AI Providers Overview¶

This section provides AI provider-specific guidance and resource examples for deploying Pixee Enterprise Server on major cloud platforms.

Advanced AI Model Configuration¶

SCA Models¶

Software Composition Analysis (SCA) models enable enhanced vulnerability detection in dependencies and third-party libraries. This feature is disabled by default and can be enabled in the AI settings.

Embedded ClusterHelm Deployment

To configure SCA models in Embedded Cluster deployments:

Navigate to the admin console, select the Config tab, then go to the AI Settings section.

Enable SCA Models: Toggle to enable Software Composition Analysis models (disabled by default)
SCA Model Name: Optionally specify a custom LLM model for SCA analysis (only available when SCA is enabled, defaults to gpt-4.1)

To configure SCA models in Helm deployments:

global:
  pixee:
    ai:
      # Enable SCA models (disabled by default)
      scaModelsEnabled: true
      # Optionally specify a custom model name (defaults to gpt-4.1)
      scaModelName: "gpt-4.1"

Deep Research Models¶

Deep Research models provide advanced analysis capabilities for complex code understanding tasks. These models are only used when SCA is enabled.

Embedded ClusterHelm Deployment

To configure Deep Research models in Embedded Cluster deployments:

Navigate to the admin console, select the Config tab, then go to the AI Settings section.

Deep Research Model Name: Optionally specify a custom LLM model for deep research analysis (only available when SCA is enabled, defaults to o4-mini-deep-research)

To configure Deep Research models in Helm deployments:

global:
  pixee:
    ai:
      # Enable SCA first (required for deep research models)
      scaModelsEnabled: true
      # Then specify a custom model for deep research (defaults to o4-mini-deep-research)
      deepResearchModelName: "o4-mini-deep-research"

OpenAI¶

Requirements¶

AI Provider integration requires:

An AI provider API key with access to the required models
An AI provider endpoint, or default to the provider's public API endpoint
Model names for the reasoning and fast models from your chosen AI provider

For OpenAI, see OpenAI's page on creating an API Key for more information.

OpenAI-compatible APIs¶

Providers such as Azure AI Foundry and AWS Bedrock provide endpoints for connecting to both OpenAI and non-OpenAI models via a consistent OpenAI-compatible API. This enables connection to models from these providers with the same configuration as if it was OpenAI directly.

Requirements¶

An AI provider endpoint
Model names for the reasoning and fast models from your chosen AI provider
An AI provider API key with access to the required models

Example: Connecting to DeepSeek-R1 via Azure AI Foundry¶

Azure AI Foundry hosts a variety of foundation models from industry-leading providers. In this example, Pixee will be configured to connect to DeepSeek-R1 using the OpenAI-compatible API endpoints using the same process for connecting to OpenAI's API directly. This example assumes that the model has already been deployed in the target Azure environment, and a model deployment API key has already been created.

Input the OpenAI-compatible endpoint for your deployment¶

Azure AI Foundry provides a cononacle URL endpoint for AI Foundry model deployments. It's usually of the format https://{foundary-instance-name}-resource.service.ai.azure.com/models.

Input your API key¶

Copy the deployment's endpoint key from the Azure AI Foundry portal and paste it into the API key field.

Specify the reasoning and fast model names¶

Find the DeepSeek-R1 model name in the AI Foundry portal and paste it into the reasoning and fast model name fields. We want to use the same model for this example to keep things simple. But we could choose a different model if we wanted to, so long as it's available in our AI Foundry instance.

Verify with preflight checks¶

Preflight checks should run after the configuration is saved and should successfully connect to the model.

Azure OpenAI¶

Requirements¶

Azure OpenAI integration requires:

Integration with Azure OpenAI requires model deployments of the following recommended models:
- o3-mini (version: 2025-01-31) -- as fast model
- o3-mini (version: 2025-01-31) -- as reasoning model

Databricks AI¶

Requirements¶

Databricks AI integration requires:

Integration with Databricks AI serving endpoints requires:
- Databricks workspace with Mosaic AI Model Serving enabled
- External model endpoints for "o3-mini" as fast and reasoning models
- Databricks personal access token with serving endpoint access

Configuration¶

To integrate Pixee Enterprise Server with Databricks AI serving endpoints:

Create the required serving endpoints in Databricks following the Databricks external models documentation. You need to deploy the following endpoint:
- o3-mini (for o3-mini model)
Configure Pixee Enterprise Server to use your Databricks workspace URL as the OpenAI base URL with your Databricks PAT as the API key.
Verify connectivity by checking that both endpoints are accessible from your Pixee Enterprise Server deployment.

Info

Databricks integration uses the OpenAI-compatible API, so select "OpenAI" as the provider type when configuring through the admin console, and be sure to update the OpenAI Endpoint to match your Databricks base url.

Common Issues¶

Ensure your Databricks PAT has permission to access the serving endpoints
Verify network connectivity between Pixee Enterprise Server and your Databricks workspace
Check that the required endpoint ("o3-mini") is deployed and running
When using the embedded cluster installation, select "OpenAI" as the provider type since Databricks uses OpenAI-compatible APIs
Be sure to set the OpenAI endpoint to your Databricks base url

Azure Anthropic¶

Azure Anthropic allows you to access Anthropic models (such as Claude) through Azure's infrastructure.

Requirements¶

An Azure Anthropic API endpoint for your model deployments
An API key with access to the required models
Model names for the reasoning and fast models

Configuration¶

Embedded ClusterHelm Deployment

Navigate to the admin console, select the Config tab, then go to the AI Settings section.

Select Azure Anthropic as the Default LLM Provider and configure:

API Key: Your Azure Anthropic API key
Endpoint: The Azure Anthropic API endpoint for your model deployments
Reasoning Model Name: Model for complex reasoning tasks (default: claude-sonnet-4-20250514)
Fast Model Name: Model for quick response tasks (default: claude-sonnet-4-20250514)

To configure Azure Anthropic in Helm deployments, add the following to your values.yaml:

global:
  pixee:
    ai:
      enabled: true
      default:
        provider: "azure-anthropic"
        model: "claude-sonnet-4-20250514"
        apiKey: "<your Azure Anthropic API key>"
        endpoint: "<your Azure Anthropic endpoint>"
      reasoning:
        model: "claude-sonnet-4-20250514"
      fast:
        model: "claude-sonnet-4-20250514"

Web Search LLM Model¶

When an AI provider is configured, you can optionally specify a model for web-search-enabled queries. This model is used for tasks that benefit from real-time web search capabilities during analysis.

Embedded ClusterHelm Deployment

Navigate to the admin console, select the Config tab, then go to the AI Settings section.

When using OpenAI or Azure AI Foundry as your provider, a Web Search Model Name field is available. The default is gpt-5.2.

To configure a web search model in Helm deployments:

global:
  pixee:
    ai:
      webSearch:
        model: "gpt-5.2"

Note

Web search models are currently supported for OpenAI and Azure AI Foundry providers only.

Oracle Cloud Infrastructure Generative AI Services¶

Requirements¶

Oracle Cloud Infrastructure (OCI) integration requires: - Integration with Oracle Cloud Infrastructure Generative AI Services * Llama and custom models can be deployed, please contact support for up-to-date instructions based on your deployment type. *