--- name: bedrock description: AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns. last_updated: "2026-01-07" doc_source: https://docs.aws.amazon.com/bedrock/latest/userguide/ ---

# AWS Bedrock

Amazon Bedrock provides access to foundation models (FMs) from AI companies through a unified API. Build generative AI applications with text generation, embeddings, and image generation capabilities.

## Table of Contents

- [Core Concepts](#core-concepts) - [Common Patterns](#common-patterns) - [CLI Reference](#cli-reference) - [Best Practices](#best-practices) - [Troubleshooting](#troubleshooting) - [References](#references)

## Core Concepts

### Foundation Models

Pre-trained models available through Bedrock: - **Claude** (Anthropic): Text generation, analysis, coding - **Titan** (Amazon): Text, embeddings, image generation - **Llama** (Meta): Open-weight text generation - **Mistral**: Efficient text generation - **Stable Diffusion** (Stability AI): Image generation

### Model Access

Models must be enabled in your account before use: - Request access in Bedrock console - Some models require acceptance of EULAs - Access is region-specific

### Inference Types

| Type | Use Case | Pricing | |------|----------|---------| | **On-Demand** | Variable workloads | Per token | | **Provisioned Throughput** | Consistent high-volume | Hourly commitment | | **Batch Inference** | Async large-scale | Discounted per token |

## Common Patterns

### Invoke Model (Text Generation)

**AWS CLI:**

```bash # Invoke Claude aws bedrock-runtime invoke-model \ --model-id anthropic.claude-3-sonnet-20240229-v1:0 \ --content-type application/json \ --accept application/json \ --body '{ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain AWS Lambda in 3 sentences."} ] }' \ response.json

cat response.json | jq -r '.content[0].text' ```

**boto3:**

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def invoke_claude(prompt, max_tokens=1024): response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps({ 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': max_tokens, 'messages': [ {'role': 'user', 'content': prompt} ] }) )

result = json.loads(response['body'].read()) return result['content'][0]['text']

# Usage response = invoke_claude('What is Amazon S3?') print(response) ```

### Streaming Response

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def stream_claude(prompt): response = bedrock.invoke_model_with_response_stream( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps({ 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': 1024, 'messages': [ {'role': 'user', 'content': prompt} ] }) )

for event in response['body']: chunk = json.loads(event['chunk']['bytes']) if chunk['type'] == 'content_block_delta': yield chunk['delta'].get('text', '')

# Usage for text in stream_claude('Write a haiku about cloud computing.'): print(text, end='', flush=True) ```

### Generate Embeddings

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def get_embedding(text): response = bedrock.invoke_model( modelId='amazon.titan-embed-text-v2:0', contentType='application/json', accept='application/json', body=json.dumps({ 'inputText': text, 'dimensions': 1024, 'normalize': True }) )

result = json.loads(response['body'].read()) return result['embedding']

# Usage embedding = get_embedding('AWS Lambda is a serverless compute service.') print(f'Embedding dimension: {len(embedding)}') ```

### Conversation with History

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

class Conversation: def __init__(self, system_prompt=None): self.messages = [] self.system = system_prompt

def chat(self, user_message): self.messages.append({ 'role': 'user', 'content': user_message })

body = { 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': 1024, 'messages': self.messages }

if self.system: body['system'] = self.system

response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps(body) )

result = json.loads(response['body'].read()) assistant_message = result['content'][0]['text']

self.messages.append({ 'role': 'assistant', 'content': assistant_message })

return assistant_message

# Usage conv = Conversation(system_prompt='You are an AWS solutions architect.') print(conv.chat('What database should I use for a chat application?')) print(conv.chat('What about for time-series data?')) ```

### List Available Models

```bash # List all foundation models aws bedrock list-foundation-models \ --query 'modelSummaries[*].[modelId,modelName,providerName]' \ --output table

