# Models > **📝 Note**: This is a Preview feature. Models integrate directly with existing SingleStore workflows and provide enterprise-grade performance, security, and observability to support building GenAI-ready applications. This Helios feature supports: 1. **LLM models**: Deploy and run large language models (LLMs) for text generation and conversational tasks. 2. **Embedding models**: Generate embeddings for semantic search, recommendations, and similarity tasks. 3. **ML models**: Build, train, and run custom ML models for specific applications. SingleStore Models supports the following deployment options: * Externally hosted: * Azure AI Services * Amazon Bedrock * Aura hosted: This option hosts the model on SingleStore Aura container service that provides lowest latency (model co-located with compute and data) and ensures that data never leaves the SingleStore VPC. It supports custom and open-source models. ## LLM Models SingleStore supports the following LLM models: | Provider | Publisher | Model | | ---------------------- | ----------------------------------------------------------- | -------------------------------------------------------------- | | Amazon Bedrock | Anthropic | | | Azure AI Services | OpenAI | | | Aura Hosted LLM Models | Open source models (Refer to License for individual models) | | ## Embedding Models SingleStore supports the following embedding models: | Provider | Publisher | Model | | ---------------------------- | ----------------------------------------------------------- | -------------------------------------------------------------------------------- | | Amazon Bedrock | Amazon | | | Amazon Bedrock | Anthropic | | | Azure AI Services | OpenAI | | | Aura Hosted embedding models | Open source models (Refer to License for individual models) | | ## ML Models Refer to [ML Functions](https://docs.singlestore.com/cloud/ai/ai-ml-functions/ml-functions.md) for more information. ## Create a Model Inference ## LLM Model Inference Create an LLM model inference by following these steps: 1. Navigate to the [Cloud Portal](http://portal.singlestore.com). 2. In the left navigation, select **AI > Models**. 3. Select the **LLM Models** tab, and then select **New**. 4. In **Select Model**, select the **Provider** and the model of your choice, and then select **Next**. 5. In **Model Settings**, enter and review the following information: | **Name** | Enter a name for the selected model. | | ------------------ | --------------------------------------------------------------------- | | **Description** | Enter the selected model description. | | **Region** | Select the region for the selected model. | | **Estimated Cost** | Displays the maximum number of tokens allowed for the selected model. | 6. Accept the terms of service agreement and select **Publish** for the selected LLM model inference deployment. Once the LLM model inference is deployed, use the LLM model inference via the following options: * API: Follow the sample code to start integrating with the model in either Python or Node.js. * AI Functions: Refer to [Text Processing Functions](https://docs.singlestore.com/cloud/ai/ai-ml-functions/ai-functions/#section-id235175307180532.md) for more information. ## Embedding Model Inference Create an embedding model inference by following these steps: 1. Navigate to the [Cloud Portal](http://portal.singlestore.com). 2. In the left navigation, select **AI > Models**. 3. Select the **Embedding Models** tab, and then select **New**. 4. In **Select Model**, select the **Provider** and the model of your choice, and then select **Next**. 5. In **Model Settings**, enter and review the following information: | **Name** | Enter a name for the selected model. | | ------------------ | --------------------------------------------------------------------- | | **Description** | Enter the selected model description. | | **Region** | Select the region for the selected model. | | **Estimated Cost** | Displays the maximum number of tokens allowed for the selected model. | 6. Accept the terms of service agreement and select **Publish** for the selected embedding model inference deployment. Once the embedding model inference is deployed, use the embedding model inference via the following options: * API: Follow the sample code to start integrating with the model in either Python or Node.js. * AI Functions: Refer to [Embedding Function](https://docs.singlestore.com/cloud/ai/ai-ml-functions/ai-functions/#section-id235175344319444.md) for more information. ## Train a New ML Model Refer to [Train a New ML Model](https://docs.singlestore.com/cloud/ai/ai-ml-functions/ml-functions/#section-id23517557347527.md) for more information. ## Manage LLMs and Embedding Model Inference Existing LLMs and embedding model inferences can be managed by performing following actions: * Explore the existing model inferences in Playground * Update * Share * Delete A notebook can be generated for the embedding model inference only. ## Use an Existing Model Inference To use the existing model inference, perform the following actions: * Select the model in the **Name** column and then select **Details** to view the sample code for the integration. * Create an Aura App API key to use the existing model inference. Refer to [Aura App API Keys](https://docs.singlestore.com/cloud/container-services/container-app-api-keys.md) for related information. ## Generate an Embeddings Notebook Embedding notebooks can be generated for an embedding model inference. Select **Details > Generate Notebook**. The embedding notebook uses an existing embedding model inference to generate embeddings for a column in a specified table. It stores the resulting vectors in a new column within the same table that enables it to manage and query the embeddings alongside the original data. In the **Embeddings Notebook** dialog, enter or select the following: | **Workspace** | Select aworkspace. | | ---------------------- | ---------------------------------------------------------------------- | | **Database** | Select a database. | | **Table** | Select a table. The selected table must have embeddable columns. | | **Source Column** | Select a source column. | | **Destination Column** | Enter the name of the destination column. | | **API Key Secret** | Select the Aura App API key created for the embedding model inference. | Select **Generate** to generate the embedding notebook. A **Vector Embedding Pipeline Notebook** is created. View the generated embedding notebook in **Editor > Shared**. ## Explore an Existing Model Inference in Playground To explore an existing model inference in the playground, select the model inference in the **Name** column and then select **Playground**. ## Embedding Model In **Text Input**, enter the text to generate embeddings and view the results in **Embedding Results**. ## Chat Completion Model In **Text Input**, enter the text to generate text or chat with the model. Set model parameters as required. **Parameters** | **System Prompt** | Enter the system prompt for the chat completion model. | | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Temperature** | Set the temperature of the model. Lower values are less random and near zero is deterministic. The default value is 0.7. It ranges between 0 and 1. | | **Max. Tokens** | Set the maximum number of tokens generated by the model. The default value is 512. It ranges between 1 and 1024. | | **Frequency Penalty** | Set the frequency penalty of the model. It penalizes new tokens based on existing frequency which reduces repetition. The default value is 0. It ranges between -2 and 2. | | **Top P** | Set the top P parameter of the model. It controls diversity via nucleus sampling (for example, 0.5 considers half of options). The default value is 1. It ranges between 0 and 1. | ## Update an Existing Model Inference To update an existing model inference, select the model in the **Name** column and then select **Update** in the upper right. Enter the updates required for the existing model inference, and select **Update**. ## Share an Existing Model Inference To share an existing model inference, navigate to **Models > LLM Models/Embedding Models**, select the ellipsis in the **Actions** column of the model, and select **Share**. Select the **User** or **Team** with which to share the model inference and select **Save**. > **📝 Note**: Custom models must be shared before use. Hence, to use a newly created model instance in an AI function, you have to explicitly share the model with the service account created during AI function installation. ## Delete an Existing Model Inference To delete an existing model inference, select the ellipsis in the **Actions** column of the model, and select **Delete**. ## Manage an Existing ML Model Refer to [Manage an Existing ML Model](https://docs.singlestore.com/cloud/ai/ai-ml-functions/ml-functions/#section-id235175587330847.md) for more information. *** Modified at: June 1, 2026 Source: [/cloud/ai/models/](https://docs.singlestore.com/cloud/ai/models/) (An index of the documentation is available at /llms.txt)