# Amazon Bedrock Provider

The Amazon Bedrock provider for the [AI SDK](/docs) contains language model support for the [Amazon Bedrock](https://aws.amazon.com/bedrock) APIs.

## Setup

The Bedrock provider is available in the `@ai-sdk/amazon-bedrock` module. You can install it with

<Tabs items={['pnpm', 'npm', 'yarn']}>
  <Tab>
    <Snippet text="pnpm add @ai-sdk/amazon-bedrock" dark />
  </Tab>
  <Tab>
    <Snippet text="npm install @ai-sdk/amazon-bedrock" dark />
  </Tab>
  <Tab>
    <Snippet text="yarn add @ai-sdk/amazon-bedrock" dark />
  </Tab>
</Tabs>

### Prerequisites

Access to Amazon Bedrock foundation models isn't granted by default. In order to gain access to a foundation model, an IAM user with sufficient permissions needs to request access to it through the console. Once access is provided to a model, it is available for all users in the account.

See the [Model Access Docs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html) for more information.

### Authentication

#### Using IAM Access Key and Secret Key

**Step 1: Creating AWS Access Key and Secret Key**

To get started, you'll need to create an AWS access key and secret key. Here's how:

**Login to AWS Management Console**

- Go to the [AWS Management Console](https://console.aws.amazon.com/) and log in with your AWS account credentials.

**Create an IAM User**

- Navigate to the [IAM dashboard](https://console.aws.amazon.com/iam/home) and click on "Users" in the left-hand navigation menu.
- Click on "Create user" and fill in the required details to create a new IAM user.
- Make sure to select "Programmatic access" as the access type.
- The user account needs the `AmazonBedrockFullAccess` policy attached to it.

**Create Access Key**

- Click on the "Security credentials" tab and then click on "Create access key".
- Click "Create access key" to generate a new access key pair.
- Download the `.csv` file containing the access key ID and secret access key.

**Step 2: Configuring the Access Key and Secret Key**

Within your project add a `.env` file if you don't already have one. This file will be used to set the access key and secret key as environment variables. Add the following lines to the `.env` file:

```makefile
AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY=YOUR_SECRET_ACCESS_KEY
AWS_REGION=YOUR_REGION
```

<Note>
  Many frameworks such as [Next.js](https://nextjs.org/) load the `.env` file
  automatically. If you're using a different framework, you may need to load the
  `.env` file manually using a package like
  [`dotenv`](https://github.com/motdotla/dotenv).
</Note>

Remember to replace `YOUR_ACCESS_KEY_ID`, `YOUR_SECRET_ACCESS_KEY`, and `YOUR_REGION` with the actual values from your AWS account.

#### Using AWS SDK Credentials Chain (instance profiles, instance roles, ECS roles, EKS Service Accounts, etc.)

When using AWS SDK, the SDK will automatically use the credentials chain to determine the credentials to use. This includes instance profiles, instance roles, ECS roles, EKS Service Accounts, etc. A similar behavior is possible using the AI SDK by not specifying the `accessKeyId` and `secretAccessKey`, `sessionToken` properties in the provider settings and instead passing a `credentialProvider` property.

_Usage:_

`@aws-sdk/credential-providers` package provides a set of credential providers that can be used to create a credential provider chain.

<Tabs items={['pnpm', 'npm', 'yarn']}>
  <Tab>
    <Snippet text="pnpm add @aws-sdk/credential-providers" dark />
  </Tab>
  <Tab>
    <Snippet text="npm install @aws-sdk/credential-providers" dark />
  </Tab>
  <Tab>
    <Snippet text="yarn add @aws-sdk/credential-providers" dark />
  </Tab>
</Tabs>

```ts
import { createAmazonBedrock } from '@ai-sdk/amazon-bedrock';
import { fromNodeProviderChain } from '@aws-sdk/credential-providers';

const bedrock = createAmazonBedrock({
  region: 'us-east-1',
  credentialProvider: fromNodeProviderChain(),
});
```

## Provider Instance

You can import the default provider instance `bedrock` from `@ai-sdk/amazon-bedrock`:

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
```

If you need a customized setup, you can import `createAmazonBedrock` from `@ai-sdk/amazon-bedrock` and create a provider instance with your settings:

