bedrock

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
• Common Patterns
• CLI Reference
• Best Practices
• Troubleshooting
• References

Core Concepts

Foundation Models

Pre-trained models available through Bedrock:

• Claude (Anthropic): Text generation, analysis, coding
• Nova / Titan (Amazon): Text, multimodal, embeddings
• GPT / gpt-oss (OpenAI): Text generation, reasoning
• Llama (Meta): Open-weight text generation
• Mistral: Efficient text generation
• Stable Image (Stability AI): Image generation and editing

Model Access

In commercial Regions, access to all serverless models is enabled by default (no console opt-in). In GovCloud (US), models are still enabled manually on the Model access page (third-party models also in the linked commercial account):

• First invocation of a third-party model auto-subscribes via AWS Marketplace (up to 15 min); caller needs aws-marketplace:Subscribe, Unsubscribe, ViewSubscriptions
• Anthropic models on bedrock-runtime need a one-time use case form per account/org (put-use-case-for-model-access)
• Invoking implies EULA acceptance; to block a model, deny both bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream on it (SCP/IAM); streaming APIs such as ConverseStream use the latter. Denying aws-marketplace:Subscribe alone does not block first use

Endpoints

Endpoint	APIs	Use for
bedrock-runtime.{region}.amazonaws.com (recommended)	InvokeModel, Converse, Anthropic Messages (/anthropic), OpenAI Responses/Chat Completions (/openai/v1)	Guardrails, cross-Region inference, prompt routing, application inference profiles
bedrock-mantle.{region}.api.aws	OpenAI Responses/Chat Completions (/openai/v1), Anthropic Messages	Server-side tools (Web Search), background=true async, Projects/Workspaces, single-Region access to CRIS-only models

• Same per-token price on both; auth via SigV4 or Bedrock API key (AWS_BEARER_TOKEN_BEDROCK)
• IAM: bedrock:InvokeModel (runtime) vs bedrock-mantle:CreateInference (mantle)
• Responses API on bedrock-runtime is synchronous only and has no server-side tools

Inference Profiles and Model Lifecycle

• Newer models (e.g. Claude Sonnet 5) have no in-Region on-demand ID on bedrock-runtime: use a geo (us., eu., au.) or global. inference profile ID as modelId
• Lifecycle is Active -> Legacy -> EOL (see modelLifecycle in get-foundation-model). Legacy: no new Provisioned Throughput, fine-tuning, or quota increases; EOL: requests fail
• Model cards list an "EOL no sooner than" date; check before pinning a model ID

Knowledge Bases and Agents

• Managed knowledge bases (type: MANAGED): Bedrock runs storage, indexing, and retrieval. Only type that supports AgenticRetrieveStream (query decomposition, iterative retrieval, optional AgentCore Memory via memoryConfiguration)
• Native multimodal managed KBs embed video/audio/image directly with TwelveLabs Marengo Embed 3.0 (twelvelabs.marengo-embed-3-0-v1:0); query with text via Retrieve only (no RetrieveAndGenerate)
• Bedrock Agents Classic is in maintenance mode: closed to new accounts since July 30, 2026 (CreateAgent/InvokeInlineAgent return 403 without prior 12-month usage), model catalog frozen. Build new agents on Amazon Bedrock AgentCore

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:

# Invoke Claude
aws bedrock-runtime invoke-model \
  --model-id us.anthropic.claude-sonnet-5 \
  --content-type application/json \
  --accept application/json \
  --cli-binary-format raw-in-base64-out \
  --body '{
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 4096,
    "messages": [
      {"role": "user", "content": "Explain AWS Lambda in 3 sentences."}
    ]
  }' \
  response.json

# Claude Sonnet 5/Opus 5 think by default: content may start with a thinking block
cat response.json | jq -r '.content[] | select(.type=="text") | .text'

boto3:

import boto3
import json

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

def invoke_claude(prompt, max_tokens=4096):
    response = bedrock.invoke_model(
        modelId='us.anthropic.claude-sonnet-5',
        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())
    # Skip thinking blocks (adaptive thinking is on by default for Sonnet 5).
    # max_tokens caps thinking + text, so a truncated response may have no text block.
    if result['stop_reason'] == 'max_tokens':
        print('Truncated at max_tokens: raise it or lower output_config.effort')
    return next((b['text'] for b in result['content'] if b['type'] == 'text'), '')

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

Streaming Response

import boto3
import json

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

def stream_claude(prompt):
    response = bedrock.invoke_model_with_response_stream(
        modelId='us.anthropic.claude-sonnet-5',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': 4096,
            '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

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

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': 4096,
            'messages': self.messages
        }

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

        response = bedrock.invoke_model(
            modelId='us.anthropic.claude-sonnet-5',
            contentType='application/json',
            accept='application/json',
            body=json.dumps(body)
        )

        result = json.loads(response['body'].read())
        if result['stop_reason'] == 'max_tokens':
            # max_tokens caps thinking + text; don't store a truncated/empty turn
            self.messages.pop()
            raise RuntimeError('Truncated at max_tokens: raise it or lower output_config.effort')
        assistant_message = next(
            (b['text'] for b in result['content'] if b['type'] == '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

# 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 (includes modelLifecycle.status)
aws bedrock get-foundation-model \
  --model-identifier anthropic.claude-sonnet-5

Check Model Access

# agreementAvailability.status AVAILABLE / NOT_AVAILABLE, authorizationStatus
aws bedrock get-foundation-model-availability \
  --model-id anthropic.claude-sonnet-5

# Anthropic one-time use case form (base64-encoded JSON:
# companyName, companyWebsite, intendedUsers, industryOption, otherIndustryOption, useCases)
aws bedrock put-use-case-for-model-access --form-data <base64-json>

# Programmatic agreement for third-party models
aws bedrock list-foundation-model-agreement-offers --model-id <model-id>
aws bedrock create-foundation-model-agreement --model-id <model-id> --offer-token <token>

Count Tokens

# Free; returns inputTokens. Not supported for every model (e.g. CRIS-only Claude models)
aws bedrock-runtime count-tokens \
  --model-id anthropic

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