 ### Step 2 - Launch your training job and attach your data to its environment
 
-To launch your training job, you can also use the [OVHcloud Control Panel](https://www.ovh.com/manager/#/public-cloud/) or the [ovhai CLI](https://cli.bhs.ai.cloud.ovh.net/).
+To launch your training job, you can also use the [OVHcloud Control Panel](https://www.ovh.com/manager/#/public-cloud/) or the [ovhai CLI](/pages/public_cloud/ai_machine_learning/cli_10_howto_install_cli).
 
 #### 2.1 - Launch a training job via UI (Control Panel)
 
diff --git a/pages/public_cloud/ai_machine_learning/training_tuto_09_train_model_export_onnx/guide.en-gb.md b/pages/public_cloud/ai_machine_learning/training_tuto_09_train_model_export_onnx/guide.en-gb.md
@@ -39,7 +39,7 @@ You can create the bucket that will store your ONNX model at the end of the trai
 
 #### Create your bucket via ovhai CLI
 
-To follow this part, make sure you have installed the [ovhai CLI](https://cli.bhs.ai.cloud.ovh.net/) on your computer or on an instance.
+To follow this part, make sure you have installed the [ovhai CLI](/pages/public_cloud/ai_machine_learning/cli_10_howto_install_cli) on your computer or on an instance.
 
 As in the Control Panel, you will have to specify the `region`and the `name` (**cnn-model-onnx**) of your bucket. Create your Object Storage bucket as follows:
 