# Filter by provider aws bedrock list-foundation-models \ --by-provider anthropic \ --query 'modelSummaries[*].modelId'

# Get model details aws bedrock get-foundation-model \ --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 ```

### Request Model Access

```bash # List model access status aws bedrock list-foundation-model-agreement-offers \ --model-id anthropic.claude-3-sonnet-20240229-v1:0 ```

## CLI Reference

### Bedrock (Control Plane)

| Command | Description | |---------|-------------| | `aws bedrock list-foundation-models` | List available models | | `aws bedrock get-foundation-model` | Get model details | | `aws bedrock list-custom-models` | List fine-tuned models | | `aws bedrock create-model-customization-job` | Start fine-tuning | | `aws bedrock list-provisioned-model-throughputs` | List provisioned capacity |

### Bedrock Runtime (Data Plane)

| Command | Description | |---------|-------------| | `aws bedrock-runtime invoke-model` | Invoke model synchronously | | `aws bedrock-runtime invoke-model-with-response-stream` | Invoke with streaming | | `aws bedrock-runtime converse` | Multi-turn conversation API | | `aws bedrock-runtime converse-stream` | Streaming conversation |

### Bedrock Agent Runtime

| Command | Description | |---------|-------------| | `aws bedrock-agent-runtime invoke-agent` | Invoke a Bedrock agent | | `aws bedrock-agent-runtime retrieve` | Query knowledge base | | `aws bedrock-agent-runtime retrieve-and-generate` | RAG query |

## Best Practices

### Cost Optimization

- **Use appropriate models**: Smaller models for simple tasks - **Set max_tokens**: Limit output length when possible - **Cache responses**: For repeated identical queries - **Batch when possible**: Use batch inference for bulk processing - **Monitor usage**: Set up CloudWatch alarms for cost

### Performance

- **Use streaming**: For better user experience with long outputs - **Connection pooling**: Reuse boto3 clients - **Regional deployment**: Use closest region to reduce latency - **Provisioned throughput**: For consistent high-volume workloads

### Security

- **Least privilege IAM**: Only grant needed model access - **VPC endpoints**: Keep traffic private - **Guardrails**: Implement content filtering - **Audit with CloudTrail**: Track model invocations

### IAM Permissions

```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream" ], "Resource": [ "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0", "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0" ] } ] } ```

## Troubleshooting

### AccessDeniedException

**Causes:** - Model access not enabled in console - IAM policy missing `bedrock:InvokeModel` - Wrong model ID or region

**Debug:**

```bash # Check model access status aws bedrock list-foundation-models \ --query 'modelSummaries[?modelId==`anthropic.claude-3-sonnet-20240229-v1:0`]'

# Test IAM permissions aws iam simulate-principal-policy \ --policy-source-arn arn:aws:iam::123456789012:role/my-role \ --action-names bedrock:InvokeModel \ --resource-arns "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0" ```

### ModelNotReadyException

**Cause:** Model is still being provisioned or temporarily unavailable.

**Solution:** Implement retry with exponential backoff:

```python import time from botocore.exceptions import ClientError

def invoke_with_retry(bedrock, body, max_retries=3): for attempt in range(max_retries): try: return bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', body=json.dumps(body) ) except ClientError as e: if e.response['Error']['Code'] == 'ModelNotReadyException': time.sleep(2 ** attempt) else: raise raise Exception('Max retries exceeded') ```