```ts
import { createAmazonBedrock } from '@ai-sdk/amazon-bedrock';

const bedrock = createAmazonBedrock({
  region: 'us-east-1',
  accessKeyId: 'xxxxxxxxx',
  secretAccessKey: 'xxxxxxxxx',
  sessionToken: 'xxxxxxxxx',
});
```

<Note>
  The credentials settings fall back to environment variable defaults described
  below. These may be set by your serverless environment without your awareness,
  which can lead to merged/conflicting credential values and provider errors
  around failed authentication. If you're experiencing issues be sure you are
  explicitly specifying all settings (even if `undefined`) to avoid any
  defaults.
</Note>

You can use the following optional settings to customize the Amazon Bedrock provider instance:

- **region** _string_

  The AWS region that you want to use for the API calls.
  It uses the `AWS_REGION` environment variable by default.

- **accessKeyId** _string_

  The AWS access key ID that you want to use for the API calls.
  It uses the `AWS_ACCESS_KEY_ID` environment variable by default.

- **secretAccessKey** _string_

  The AWS secret access key that you want to use for the API calls.
  It uses the `AWS_SECRET_ACCESS_KEY` environment variable by default.

- **sessionToken** _string_

  Optional. The AWS session token that you want to use for the API calls.
  It uses the `AWS_SESSION_TOKEN` environment variable by default.

- **credentialProvider** _() =&gt; Promise&lt;&#123; accessKeyId: string; secretAccessKey: string; sessionToken?: string; &#125;&gt;_

  Optional. The AWS credential provider chain that you want to use for the API calls.
  It uses the specified credentials by default.

## Language Models

You can create models that call the Bedrock API using the provider instance.
The first argument is the model id, e.g. `meta.llama3-70b-instruct-v1:0`.

```ts
const model = bedrock('meta.llama3-70b-instruct-v1:0');
```

Amazon Bedrock models also support some model specific settings that are not part of the [standard call settings](/docs/ai-sdk-core/settings).
You can pass them as an options argument:

```ts
const model = bedrock('anthropic.claude-3-sonnet-20240229-v1:0', {
  additionalModelRequestFields: { top_k: 350 },
});
```

Documentation for additional settings based on the selected model can be found within the [Amazon Bedrock Inference Parameter Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html).

You can use Amazon Bedrock language models to generate text with the `generateText` function:

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { generateText } from 'ai';

const { text } = await generateText({
  model: bedrock('meta.llama3-70b-instruct-v1:0'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});
```

Amazon Bedrock language models can also be used in the `streamText` function
(see [AI SDK Core](/docs/ai-sdk-core)).

### File Inputs

<Note type="warning">
  Amazon Bedrock supports file inputs on in combination with specific models,
  e.g. `anthropic.claude-3-haiku-20240307-v1:0`.
</Note>

The Amazon Bedrock provider supports file inputs, e.g. PDF files.

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { generateText } from 'ai';

const result = await generateText({
  model: bedrock('anthropic.claude-3-haiku-20240307-v1:0'),
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'Describe the pdf in detail.' },
        {
          type: 'file',
          data: fs.readFileSync('./data/ai.pdf'),
          mimeType: 'application/pdf',
        },
      ],
    },
  ],
});
```

### Guardrails

You can use the `bedrock` provider options to utilize [Amazon Bedrock Guardrails](https://aws.amazon.com/bedrock/guardrails/):

```ts
const result = await generateText({
  bedrock('anthropic.claude-3-sonnet-20240229-v1:0'),
  providerOptions: {
    bedrock: {
      guardrailConfig: {
        guardrailIdentifier: '1abcd2ef34gh',
        guardrailVersion: '1',
        trace: 'enabled' as const,
        streamProcessingMode: 'async',
      },
    },
  },
});
```

Tracing information will be returned in the provider metadata if you have tracing enabled.

