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
      • Cohere on AWS
        • Amazon Bedrock
        • Amazon SageMaker
      • Cohere on Azure
      • Cohere on Oracle Cloud Infrastructure (OCI)
  • Tutorials
    • Cookbooks
    • LLM University
    • Build Things with Cohere!
    • Agentic RAG
    • Cohere on Azure
  • 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
  • Amazon SageMaker
  • Amazon Bedrock
  • Python SDK
  • Pricing
Deployment OptionsCloud AI Services

Cohere on Amazon Web Services (AWS)

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Cohere Models on Amazon Bedrock

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Built with

Developers can access a range of Cohere language models in a private environment via Amazon’s AWS Cloud platform. Cohere’s models are supported on two Amazon services: Amazon SageMaker and Amazon Bedrock.

Amazon SageMaker

Amazon SageMaker is a service that allows customers to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. Read about SageMaker here.

Cohere offers a comprehensive suite of generative and embedding models through SageMaker on a range of hardware options, many of which support finetuning for deeper customization and performance.

View Cohere’s products on Amazon SageMaker.

Amazon Bedrock

Amazon Bedrock is a fully managed service where foundational models from Cohere and other LLM providers are made available through a single, serverless API. Read about Amazon Bedrock here.

Cohere has three flagship offerings available on-demand through Amazon Bedrock: Command, the Embed v3 family of models, and Rerank v3.5. Finetuning is also supported for the Command and Command-Light models. Cohere will continue to add products and services to Amazon Bedrock in the coming months.

View Cohere’s products on Amazon Bedrock

Python SDK

The Cohere Python SDK supports both Amazon SageMaker and Amazon Bedrock. The SDK provides a simple and consistent interface for interacting with Cohere models across platforms.

cohere-aws SDK on Github

Pricing

The latest pricing for Cohere models can all be viewed directly from from the listing pages on our Amazon Bedrock and Amazon SageMaker marketplaces. If you have any questions about pricing or deployment options, please contact our sales team.