### ThrottlingException

**Causes:** - Exceeded on-demand quota - Too many concurrent requests

**Solutions:** - Request quota increase - Implement exponential backoff - Consider provisioned throughput

### ValidationException

**Common issues:** - Invalid model ID - Malformed request body - max_tokens exceeds model limit

**Debug:**

```python # Check model-specific requirements aws bedrock get-foundation-model \ --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 \ --query 'modelDetails.inferenceTypesSupported' ```

## References

- [Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/) - [Bedrock API Reference](https://docs.aws.amazon.com/bedrock/latest/APIReference/) - [Bedrock Runtime API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Amazon_Bedrock_Runtime.html) - [Model Parameters](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) - [Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/)

--- name: bedrock description: AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns. last_updated: "2026-01-07" doc_source: https://docs.aws.amazon.com/bedrock/latest/userguide/ ---

# AWS Bedrock

Amazon Bedrock provides access to foundation models (FMs) from AI companies through a unified API. Build generative AI applications with text generation, embeddings, and image generation capabilities.

## Table of Contents

- [Core Concepts](#core-concepts) - [Common Patterns](#common-patterns) - [CLI Reference](#cli-reference) - [Best Practices](#best-practices) - [Troubleshooting](#troubleshooting) - [References](#references)

## Core Concepts

### Foundation Models

Pre-trained models available through Bedrock: - **Claude** (Anthropic): Text generation, analysis, coding - **Titan** (Amazon): Text, embeddings, image generation - **Llama** (Meta): Open-weight text generation - **Mistral**: Efficient text generation - **Stable Diffusion** (Stability AI): Image generation

### Model Access

Models must be enabled in your account before use: - Request access in Bedrock console - Some models require acceptance of EULAs - Access is region-specific

### Inference Types

| Type | Use Case | Pricing | |------|----------|---------| | **On-Demand** | Variable workloads | Per token | | **Provisioned Throughput** | Consistent high-volume | Hourly commitment | | **Batch Inference** | Async large-scale | Discounted per token |

## Common Patterns

### Invoke Model (Text Generation)

**AWS CLI:**

```bash # Invoke Claude aws bedrock-runtime invoke-model \ --model-id anthropic.claude-3-sonnet-20240229-v1:0 \ --content-type application/json \ --accept application/json \ --body '{ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain AWS Lambda in 3 sentences."} ] }' \ response.json

cat response.json | jq -r '.content[0].text' ```

**boto3:**

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def invoke_claude(prompt, max_tokens=1024): response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps({ 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': max_tokens, 'messages': [ {'role': 'user', 'content': prompt} ] }) )

result = json.loads(response['body'].read()) return result['content'][0]['text']

# Usage response = invoke_claude('What is Amazon S3?') print(response) ```

### Streaming Response

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def stream_claude(prompt): response = bedrock.invoke_model_with_response_stream( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps({ 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': 1024, 'messages': [ {'role': 'user', 'content': prompt} ] }) )

for event in response['body']: chunk = json.loads(event['chunk']['bytes']) if chunk['type'] == 'content_block_delta': yield chunk['delta'].get('text', '')

# Usage for text in stream_claude('Write a haiku about cloud computing.'): print(text, end='', flush=True) ```

### Generate Embeddings

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def get_embedding(text): response = bedrock.invoke_model( modelId='amazon.titan-embed-text-v2:0', contentType='application/json', accept='application/json', body=json.dumps({ 'inputText': text, 'dimensions': 1024, 'normalize': True }) )

result = json.loads(response['body'].read()) return result['embedding']

# Usage embedding = get_embedding('AWS Lambda is a serverless compute service.') print(f'Embedding dimension: {len(embedding)}') ```

### Conversation with History

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

class Conversation: def __init__(self, system_prompt=None): self.messages = [] self.system = system_prompt

def chat(self, user_message): self.messages.append({ 'role': 'user', 'content': user_message })

body = { 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': 1024, 'messages': self.messages }

if self.system: body['system'] = self.system

response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps(body) )

result = json.loads(response['body'].read()) assistant_message = result['content'][0]['text']

self.messages.append({ 'role': 'assistant', 'content': assistant_message })

return assistant_message

# Usage conv = Conversation(system_prompt='You are an AWS solutions architect.') print(conv.chat('What database should I use for a chat application?')) print(conv.chat('What about for time-series data?')) ```

### List Available Models

```bash # List all foundation models aws bedrock list-foundation-models \ --query 'modelSummaries[*].[modelId,modelName,providerName]' \ --output table

# Filter by provider aws bedrock list-foundation-models \ --by-provider anthropic \ --query 'modelSummaries[*].modelId'

# Get model details aws bedrock get-foundation-model \ --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 ```

### Request Model Access

