> ## Documentation Index
> Fetch the complete documentation index at: https://docs.shaktistudio.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Dedicated Endpoint

Private endpoints provide dedicated infrastructure for your model deployments, ensuring better performance and reliability.

<img src="https://mintcdn.com/simplismart-2/1ywLKuDLFRMavXjh/images/221.png?fit=max&auto=format&n=1ywLKuDLFRMavXjh&q=85&s=6cde51f593c65f3b390fd91577b7645e" alt="221 Pn" width="1650" height="1125" data-path="images/221.png" />

## **Benefits of using a private endpoint**

* **Dedicated resources**: No sharing of compute resources with other users.
* **Enhanced performance**: Improved response times and throughput.
* **Higher reliability**: Reduced risk of downtime and performance degradation.

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## Deploying your model on a Private Endpoint

To deploy your model on a private endpoint, follow these outlined processes. Each step includes a link for detailed instructions, ensuring a smooth launch and use of your deployed model.

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## **Model Optimisation**

1. You can either choose an available model from our [model marketplace](https://uat-infer.shakticloud.ai/model-marketplace) or add your own.
2. Optimise your model for deployment by visiting the [Models](https://uat-infer.shakticloud.ai/my-models) page and using our optimisation tools.

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## **Model Deployment**

You can deploy your optimised model on a private endpoint by selecting your cloud provider as **Yotta Cloud**. Click here for detailed **deployment** steps.

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## **Inferencing**

You can invoke your deployed models from the **API tab** of the model deployment.