# Shared Embedding Model Service

The Shared Embedding Model Service is a multi-tenant embedding service that provides a shared embedding model for AI Functions. Unlike the dedicated embedding service provisioned per user, this shared service allows multiple users and organizations to use the same embedding infrastructure without provisioning individual resources.

## Overview

When AI Functions are installed, a shared embedding model is automatically provisioned. This model runs on SingleStore Aura container service and is available to all organizations with AI Functions enabled.

The shared embedding model is powered by Qwen3-Embedding-0.6B, a lightweight yet high-quality embedding model optimized for text vectorization, semantic search, and RAG applications. The shared service is currently available in US East (N. Virginia).

| Property     | Value                      |
| ------------ | -------------------------- |
| Model name   | `shared-qwen3-embed-0-6b`  |
| Model family | Qwen3 Embedding            |
| Dimensions   | 1024                       |
| Region       | US East (N. Virginia)      |
| Hosting      | Aura-hosted (multi-tenant) |

## How It Works

The shared embedding model service differs from dedicated embedding models in the following ways:

|                | Shared Embedding Model                    | Dedicated Embedding Model           |
| -------------- | ----------------------------------------- | ----------------------------------- |
| Infrastructure | Multi-tenant, shared across organizations | Single-tenant, provisioned per user |
| Provisioning   | Automatic during AI Functions install     | Manual setup required               |
| Scaling        | SingleStore-managed                       | User-configurable                   |
| Model          | Qwen3-Embedding-0.6B                      | User's choice                       |

The shared embedding service runs on GPU-accelerated infrastructure and automatically scales based on demand. SingleStore manages the lifecycle, scaling, and availability of the service.

## Prerequisites

* A SingleStore Helios workspace group with AI Functions installed.
* The Shared Embedding Service feature must be enabled for your organization.

## Select the Embedding Model

You can view and change your default embedding model in the Cloud Portal:

1. Navigate to **AI > AI & ML Functions**.

2. Select your workspace group.

3. On the **AI Functions** tab, in **Settings**, select **Edit**.

4. On the **Edit AI Functions** page, in **Models**, select the **Embedding Model** section.

5. The available embedding models include both the shared model (`shared-qwen3-embed-0-6b`) and the dedicated models provisioned for your organization.

To update the default embedding model used by `EMBED_TEXT`, select the desired model and save the change.

## Usage

Use the shared embedding model with the `EMBED_TEXT` function by specifying the model name `shared-qwen3-embed-0-6b`. The following examples use the `customer_reviews` table.

## Basic Usage

```
SELECT cluster.EMBED_TEXT(
    'The headphones have poor sound quality, disconnect frequently, and the battery lasts only a few hours. I would not recommend this product.',
    'shared-qwen3-embed-0-6b'
) AS embedding;
```

## Using the Default Model

If the shared embedding model is set as your default embedding model, you can remove the model parameter:

```
SELECT cluster.EMBED_TEXT(
    'The headphones have poor sound quality, disconnect frequently, and the battery lasts only a few hours. I would not recommend this product.'
) AS embedding;
```

## Generating Embeddings for a Table Column

```
SET batch_external_functions = AUTO;

UPDATE customer_reviews
SET review_embedding = cluster.EMBED_TEXT(review_text, 'shared-qwen3-embed-0-6b')
WHERE review_embedding IS NULL;
```

## Performance Considerations

* **Batching**: Use `SET batch_external_functions = AUTO`, before bulk embedding operations to enable automatic batching, which significantly improves throughput.
* **Token limits**: The Qwen3-Embedding-0.6B model processes text in token chunks. For large text inputs, the service automatically handles tokenization and batching.
* **Region**: For lowest latency, use workspace groups in the same region.

> **📝 Note**: The shared embedding model is available only in the US East (N. Virginia) region. As a shared multi-tenant service, throughput may vary based on overall system demand. The model selection is fixed to Qwen3-Embedding-0.6B; for custom model requirements, use a dedicated embedding model.

## Related Topics

* [AI Functions](https://docs.singlestore.com/cloud/ai/ai-ml-functions/ai-functions.md)
* [EMBED\_TEXT](https://docs.singlestore.com/cloud/ai/ai-ml-functions/ai-functions/#section-id235175351219875.md)
* [Working with Vector Data](https://docs.singlestore.com/cloud/developer-resources/functional-extensions/working-with-vector-data.md)

***

Modified at: August 4, 2026

Source: [/cloud/ai/ai-ml-functions/shared-embedding-model-service/](https://docs.singlestore.com/cloud/ai/ai-ml-functions/shared-embedding-model-service/)

(An index of the documentation is available at /llms.txt)