```bash # List model access status aws bedrock list-foundation-model-agreement-offers \ --model-id anthropic.claude-3-sonnet-20240229-v1:0 ```

## CLI Reference

### Bedrock (Control Plane)

| Command | Description | |---------|-------------| | `aws bedrock list-foundation-models` | List available models | | `aws bedrock get-foundation-model` | Get model details | | `aws bedrock list-custom-models` | List fine-tuned models | | `aws bedrock create-model-customization-job` | Start fine-tuning | | `aws bedrock list-provisioned-model-throughputs` | List provisioned capacity |

### Bedrock Runtime (Data Plane)

| Command | Description | |---------|-------------| | `aws bedrock-runtime invoke-model` | Invoke model synchronously | | `aws bedrock-runtime invoke-model-with-response-stream` | Invoke with streaming | | `aws bedrock-runtime converse` | Multi-turn conversation API | | `aws bedrock-runtime converse-stream` | Streaming conversation |

### Bedrock Agent Runtime

| Command | Description | |---------|-------------| | `aws bedrock-agent-runtime invoke-agent` | Invoke a Bedrock agent | | `aws bedrock-agent-runtime retrieve` | Query knowledge base | | `aws bedrock-agent-runtime retrieve-and-generate` | RAG query |

## Best Practices

### Cost Optimization

- **Use appropriate models**: Smaller models for simple tasks - **Set max_tokens**: Limit output length when possible - **Cache responses**: For repeated identical queries - **Batch when possible**: Use batch inference for bulk processing - **Monitor usage**: Set up CloudWatch alarms for cost

### Performance

- **Use streaming**: For better user experience with long outputs - **Connection pooling**: Reuse boto3 clients - **Regional deployment**: Use closest region to reduce latency - **Provisioned throughput**: For consistent high-volume workloads

### Security

- **Least privilege IAM**: Only grant needed model access - **VPC endpoints**: Keep traffic private - **Guardrails**: Implement content filtering - **Audit with CloudTrail**: Track model invocations

### IAM Permissions

```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream" ], "Resource": [ "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0", "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0" ] } ] } ```

## Troubleshooting

### AccessDeniedException

**Causes:** - Model access not enabled in console - IAM policy missing `bedrock:InvokeModel` - Wrong model ID or region

**Debug:**

```bash # Check model access status aws bedrock list-foundation-models \ --query 'modelSummaries[?modelId==`anthropic.claude-3-sonnet-20240229-v1:0`]'

# Test IAM permissions aws iam simulate-principal-policy \ --policy-source-arn arn:aws:iam::123456789012:role/my-role \ --action-names bedrock:InvokeModel \ --resource-arns "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0" ```

### ModelNotReadyException

**Cause:** Model is still being provisioned or temporarily unavailable.

**Solution:** Implement retry with exponential backoff:

```python import time from botocore.exceptions import ClientError

def invoke_with_retry(bedrock, body, max_retries=3): for attempt in range(max_retries): try: return bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', body=json.dumps(body) ) except ClientError as e: if e.response['Error']['Code'] == 'ModelNotReadyException': time.sleep(2 ** attempt) else: raise raise Exception('Max retries exceeded') ```

### ThrottlingException

**Causes:** - Exceeded on-demand quota - Too many concurrent requests

**Solutions:** - Request quota increase - Implement exponential backoff - Consider provisioned throughput

### ValidationException

**Common issues:** - Invalid model ID - Malformed request body - max_tokens exceeds model limit

**Debug:**

```python # Check model-specific requirements aws bedrock get-foundation-model \ --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 \ --query 'modelDetails.inferenceTypesSupported' ```

## References

- [Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/) - [Bedrock API Reference](https://docs.aws.amazon.com/bedrock/latest/APIReference/) - [Bedrock Runtime API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Amazon_Bedrock_Runtime.html) - [Model Parameters](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) - [Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/)

--- name: bedrock description: AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns. last_updated: "2026-01-07" doc_source: https://docs.aws.amazon.com/bedrock/latest/userguide/ ---

# AWS Bedrock

Amazon Bedrock provides access to foundation models (FMs) from AI companies through a unified API. Build generative AI applications with text generation, embeddings, and image generation capabilities.