```ts
if (result.providerMetadata?.bedrock.trace) {
  // ...
}
```

See the [Amazon Bedrock Guardrails documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html) for more information.

### Cache Points

<Note>
  Amazon Bedrock prompt caching is currently in preview release. To request
  access, visit the [Amazon Bedrock prompt caching
  page](https://aws.amazon.com/bedrock/prompt-caching/).
</Note>

In messages, you can use the `providerOptions` property to set cache points. Set the `bedrock` property in the `providerOptions` object to `{ cachePoint: { type: 'default' } }` to create a cache point.

Cache usage information is returned in the `providerMetadata` object`. See examples below.

<Note>
  Cache points have model-specific token minimums and limits. For example,
  Claude 3.5 Sonnet v2 requires at least 1,024 tokens for a cache point and
  allows up to 4 cache points. See the [Amazon Bedrock prompt caching
  documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html)
  for details on supported models, regions, and limits.
</Note>

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { generateText } from 'ai';

const cyberpunkAnalysis =
  '... literary analysis of cyberpunk themes and concepts ...';

const result = await generateText({
  model: bedrock('anthropic.claude-3-5-sonnet-20241022-v2:0'),
  messages: [
    {
      role: 'system',
      content: `You are an expert on William Gibson's cyberpunk literature and themes. You have access to the following academic analysis: ${cyberpunkAnalysis}`,
      providerOptions: {
        bedrock: { cachePoint: { type: 'default' } },
      },
    },
    {
      role: 'user',
      content:
        'What are the key cyberpunk themes that Gibson explores in Neuromancer?',
    },
  ],
});

console.log(result.text);
console.log(result.providerMetadata?.bedrock?.usage);
// Shows cache read/write token usage, e.g.:
// {
//   cacheReadInputTokens: 1337,
//   cacheWriteInputTokens: 42,
// }
```

Cache points also work with streaming responses:

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { streamText } from 'ai';

const cyberpunkAnalysis =
  '... literary analysis of cyberpunk themes and concepts ...';

const result = streamText({
  model: bedrock('anthropic.claude-3-5-sonnet-20241022-v2:0'),
  messages: [
    {
      role: 'assistant',
      content: [
        { type: 'text', text: 'You are an expert on cyberpunk literature.' },
        { type: 'text', text: `Academic analysis: ${cyberpunkAnalysis}` },
      ],
      providerOptions: { bedrock: { cachePoint: { type: 'default' } } },
    },
    {
      role: 'user',
      content:
        'How does Gibson explore the relationship between humanity and technology?',
    },
  ],
});

for await (const textPart of result.textStream) {
  process.stdout.write(textPart);
}

console.log(
  'Cache token usage:',
  (await result.providerMetadata)?.bedrock?.usage,
);
// Shows cache read/write token usage, e.g.:
// {
//   cacheReadInputTokens: 1337,
//   cacheWriteInputTokens: 42,
// }
```

## Reasoning

Amazon Bedrock has reasoning support for the `claude-3-7-sonnet-20250219` model.

You can enable it using the `reasoningConfig` provider option and specifying a thinking budget in tokens (minimum: `1024`, maximum: `64000`).

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { generateText } from 'ai';

const { text, reasoning, reasoningDetails } = await generateText({
  model: bedrock('us.anthropic.claude-3-7-sonnet-20250219-v1:0'),
  prompt: 'How many people will live in the world in 2040?',
  providerOptions: {
    bedrock: {
      reasoningConfig: { type: 'enabled', budgetTokens: 1024 },
    },
  },
});

console.log(reasoning); // reasoning text
console.log(reasoningDetails); // reasoning details including redacted reasoning
console.log(text); // text response
```

See [AI SDK UI: Chatbot](/docs/ai-sdk-ui/chatbot#reasoning) for more details
on how to integrate reasoning into your chatbot.

### Model Capabilities

| Model                                       | Image Input         | Object Generation   | Tool Usage          | Tool Streaming      |
| ------------------------------------------- | ------------------- | ------------------- | ------------------- | ------------------- |
| `amazon.titan-tg1-large`                    | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `amazon.titan-text-express-v1`              | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `amazon.nova-micro-v1:0`                    | <Cross size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `amazon.nova-lite-v1:0`                     | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `amazon.nova-pro-v1:0`                      | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-4-sonnet-20250514-v1:0`   | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-4-opus-20250514-v1:0`     | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-3-7-sonnet-20250219-v1:0` | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-3-5-sonnet-20241022-v2:0` | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-3-5-sonnet-20240620-v1:0` | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-3-5-haiku-20241022-v1:0`  | <Cross size={18} /> | <Check size={18} /> | <Check size={18} /> | <Cross size={18} /> |
| `anthropic.claude-3-opus-20240229-v1:0`     | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-3-sonnet-20240229-v1:0`   | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-3-haiku-20240307-v1:0`    | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `anthropic.claude-v2:1`                     | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `cohere.command-r-v1:0`                     | <Cross size={18} /> | <Cross size={18} /> | <Check size={18} /> | <Cross size={18} /> |
| `cohere.command-r-plus-v1:0`                | <Cross size={18} /> | <Cross size={18} /> | <Check size={18} /> | <Cross size={18} /> |
| `deepseek.r1-v1:0`                          | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama2-13b-chat-v1`                   | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama2-70b-chat-v1`                   | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-8b-instruct-v1:0`              | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-70b-instruct-v1:0`             | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-1-8b-instruct-v1:0`            | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-1-70b-instruct-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-1-405b-instruct-v1:0`          | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-2-1b-instruct-v1:0`            | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-2-3b-instruct-v1:0`            | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-2-11b-instruct-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta.llama3-2-90b-instruct-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `mistral.mistral-7b-instruct-v0:2`          | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `mistral.mixtral-8x7b-instruct-v0:1`        | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `mistral.mistral-large-2402-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `mistral.mistral-small-2402-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |

