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articles/container-apps/ai-integration.md

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ms.author: jefmarti
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ms.service: azure-container-apps
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ms.date: 10/03/2025
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ms.topic: article
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# AI integration with Azure Container Apps
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Use GPU accelerated workload profiles to meet a variety of your AI workload needs, including:
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- **[Serverless GPUs](https://learn.microsoft.com/azure/container-apps/gpu-serverless-overview)**: Ideal for variable traffic scenarios and cost-sensitive inference workloads.
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- **[Serverless GPUs](/container-apps/gpu-serverless-overview)**: Ideal for variable traffic scenarios and cost-sensitive inference workloads.
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- **Dedicated GPUs**: best for continuous, low-latency inference scenarios.
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- **Scale to zero**: automatically scale down idle GPU resources to minimize costs.
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Dynamic Sessions provide a secure, isolated environment for executing AI-generated code. Perfect for scenarios like sandboxed execution, code evaluation, or AI agents.
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Supported session types include:
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- **[Code interpreters](https://learn.microsoft.com/azure/container-apps/sessions-code-interpreter)**: a platform-managed container that supports executing code in multiple programming languages, including Python and JavaScript.
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- **[Custom containers](https://learn.microsoft.com/azure/container-apps/sessions-custom-container)**: create a sessions pool using a custom container for specialized workloads or additional language support.
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- **[Code interpreters](/azure/container-apps/sessions-code-interpreter)**: a platform-managed container that supports executing code in multiple programming languages, including Python and JavaScript.
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- **[Custom containers](/azure/container-apps/sessions-custom-container)**: create a sessions pool using a custom container for specialized workloads or additional language support.
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## Deploying Azure AI Foundry models
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