## Table of Contents

- [Core Concepts](#core-concepts) - [Common Patterns](#common-patterns) - [CLI Reference](#cli-reference) - [Best Practices](#best-practices) - [Troubleshooting](#troubleshooting) - [References](#references)

## Core Concepts

### Foundation Models

Pre-trained models available through Bedrock: - **Claude** (Anthropic): Text generation, analysis, coding - **Titan** (Amazon): Text, embeddings, image generation - **Llama** (Meta): Open-weight text generation - **Mistral**: Efficient text generation - **Stable Diffusion** (Stability AI): Image generation

### Model Access

Models must be enabled in your account before use: - Request access in Bedrock console - Some models require acceptance of EULAs - Access is region-specific

### Inference Types

| Type | Use Case | Pricing | |------|----------|---------| | **On-Demand** | Variable workloads | Per token | | **Provisioned Throughput** | Consistent high-volume | Hourly commitment | | **Batch Inference** | Async large-scale | Discounted per token |

## Common Patterns

### Invoke Model (Text Generation)

**AWS CLI:**

```bash # Invoke Claude aws bedrock-runtime invoke-model \ --model-id anthropic.claude-3-sonnet-20240229-v1:0 \ --content-type application/json \ --accept application/json \ --body '{ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain AWS Lambda in 3 sentences."} ] }' \ response.json

cat response.json | jq -r '.content[0].text' ```

**boto3:**

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def invoke_claude(prompt, max_tokens=1024): response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps({ 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': max_tokens, 'messages': [ {'role': 'user', 'content': prompt} ] }) )

result = json.loads(response['body'].read()) return result['content'][0]['text']

# Usage response = invoke_claude('What is Amazon S3?') print(response) ```

### Streaming Response

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def stream_claude(prompt): response = bedrock.invoke_model_with_response_stream( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps({ 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': 1024, 'messages': [ {'role': 'user', 'content': prompt} ] }) )

for event in response['body']: chunk = json.loads(event['chunk']['bytes']) if chunk['type'] == 'content_block_delta': yield chunk['delta'].get('text', '')

# Usage for text in stream_claude('Write a haiku about cloud computing.'): print(text, end='', flush=True) ```

### Generate Embeddings

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def get_embedding(text): response = bedrock.invoke_model( modelId='amazon.titan-embed-text-v2:0', contentType='application/json', accept='application/json', body=json.dumps({ 'inputText': text, 'dimensions': 1024, 'normalize': True }) )

result = json.loads(response['body'].read()) return result['embedding']

# Usage embedding = get_embedding('AWS Lambda is a serverless compute service.') print(f'Embedding dimension: {len(embedding)}') ```

### Conversation with History

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

class Conversation: def __init__(self, system_prompt=None): self.messages = [] self.system = system_prompt

def chat(self, user_message): self.messages.append({ 'role': 'user', 'content': user_message })

body = { 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': 1024, 'messages': self.messages }

if self.system: body['system'] = self.system

response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps(body) )

result = json.loads(response['body'].read()) assistant_message = result['content'][0]['text']

self.messages.append({ 'role': 'assistant', 'content': assistant_message })

return assistant_message

# Usage conv = Conversation(system_prompt='You are an AWS solutions architect.') print(conv.chat('What database should I use for a chat application?')) print(conv.chat('What about for time-series data?')) ```

### List Available Models

```bash # List all foundation models aws bedrock list-foundation-models \ --query 'modelSummaries[*].[modelId,modelName,providerName]' \ --output table