<Note>
  The table above lists popular models. Please see the [Amazon Bedrock
  docs](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html)
  for a full list of available models. The table above lists popular models. You
  can also pass any available provider model ID as a string if needed.
</Note>

## Embedding Models

You can create models that call the Bedrock API [Bedrock API](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html)
using the `.embedding()` factory method.

```ts
const model = bedrock.embedding('amazon.titan-embed-text-v1');
```

Bedrock Titan embedding model amazon.titan-embed-text-v2:0 supports several additional settings.
You can pass them as an options argument:

```ts
const model = bedrock.embedding('amazon.titan-embed-text-v2:0', {
  dimensions: 512 // optional, number of dimensions for the embedding
  normalize: true // optional  normalize the output embeddings
})
```

The following optional settings are available for Bedrock Titan embedding models:

- **dimensions**: _number_

  The number of dimensions the output embeddings should have. The following values are accepted: 1024 (default), 512, 256.

- **normalize** _boolean_

  Flag indicating whether or not to normalize the output embeddings. Defaults to true.

### Model Capabilities

| Model                          | Default Dimensions | Custom Dimensions   |
| ------------------------------ | ------------------ | ------------------- |
| `amazon.titan-embed-text-v1`   | 1536               | <Cross size={18} /> |
| `amazon.titan-embed-text-v2:0` | 1024               | <Check size={18} /> |

## Image Models

You can create models that call the Bedrock API [Bedrock API](https://docs.aws.amazon.com/nova/latest/userguide/image-generation.html)
using the `.image()` factory method.

For more on the Amazon Nova Canvas image model, see the [Nova Canvas
Overview](https://docs.aws.amazon.com/ai/responsible-ai/nova-canvas/overview.html).

<Note>
  The `amazon.nova-canvas-v1:0` model is available in the `us-east-1` region.
</Note>

```ts
const model = bedrock.image('amazon.nova-canvas-v1:0');
```

You can then generate images with the `experimental_generateImage` function:

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { experimental_generateImage as generateImage } from 'ai';

const { image } = await generateImage({
  model: bedrock.imageModel('amazon.nova-canvas-v1:0'),
  prompt: 'A beautiful sunset over a calm ocean',
  size: '512x512',
  seed: 42,
});
```

You can also pass the `providerOptions` object to the `generateImage` function to customize the generation behavior:

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { experimental_generateImage as generateImage } from 'ai';

const { image } = await generateImage({
  model: bedrock.imageModel('amazon.nova-canvas-v1:0'),
  prompt: 'A beautiful sunset over a calm ocean',
  size: '512x512',
  seed: 42,
  providerOptions: { bedrock: { quality: 'premium' } },
});
```

Documentation for additional settings can be found within the [Amazon Bedrock
User Guide for Amazon Nova
Documentation](https://docs.aws.amazon.com/nova/latest/userguide/image-gen-req-resp-structure.html).

### Image Model Settings

When creating an image model, you can customize the generation behavior with optional settings:

```ts
const model = bedrock.imageModel('amazon.nova-canvas-v1:0', {
  maxImagesPerCall: 1, // Maximum number of images to generate per API call
});
```

- **maxImagesPerCall** _number_

  Override the maximum number of images generated per API call. Default can vary
  by model, with 5 as a common default.

### Model Capabilities

The Amazon Nova Canvas model supports custom sizes with constraints as follows:

- Each side must be between 320-4096 pixels, inclusive.
- Each side must be evenly divisible by 16.
- The aspect ratio must be between 1:4 and 4:1. That is, one side can't be more than 4 times longer than the other side.
- The total pixel count must be less than 4,194,304.

