# AWS + Weaviate

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AWS offers a wide range of models for natural language processing and generation. Weaviate seamlessly integrates with AWS's APIs, allowing users to leverage AWS's models directly from the Weaviate Database.

Weaviate integrates with both AWS [Sagemaker](https://aws.amazon.com/sagemaker/) and [Bedrock](https://aws.amazon.com/bedrock/).

These integrations empower developers to build sophisticated AI-driven applications with ease.

:::callout{intent="tip" title="Sagemaker vs Bedrock"}
Amazon SageMaker is a fully managed service where you can build, train and deploy ML models. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies.
:::

## Integrations with AWS

### Embedding models for vector search

![Embedding integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_aws_embedding.png)

AWS's embedding models transform text data into vector embeddings, capturing meaning and context.

[Weaviate integrates with AWS's embedding models](aws-embeddings.md) to enable seamless vectorization of data. This integration allows users to perform semantic and hybrid search operations without the need for additional preprocessing or data transformation steps.

[AWS embedding integration page](aws-embeddings.md)

### Generative AI models for RAG

![Single prompt RAG integration generates individual outputs per search result](/assets/docs/weaviate/model-providers/_includes/integration_aws_rag_single.png)

AWS's generative AI models can generate human-like text based on given prompts and contexts.

[Weaviate's generative AI integration](aws-generative.md) enables users to perform retrieval augmented generation (RAG) directly from the Weaviate Database. This combines Weaviate's efficient storage and fast retrieval capabilities with AWS's generative AI models to generate personalized and context-aware responses.

[AWS generative AI integration page](aws-generative.md)

## Summary

These integrations enable developers to leverage AWS's powerful models directly within Weaviate.

In turn, they simplify the process of building AI-driven applications to speed up your development process, so that you can focus on creating innovative solutions.

## Get started

You must provide [access key based AWS credentials](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html) to Weaviate for these integrations. Go to [AWS](https://aws.amazon.com/) to sign up and obtain an AWS access key and a corresponding AWS secret access key.

Then, go to the relevant integration page to learn how to configure Weaviate with the AWS models and start using them in your applications.

- [Text Embeddings](aws-embeddings.md)
- [Generative AI](aws-generative.md)

## Questions and feedback

Have a question or feedback? Here's how to reach us.

::::card-grid
:::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"}
Ask questions and connect with other developers on our **Community forum**.
:::

:::card{title="Support" href="/guides/support-overview" icon="life-buoy"}
Weaviate Cloud user or customer? Find the right channel on the **Support page**.
:::
::::

## Related pages

- [Agents](./agents-index.md)
- [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md)
- [APIs](./apis-index.md)
- [Authorization and authentication](./authorization-and-authentication-index.md)
- [Benchmarks](./benchmarks-index.md)
- [Best practices](./best-practices-index.md)
- [Client libraries](./clients-index.md)
- [Client Libraries / SDKs](./client-libraries-index.md)
- [Cloud](./cloud-index.md)
- [Cloud account management](./cloud-account-management-index.md)

# Agent Instructions

This portal answers questions programmatically. To receive a synthesized,
source-cited answer instead of crawling page by page, append the `?ask=`
query parameter to any page URL on this site:

    /guides/quickstart?ask=how+do+I+authenticate

Optional parameters:

- `&goal=<what-you-are-trying-to-do>` steers the answer toward your
  objective (e.g. `&goal=write+a+python+client`).
- `&version=<label>` scopes the answer to a mounted version when the
  portal publishes more than one.

The response is `text/markdown`: the answer followed by a `# Sources` list
of the portal pages it was grounded in. Status codes are the contract:

- `200` — the answer; `402` — the portal owner’s plan or answer credits are
  exhausted (surface this to your operator; do NOT retry); `429` — you are
  rate-limited; back off for the `Retry-After` seconds; `503` — the answer
  lane is temporarily unavailable; fall back to crawling the `.md` pages.

For the full corpus map read `llms.txt` at the site root; for the tool
surface (search + page fetch as MCP tools) see `/mcp`.