# Filter by provider aws bedrock list-foundation-models \ --by-provider anthropic \ --query 'modelSummaries[*].modelId'

# Get model details aws bedrock get-foundation-model \ --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 ```

### Request Model Access

```bash # List model access status aws bedrock list-foundation-model-agreement-offers \ --model-id anthropic.claude-3-sonnet-20240229-v1:0 ```

## CLI Reference

### Bedrock (Control Plane)

| Command | Description | |---------|-------------| | `aws bedrock list-foundation-models` | List available models | | `aws bedrock get-foundation-model` | Get model details | | `aws bedrock list-custom-models` | List fine-tuned models | | `aws bedrock create-model-customization-job` | Start fine-tuning | | `aws bedrock list-provisioned-model-throughputs` | List provisioned capacity |

### Bedrock Runtime (Data Plane)

| Command | Description | |---------|-------------| | `aws bedrock-runtime invoke-model` | Invoke model synchronously | | `aws bedrock-runtime invoke-model-with-response-stream` | Invoke with streaming | | `aws bedrock-runtime converse` | Multi-turn conversation API | | `aws bedrock-runtime converse-stream` | Streaming conversation |

### Bedrock Agent Runtime

| Command | Description | |---------|-------------| | `aws bedrock-agent-runtime invoke-agent` | Invoke a Bedrock agent | | `aws bedrock-agent-runtime retrieve` | Query knowledge base | | `aws bedrock-agent-runtime retrieve-and-generate` | RAG query |

## Best Practices

### Cost Optimization

- **Use appropriate models**: Smaller models for simple tasks - **Set max_tokens**: Limit output length when possible - **Cache responses**: For repeated identical queries - **Batch when possible**: Use batch inference for bulk processing - **Monitor usage**: Set up CloudWatch alarms for cost

### Performance

- **Use streaming**: For better user experience with long outputs - **Connection pooling**: Reuse boto3 clients - **Regional deployment**: Use closest region to reduce latency - **Provisioned throughput**: For consistent high-volume workloads

### Security

- **Least privilege IAM**: Only grant needed model access - **VPC endpoints**: Keep traffic private - **Guardrails**: Implement content filtering - **Audit with CloudTrail**: Track model invocations

### IAM Permissions

```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream" ], "Resource": [ "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0", "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0" ] } ] } ```

## Troubleshooting

### AccessDeniedException

**Causes:** - Model access not enabled in console - IAM policy missing `bedrock:InvokeModel` - Wrong model ID or region

**Debug:**

```bash # Check model access status aws bedrock list-foundation-models \ --query 'modelSummaries[?modelId==`anthropic.claude-3-sonnet-20240229-v1:0`]'

# Test IAM permissions aws iam simulate-principal-policy \ --policy-source-arn arn:aws:iam::123456789012:role/my-role \ --action-names bedrock:InvokeModel \ --resource-arns "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0" ```

### ModelNotReadyException

**Cause:** Model is still being provisioned or temporarily unavailable.

**Solution:** Implement retry with exponential backoff:

```python import time from botocore.exceptions import ClientError

def invoke_with_retry(bedrock, body, max_retries=3): for attempt in range(max_retries): try: return bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', body=json.dumps(body) ) except ClientError as e: if e.response['Error']['Code'] == 'ModelNotReadyException': time.sleep(2 ** attempt) else: raise raise Exception('Max retries exceeded') ```

