For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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Guides and conceptsAPI ReferenceRelease NotesLLMUCookbooks
Guides and conceptsAPI ReferenceRelease NotesLLMUCookbooks
  • Get Started
    • Introduction
    • Installation
    • Creating a client
    • Playground
    • FAQs
  • Models
    • An Overview of Cohere's Models
    • Aya
    • Embed
    • Rerank
  • Text Generation
    • Introduction to Text Generation at Cohere
    • Using the Chat API
    • Reasoning
    • Image Inputs
    • Streaming Responses
    • Predictable Outputs
    • Advanced Generation Parameters
    • Tool Use
    • Tokens and Tokenizers
    • Summarizing Text
    • Safety Modes
  • Embeddings (Vectors, Search, Retrieval)
    • Introduction to Embeddings at Cohere
    • Semantic Search with Embeddings
    • Multimodal Embeddings
    • Batch Embedding Jobs
  • Going to Production
    • API Keys and Rate Limits
    • Going Live
    • Deprecations
    • How Does Cohere's Pricing Work?
  • Integrations
    • Integrating Embedding Models with Other Tools
    • Cohere and LangChain
    • LlamaIndex and Cohere
  • Deployment Options
    • Overview
    • SDK Compatibility
  • Tutorials
    • Cookbooks
    • LLM University
    • Build Things with Cohere!
    • Agentic RAG
    • Cohere on Azure
      • Text Generation
      • Semantic Search
      • Reranking
      • Retrieval Augmented Generation (RAG)
      • Tool Use & Agents
  • Responsible Use
    • Security
    • Usage Policy
    • Command A Technical Report
    • Command R and Command R+ Model Card
  • Cohere Labs
    • Cohere Labs Acceptable Use Policy
  • More Resources
    • Cohere Toolkit
    • Datasets
    • Improve Cohere Docs
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On this page
  • What is Azure AI Foundry
  • Azure AI Foundry Features
  • Cohere Models on Azure AI Foundry
  • Pricing Mechanisms
  • Deploying Cohere’s Models on Azure AI Foundry.
  • Conclusion
Tutorials

Introduction to Cohere on Azure AI Foundry

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What is Azure AI Foundry

Azure AI Foundry is a trusted platform that empowers developers to build and deploy innovative, responsible AI applications. It offers an enterprise-grade environment with cutting-edge tools and models, ensuring a safe and secure development process.

The platform facilitates collaboration, allowing teams to work together on the full lifecycle of application development. With Azure AI Foundry, developers can explore a wide range of models, services, and capabilities to build AI applications that meet their specific goals.

Hubs are the primary top-level Azure resource for AI Foundry. They provide a central way for a team to govern security, connectivity, and computing resources across playgrounds and projects. Once a hub is created, developers can create projects from it and access shared company resources without needing an IT administrator’s repeated help.

Your new project will be added under your current hub, which provides security, governance controls, and shared configurations that all projects can use. Project workspaces that are created using a hub inherit the same security settings and shared resource access. Teams can create project workspaces as needed to organize their work, isolate data, and/or restrict access.

Azure AI Foundry Features

  • Build generative AI applications on an enterprise-grade platform.
  • Explore, build, test, and deploy using cutting-edge AI tools and ML models, grounded in responsible AI practices.
  • Collaborate with a team for the full life-cycle of application development.
  • Improve your application’s performance using tools like tracing to debug your application or compare evaluations to hone in on how you want your application to behave.
  • Safegaurd every layer with trustworthy AI from the start and protect against any risks.

With AI Foundry, you can explore a wide variety of models, services and capabilities, and get to building AI applications that best serve your goals.

Cohere Models on Azure AI Foundry

To get the most updated list of available models, visit the Azure AI Foundry documentation here.

Pricing Mechanisms

Cohere models can be deployed to serverless API endpoints with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need.

To get the most updated list of available models, visit the Azure marketplace here.

Deploying Cohere’s Models on Azure AI Foundry.

To deploy Cohere’s models on Azure AI Foundry, follow the steps described in Azure AI Foundry documentation here.

In summary, you will need to:

  1. Set up AI Foundry Hub and a project
  2. Find your model and model ID in the model catalog
  3. Subscribe your project to the model offering
  4. Deploy the model to a serverless API endpoint

Models that are offered by Cohere are billed through the Azure Marketplace. For such models, you’re required to subscribe your project to the particular model offering.

Conclusion

This page introduces Azure AI Foundry, a fully managed service by Azure that you can deploy Cohere’s models on. We also went through the steps to get set up with Azure AI Foundry and deploy a Cohere model.

In the next sections, we will go through the various use cases of using Cohere’s Command, Embed, and Rerank models on Azure AI Foundry.