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serverless/pages/ml-nlp-auto-scale.mdx

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@@ -20,11 +20,6 @@ Trained model autoscaling is available for Search, Observability, and Security p
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Security and Observability projects are only charged for data ingestion and retention. They are not charged for processing power (vCU usage), which is used for more complex operations, like running advanced search models. For example, in Search projects, models such as ELSER require significant processing power to provide more accurate search results.
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Search projects are given access to more processing resources, while Security and Observability projects have lower limits. This difference is reflected in the UI configuration: Search projects have higher resource limits compared to Security and Observability projects to accommodate their more complex operations.
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On Serverless, adaptive allocations are automatically enabled for all project types.
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However, the "Adaptive resources" control is not displayed in Kibana for Observability and Security projects.
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## Enabling autoscaling through APIs - adaptive allocations
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Model allocations are independent units of work for NLP tasks.
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Refer to the tables in the auto-scaling-matrix section to find out the setings for the level you selected.
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<DocImage size="xxl" url="../images/ml-nlp-deployment.png" alt="ML model deployment with adaptive resources enabled." />
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<DocImage size="xxl" url="../images/ml-nlp-deployment.png" alt="ML model deployment with adaptive resources enabled." />
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Search projects are given access to more processing resources, while Security and Observability projects have lower limits. This difference is reflected in the UI configuration: Search projects have higher resource limits compared to Security and Observability projects to accommodate their more complex operations.
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On Serverless, adaptive allocations are automatically enabled for all project types.
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However, the "Adaptive resources" control is not displayed in Kibana for Observability and Security projects.
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## Model deployment resource matrix
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