---
title: Deploy a Large Language Model (LLM)
description: The AI Data Plane Model Service can deploy Large Language Models
  (LLMs) close to your data in Capella to power your AI applications.
pubDate: 2026-08-17T09:53:44.266Z
antora:
  editUrl: https://github.com/couchbaselabs/docs-ai/edit/main/modules/build/pages/model-service/deploy-llm-model.adoc
  xref: xref:ai:build:model-service/deploy-llm-model.adoc[]
---

[Consult the llms.txt file for a full list of contents](/llms.txt)
[View original HTML](/ai/build/model-service/deploy-llm-model.html)

# Deploy a Large Language Model (LLM)

> The AI Data Plane Model Service can deploy Large Language Models (LLMs) close to your data in Capella to power your AI applications. 

The AI Data Plane Model Service offers endpoints for popular Large Language Models (LLMs) and supports features such as keyword filtering, caching, and guardrails.

## [](#prerequisites)Prerequisites

* To deploy a model, you must have the [Organization Owner](../../../cloud/organizations/organization-user-roles.md#organization-role-organization-owner) role.

## [](#procedure)Procedure

1. From your organization, go to **AI Data Plane** **Models**.
2. Click **Deploy New Model**.
3. Choose an LLM to deploy:

  1. Click **View All Models**.
  2. Click **Type:All** and deselect the **Text to Embedding** option, or use the search bar to find a specific LLM.
  3. Click the model you want to deploy.
  4. Click **Use Selected Model**.
4. (Optional) Change the autogenerated name for the LLM that you're deploying.
5. Choose the AWS region where you want to deploy the model.
6. Choose the compute and GPU size configuration to run the model.  
The minimum supported compute size available for the model in your chosen region is the default.
7. (Optional) Apply advanced configuration options:  
> [!CAUTION]  
> If you change or enable any advanced configurations, such as value adds or security features, after deployment, your existing Model Service API keys will stop working, and you must create a new API key. For more information, see [Value Adds and Security Features](../../api-guide/api-start.md#value-adds).  
Quantization  
Available to select models, reduce the model size and improve inference speed by applying a quantization level. Quantization can lead to a slight decrease in model accuracy, so test if it works well for your application.  
For more information, see [Configure LLM Performance](configure-llm-performance.md#quantization).  
Optimization  
Available to select models, use optimization techniques to enhance model performance and reduce latency. Choose from predefined profiles tailored for requirements such as low latency or high throughput.  
For more information, see [Configure LLM Performance](configure-llm-performance.md#optimization-profiles).  
Guardrails  
Use the guardrails configuration options to set up content moderation and safety features for your LLM.  
For more information, see [Configure Guardrails](configure-guardrails-security.md#guardrails).  
Jailbreak  
Detect and block jailbreak attempts to safeguard your model.  
For more information, see [Configure Jailbreak Protection](configure-guardrails-security.md#jailbreak).  
Caching  
The conversation cache stores past conversations to improve the conversational experience by retrieving past or semantically similar conversations from the cache.  
For more information, see [Configure Caching](configure-value-adds.md#caching).  
Async Processing  
Increase throughput by processing jobs asynchronously when system capacity becomes available. This allows task queuing, where tasks are handled as resources permit, improving overall efficiency.  
For more information, see [Configure Async Processing](configure-value-adds.md#async-processing).  
Keyword Filtering  
You can add up to 10 keywords separated by commas to filter from user prompts and responses.  
For more information, see [Configure Keyword Filtering](configure-value-adds.md#keyword-filtering).
8. Click **Deploy Model**.

## [](#next-steps)Next Steps

The **Models** page opens with your model in a deploying state. Once the model has finished deploying, you can view the model details and manage the model by expanding its listing on the **Models** page.

To create API keys for your deployed model, see [Generate Model Service API Keys](../../api-guide/api-start.md#generate-model-keys).