### ThrottlingException

**Causes:** - Exceeded on-demand quota - Too many concurrent requests

**Solutions:** - Request quota increase - Implement exponential backoff - Consider provisioned throughput

### ValidationException

**Common issues:** - Invalid model ID - Malformed request body - max_tokens exceeds model limit

**Debug:**

```python # Check model-specific requirements aws bedrock get-foundation-model \ --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 \ --query 'modelDetails.inferenceTypesSupported' ```

## References

- [Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/) - [Bedrock API Reference](https://docs.aws.amazon.com/bedrock/latest/APIReference/) - [Bedrock Runtime API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Amazon_Bedrock_Runtime.html) - [Model Parameters](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) - [Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/)

--- name: bedrock description: AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns. last_updated: "2026-01-07" doc_source: https://docs.aws.amazon.com/bedrock/latest/userguide/ ---

# AWS Bedrock

Amazon Bedrock provides access to foundation models (FMs) from AI companies through a unified API. Build generative AI applications with text generation, embeddings, and image generation capabilities.

## Table of Contents

- [Core Concepts](#core-concepts) - [Common Patterns](#common-patterns) - [CLI Reference](#cli-reference) - [Best Practices](#best-practices) - [Troubleshooting](#troubleshooting) - [References](#references)

## Core Concepts

### Foundation Models

Pre-trained models available through Bedrock: - **Claude** (Anthropic): Text generation, analysis, coding - **Titan** (Amazon): Text, embeddings, image generation - **Llama** (Meta): Open-weight text generation - **Mistral**: Efficient text generation - **Stable Diffusion** (Stability AI): Image generation

### Model Access

Models must be enabled in your account before use: - Request access in Bedrock console - Some models require acceptance of EULAs - Access is region-specific

### Inference Types

| Type | Use Case | Pricing | |------|----------|---------| | **On-Demand** | Variable workloads | Per token | | **Provisioned Throughput** | Consistent high-volume | Hourly commitment | | **Batch Inference** | Async large-scale | Discounted per token |

## Common Patterns

### Invoke Model (Text Generation)

**AWS CLI:**

```bash # Invoke Claude aws bedrock-runtime invoke-model \ --model-id anthropic.claude-3-sonnet-20240229-v1:0 \ --content-type application/json \ --accept application/json \ --body '{ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain AWS Lambda in 3 sentences."} ] }' \ response.json

cat response.json | jq -r '.content[0].text' ```

**boto3:**

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def invoke_claude(prompt, max_tokens=1024): response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps({ 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': max_tokens, 'messages': [ {'role': 'user', 'content': prompt} ] }) )

result = json.loads(response['body'].read()) return result['content'][0]['text']

# Usage response = invoke_claude('What is Amazon S3?') print(response) ```

### Streaming Response

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def stream_claude(prompt): response = bedrock.invoke_model_with_response_stream( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps({ 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': 1024, 'messages': [ {'role': 'user', 'content': prompt} ] }) )

for event in response['body']: chunk = json.loads(event['chunk']['bytes']) if chunk['type'] == 'content_block_delta': yield chunk['delta'].get('text', '')

# Usage for text in stream_claude('Write a haiku about cloud computing.'): print(text, end='', flush=True) ```

### Generate Embeddings

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

def get_embedding(text): response = bedrock.invoke_model( modelId='amazon.titan-embed-text-v2:0', contentType='application/json', accept='application/json', body=json.dumps({ 'inputText': text, 'dimensions': 1024, 'normalize': True }) )

result = json.loads(response['body'].read()) return result['embedding']

# Usage embedding = get_embedding('AWS Lambda is a serverless compute service.') print(f'Embedding dimension: {len(embedding)}') ```

### Conversation with History

```python import boto3 import json

bedrock = boto3.client('bedrock-runtime')

class Conversation: def __init__(self, system_prompt=None): self.messages = [] self.system = system_prompt

def chat(self, user_message): self.messages.append({ 'role': 'user', 'content': user_message })

body = { 'anthropic_version': 'bedrock-2023-05-31', 'max_tokens': 1024, 'messages': self.messages }

if self.system: body['system'] = self.system

response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', contentType='application/json', accept='application/json', body=json.dumps(body) )

result = json.loads(response['body'].read()) assistant_message = result['content'][0]['text']

self.messages.append({ 'role': 'assistant', 'content': assistant_message })

return assistant_message

# Usage conv = Conversation(system_prompt='You are an AWS solutions architect.') print(conv.chat('What database should I use for a chat application?')) print(conv.chat('What about for time-series data?')) ```

### List Available Models

```bash # List all foundation models aws bedrock list-foundation-models \ --query 'modelSummaries[*].[modelId,modelName,providerName]' \ --output table