For more, see [Image generation access and
usage](https://docs.aws.amazon.com/nova/latest/userguide/image-gen-access.html).

| Model                     | Sizes                                                                                                 |
| ------------------------- | ----------------------------------------------------------------------------------------------------- |
| `amazon.nova-canvas-v1:0` | Custom sizes: 320-4096px per side (must be divisible by 16), aspect ratio 1:4 to 4:1, max 4.2M pixels |

## Response Headers

The Amazon Bedrock provider will return the response headers associated with
network requests made of the Bedrock servers.

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { generateText } from 'ai';

const { text } = await generateText({
  model: bedrock('meta.llama3-70b-instruct-v1:0'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

console.log(result.response.headers);
```

Below is sample output where you can see the `x-amzn-requestid` header. This can
be useful for correlating Bedrock API calls with requests made by the AI SDK:

```js highlight="6"
{
  connection: 'keep-alive',
  'content-length': '2399',
  'content-type': 'application/json',
  date: 'Fri, 07 Feb 2025 04:28:30 GMT',
  'x-amzn-requestid': 'c9f3ace4-dd5d-49e5-9807-39aedfa47c8e'
}
```

This information is also available with `streamText`:

```ts
import { bedrock } from '@ai-sdk/amazon-bedrock';
import { streamText } from 'ai';

const result = streamText({
  model: bedrock('meta.llama3-70b-instruct-v1:0'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});
for await (const textPart of result.textStream) {
  process.stdout.write(textPart);
}
console.log('Response headers:', (await result.response).headers);
```

With sample output as:

```js highlight="6"
{
  connection: 'keep-alive',
  'content-type': 'application/vnd.amazon.eventstream',
  date: 'Fri, 07 Feb 2025 04:33:37 GMT',
  'transfer-encoding': 'chunked',
  'x-amzn-requestid': 'a976e3fc-0e45-4241-9954-b9bdd80ab407'
}
```

## Migrating to `@ai-sdk/amazon-bedrock` 2.x

The Amazon Bedrock provider was rewritten in version 2.x to remove the
dependency on the `@aws-sdk/client-bedrock-runtime` package.

The `bedrockOptions` provider setting previously available has been removed. If
you were using the `bedrockOptions` object, you should now use the `region`,
`accessKeyId`, `secretAccessKey`, and `sessionToken` settings directly instead.

Note that you may need to set all of these explicitly, e.g. even if you're not
using `sessionToken`, set it to `undefined`. If you're running in a serverless
environment, there may be default environment variables set by your containing
environment that the Amazon Bedrock provider will then pick up and could
conflict with the ones you're intending to use.


## Navigation

- [xAI Grok](/v4/providers/ai-sdk-providers/xai)
- [Vercel](/v4/providers/ai-sdk-providers/vercel)
- [OpenAI](/v4/providers/ai-sdk-providers/openai)
- [Azure OpenAI](/v4/providers/ai-sdk-providers/azure)
- [Anthropic](/v4/providers/ai-sdk-providers/anthropic)
- [Amazon Bedrock](/v4/providers/ai-sdk-providers/amazon-bedrock)
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- [Deepgram](/v4/providers/ai-sdk-providers/deepgram)
- [Gladia](/v4/providers/ai-sdk-providers/gladia)
- [LMNT](/v4/providers/ai-sdk-providers/lmnt)
- [Google Generative AI](/v4/providers/ai-sdk-providers/google-generative-ai)
- [Hume](/v4/providers/ai-sdk-providers/hume)
- [Google Vertex AI](/v4/providers/ai-sdk-providers/google-vertex)
- [Rev.ai](/v4/providers/ai-sdk-providers/revai)
- [Mistral AI](/v4/providers/ai-sdk-providers/mistral)
- [Together.ai](/v4/providers/ai-sdk-providers/togetherai)
- [Cohere](/v4/providers/ai-sdk-providers/cohere)
- [Fireworks](/v4/providers/ai-sdk-providers/fireworks)
- [DeepSeek](/v4/providers/ai-sdk-providers/deepseek)
- [Cerebras](/v4/providers/ai-sdk-providers/cerebras)
- [Replicate](/v4/providers/ai-sdk-providers/replicate)
- [Perplexity](/v4/providers/ai-sdk-providers/perplexity)
- [Luma](/v4/providers/ai-sdk-providers/luma)
- [ElevenLabs](/v4/providers/ai-sdk-providers/elevenlabs)


[Full Sitemap](/sitemap.md)