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Foundry Tools in Fabric (preview)

Important

This feature is in preview.

Foundry Tools help developers and organizations rapidly create intelligent, cutting-edge, market-ready, and responsible applications with prebuilt and customizable APIs and models. Formerly named Azure Cognitive Services, Foundry Tools empower developers even when they don't have direct AI or data science skills or knowledge. The goal of Foundry Tools is to help developers create applications that can see, hear, speak, understand, and even begin to reason.

Fabric provides two options to use Foundry Tools:

  • Pre-built AI models in Fabric (preview)

    Fabric seamlessly integrates with Foundry Tools, allowing you to enrich your data with prebuilt AI models without any prerequisite. We recommend this option because you can use your Fabric authentication to access Foundry Tools, and all usages are billed against your Fabric capacity. This option is currently in public preview, with limited Microsoft Foundry tools available.

    Fabric offers Azure OpenAI Service, Text Analytics, and Azure Translator in Foundry Tools by default, with support for both SynapseML and the RESTful API. You can also use the OpenAI Python Library to access Azure OpenAI service in Fabric. For more information about available models, visit prebuilt AI models in Fabric.

  • Bring your own key (BYOK)

    You can provision your Foundry tools on Azure, and bring your own key to use them from Fabric. If the prebuilt AI models don't yet support the desired Foundry tools, you can still use BYOK (Bring your own key).

    To learn more about how to use Foundry Tools with BYOK, visit Foundry Tools in SynapseML with bring your own key.

Prebuilt AI models in Fabric (preview)

Azure OpenAI Service

REST API, Python SDK, SynapseML, AI Functions

Text Analytics

REST API, SynapseML

  • Language detection: detects language of the input text
  • Sentiment analysis: returns a score between 0 and 1, to indicate the sentiment in the input text
  • Key phrase extraction: identifies the key talking points in the input text
  • Personally Identifiable Information(PII) entity recognition: identify, categorize, and redact sensitive information in the input text
  • Named entity recognition: identifies known entities and general named entities in the input text
  • Entity linking: identifies and disambiguates the identity of entities found in text

Translator

REST API, SynapseML

  • Translate: Translates text
  • Transliterate: Converts text in one language, in one script, to another script.

Available regions

Available regions for Azure OpenAI Service

For the list of Azure regions where prebuilt Foundry Tools in Fabric are now available, visit the Available regions section of the Overview of Copilot in Fabric and Power BI (preview) article.

Available regions for Text Analytics and Translator

Prebuilt Text Analytics and the Translator in Fabric are now available for public preview in the Azure regions listed in this article. If you don't find your Microsoft Fabric home region in this article, you can still create a Microsoft Fabric capacity in a supported region. For more information, visit Buy a Microsoft Fabric subscription. To determine your Fabric home region, visit Find your Fabric home region.

Asia Pacific Europe Americas Middle East and Africa
Australia East North Europe Brazil South South Africa North
Australia Southeast West Europe Canada Central UAE North
Central Indian France Central Canada East
East Asia Norway East East US
Japan East Switzerland North East US 2
Korea Central Switzerland West North Central US
Southeast Asia UK South South Central US
South India UK West West US
West US 2
West US 3

Consumption rate

Consumption rate for OpenAI language models

Model Deployment Name Context Window (Tokens) Input (Per 1,000 Tokens) Cached Input (Per 1,000 Tokens) Output (Per 1,000 Tokens) Retirement Date
gpt-5-2025-08-07 gpt-5 400,000
Max output: 128,000		 42.02 CU seconds 4.20 CU seconds 336.13 CU seconds TBD
gpt-4.1-2025-04-14 gpt-4.1 128,000
Max output: 32,768		 67.23 CU seconds 16.81 CU seconds 268.91 CU seconds TBD
gpt-4.1-mini-2025-04-14 gpt-4.1-mini 128,000
Max output: 32,768		 13.45 CU seconds 3.36 CU seconds 53.78 CU seconds TBD

Consumption rate for OpenAI embedding models

Models Deployment Name Context (Tokens) Input (Per 1,000 Tokens)
Ada text-embedding-ada-002 8192 3.36 CU seconds

Consumption rate for Text Analytics

Operation Operation Unit of Measure Consumption rate
Language Detection 1,000 text records 33,613.45 CU seconds
Sentiment Analysis 1,000 text records 33,613.45 CU seconds
Key Phrase Extraction 1,000 text records 33,613.45 CU seconds
Personally Identifying Information Entity Recognition 1,000 text records 33,613.45 CU seconds
Named Entity Recognition 1,000 text records 33,613.45 CU seconds
Entity Linking 1,000 text records 33,613.45 CU seconds
Summarization 1,000 text records 67,226.89 CU seconds

Consumption rate for Text Translator

Operation Operation Unit of Measure Consumption rate
Translate 1M Characters 336,134.45 CU seconds
Transliterate 1M Characters 336,134.45 CU seconds

Changes to Foundry Tools in Fabric consumption rate

Consumption rates are subject to change at any time. Microsoft uses reasonable efforts to provide notice via email or through in-product notification. Changes shall be effective on the date stated in the Microsoft Release Notes or the Microsoft Fabric Blog. If any change to an AI service in Fabric Consumption Rate materially increases the Capacity Units (CU) required to use, customers can use the cancellation options available for the chosen payment method.

Monitor the Usage

Prebuilt AI services in Fabric are billed against the Copilot and AI billing meter on your Fabric capacity. For current consumption rates, see Consumption rate earlier in this article.

You can monitor AI services usage using the Microsoft Fabric Capacity Metrics app. To view AI-related usage:

  1. Install the Microsoft Fabric Capacity Metrics app (requires capacity admin permissions for initial setup).
  2. In the app, look for usage reported under the Copilot and AI meter to see capacity consumption from AI Services and AI Functions operations.

Note

Starting March 17, 2026, the Capacity Metrics app shows AI Functions and AI Services as separate operations. This is a reporting-only change; underlying consumption rates are unchanged.

Prebuilt AI services and AI functions usage is reported under the Copilot and AI meter. This is separate from the Spark compute used to run your notebook or Spark job, which continues to be reported under the Spark billing meter. For more information on Spark compute usage, see Spark compute usage reporting.

Example

A data analyst uses Fabric AI functions in a Fabric PySpark notebook to leverage LLM to classify millions of customer reviews into product categories. The notebook runs on Spark compute and calls ai.classify for each row.

  • The CU consumption for running the notebook (cluster time, data processing) is reported under the Spark billing meter.
  • The CU consumption for the AI function calls (token usage for classification) is reported as AI Functions under the Copilot and AI billing meter.

This separation makes it easier to track and forecast AI-related costs independently from your compute costs.

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