# Filter by provider aws bedrock list-foundation-models \ --by-provider anthropic \ --query 'modelSummaries[*].modelId'

# Get model details aws bedrock get-foundation-model \ --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 ```

### Request Model Access

```bash # List model access status aws bedrock list-foundation-model-agreement-offers \ --model-id anthropic.claude-3-sonnet-20240229-v1:0 ```

## CLI Reference

### Bedrock (Control Plane)

| Command | Description | |---------|-------------| | `aws bedrock list-foundation-models` | List available models | | `aws bedrock get-foundation-model` | Get model details | | `aws bedrock list-custom-models` | List fine-tuned models | | `aws bedrock create-model-customization-job` | Start fine-tuning | | `aws bedrock list-provisioned-model-throughputs` | List provisioned capacity |

### Bedrock Runtime (Data Plane)

| Command | Description | |---------|-------------| | `aws bedrock-runtime invoke-model` | Invoke model synchronously | | `aws bedrock-runtime invoke-model-with-response-stream` | Invoke with streaming | | `aws bedrock-runtime converse` | Multi-turn conversation API | | `aws bedrock-runtime converse-stream` | Streaming conversation |

### Bedrock Agent Runtime

| Command | Description | |---------|-------------| | `aws bedrock-agent-runtime invoke-agent` | Invoke a Bedrock agent | | `aws bedrock-agent-runtime retrieve` | Query knowledge base | | `aws bedrock-agent-runtime retrieve-and-generate` | RAG query |

## Best Practices

### Cost Optimization

- **Use appropriate models**: Smaller models for simple tasks - **Set max_tokens**: Limit output length when possible - **Cache responses**: For repeated identical queries - **Batch when possible**: Use batch inference for bulk processing - **Monitor usage**: Set up CloudWatch alarms for cost

### Performance

- **Use streaming**: For better user experience with long outputs - **Connection pooling**: Reuse boto3 clients - **Regional deployment**: Use closest region to reduce latency - **Provisioned throughput**: For consistent high-volume workloads

### Security

- **Least privilege IAM**: Only grant needed model access - **VPC endpoints**: Keep traffic private - **Guardrails**: Implement content filtering - **Audit with CloudTrail**: Track model invocations

### IAM Permissions

```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream" ], "Resource": [ "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0", "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0" ] } ] } ```

## Troubleshooting

### AccessDeniedException

**Causes:** - Model access not enabled in console - IAM policy missing `bedrock:InvokeModel` - Wrong model ID or region

**Debug:**

```bash # Check model access status aws bedrock list-foundation-models \ --query 'modelSummaries[?modelId==`anthropic.claude-3-sonnet-20240229-v1:0`]'

# Test IAM permissions aws iam simulate-principal-policy \ --policy-source-arn arn:aws:iam::123456789012:role/my-role \ --action-names bedrock:InvokeModel \ --resource-arns "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0" ```

### ModelNotReadyException

**Cause:** Model is still being provisioned or temporarily unavailable.

**Solution:** Implement retry with exponential backoff:

```python import time from botocore.exceptions import ClientError

def invoke_with_retry(bedrock, body, max_retries=3): for attempt in range(max_retries): try: return bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', body=json.dumps(body) ) except ClientError as e: if e.response['Error']['Code'] == 'ModelNotReadyException': time.sleep(2 ** attempt) else: raise raise Exception('Max retries exceeded') ```

### ThrottlingException

**Causes:** - Exceeded on-demand quota - Too many concurrent requests

**Solutions:** - Request quota increase - Implement exponential backoff - Consider provisioned throughput

### ValidationException

**Common issues:** - Invalid model ID - Malformed request body - max_tokens exceeds model limit

**Debug:**

```python # Check model-specific requirements aws bedrock get-foundation-model \ --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 \ --query 'modelDetails.inferenceTypesSupported' ```

## References

- [Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/) - [Bedrock API Reference](https://docs.aws.amazon.com/bedrock/latest/APIReference/) - [Bedrock Runtime API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Amazon_Bedrock_Runtime.html) - [Model Parameters](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) - [Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/)