AWS Bedrock Provider Guide

Enterprise AI with Claude, Nova, Llama, Mistral, DeepSeek, Qwen, and 110+ foundation models on AWS infrastructure

---

Overview

Amazon Bedrock provides serverless access to 110+ foundation models from leading AI companies including Anthropic, Amazon, Meta, Mistral, DeepSeek, Qwen, Cohere, AI21 Labs, Google, NVIDIA, Writer, and more. Perfect for enterprise deployments requiring AWS integration, scalability, and compliance.

Inference Profile ARN Required

For Anthropic Claude models, you MUST use the full inference profile ARN, not simple model names. For Claude 4+ models, use cross-region inference profile IDs (e.g., us.anthropic.claude-sonnet-4-6) or full ARNs for on-demand access. See configuration examples below for the correct format.

Key Benefits

• 🤖 110+ Models: Claude, Nova, Llama 4, Mistral, DeepSeek, Qwen, Cohere, and more
• 🏢 AWS Integration: IAM, VPC, CloudWatch, S3
• 🌍 Global Regions: 10+ AWS regions
• 🔒 Enterprise Security: PrivateLink, KMS encryption
• 💰 Pay-per-use: No infrastructure costs
• 📊 Serverless: Automatic scaling
• 🛡️ Compliance: SOC 2, HIPAA, ISO 27001

Available Model Providers

Provider	Key Models (count)	Best For
Anthropic	Claude 4.6 Opus/Sonnet, 4.5, 4.1, 4, 3.7, 3.5, 3 (12)	Complex reasoning, coding, 1M context
Amazon	Nova Premier/Pro/Lite/Micro, Nova 2, Sonic, Canvas, Reel (11)	AWS-native, multimodal, media generation
Meta	Llama 4 Scout/Maverick, 3.3, 3.2, 3.1, 3 (12)	Open source, long context (10M)
Mistral AI	Large 3, Magistral, Ministral, Pixtral, Voxtral, Devstral (14)	European compliance, coding, multimodal
DeepSeek	R1, V3 (2)	Deep reasoning, cost-effective
Qwen	Qwen 3, Qwen 3 Coder, Qwen 3 VL, Qwen 3 Next (6)	Coding, vision, multilingual
Cohere	Command R/R+, Embed v3/v4, Rerank v3.5 (6)	Enterprise search, RAG, reranking
AI21 Labs	Jamba 1.5 Large/Mini (2)	Long context
Google	Gemma 3 (27B, 12B, 4B) (3)	Lightweight open models
Other	NVIDIA Nemotron, Writer Palmyra, MiniMax, Kimi, OpenAI gpt-oss, Z.AI GLM	Specialized workloads

---

Quick Start

1. Enable Model Access

# Via AWS CLI
aws bedrock list-foundation-models --region us-east-1

# Request model access (one-time)
# Go to: https://console.aws.amazon.com/bedrock
# → Model access → Manage model access
# → Select models → Request access

Or via AWS Console:

1. Open Bedrock Console
2. Select region (us-east-1 recommended)
3. Click "Model access"
4. Enable desired models (instant for most, approval needed for some)

2. Setup IAM Permissions

# Create IAM policy
cat > bedrock-policy.json <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream"
      ],
      "Resource": "*"
    }
  ]
}
EOF

# Create policy
aws iam create-policy \
  --policy-name BedrockInvokePolicy \
  --policy-document file://bedrock-policy.json

# Attach to user/role
aws iam attach-user-policy \
  --user-name my-user \
  --policy-arn arn:aws:iam::ACCOUNT_ID:policy/BedrockInvokePolicy

3. Configure AWS Credentials

# Option A: AWS CLI credentials
aws configure
# AWS Access Key ID: YOUR_KEY
# AWS Secret Access Key: YOUR_SECRET
# Default region: us-east-1

# Option B: Environment variables
export AWS_ACCESS_KEY_ID=your_key
export AWS_SECRET_ACCESS_KEY=your_secret
export AWS_REGION=us-east-1

4. Configure NeuroLink

import { NeuroLink } from "@juspay/neurolink";

const ai = new NeuroLink({
  providers: [
    {
      name: "bedrock",
      config: {
        region: "us-east-1",
        // Credentials automatically loaded from:
        // 1. Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
        // 2. ~/.aws/credentials
        // 3. EC2 instance metadata
      },
    },
  ],
});

const result = await ai.generate({
  input: { text: "Hello from AWS Bedrock!" },
  provider: "bedrock",
  model: "anthropic.claude-sonnet-4-5-20250929-v1:0", // or use cross-region profile: "us.anthropic.claude-sonnet-4-5-20250929-v1:0"
});

console.log(result.content);

---

Regional Deployment

Available Regions

Region	Location	Models Available	Data Residency
us-east-1	N. Virginia	All models	USA
us-west-2	Oregon	All models	USA
us-gov-west-1	GovCloud West	Select models	USA Gov
ca-central-1	Canada	Most models	Canada
eu-west-1	Ireland	All models	EU
eu-west-2	London	Most models	UK
eu-west-3	Paris	Most models	EU
eu-central-1	Frankfurt	All models	EU
ap-southeast-1	Singapore	Most models	Asia
ap-northeast-1	Tokyo	Most models	Asia
ap-south-1	Mumbai	Select models	India

Multi-Region Setup

const ai = new NeuroLink({
  providers: [
    // US East (primary)
    {
      name: "bedrock-us-east",
      priority: 1,
      config: {
        region: "us-east-1",
        credentials: {
          accessKeyId: process.env.AWS_ACCESS_KEY_ID,
          secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
        },
      },
      condition: (req) => req.userRegion === "us",
    },

    // EU West (GDPR)
    {
      name: "bedrock-eu",
      priority: 1,
      config: {
        region: "eu-west-1",
        credentials: {
          accessKeyId: process.env.AWS_ACCESS_KEY_ID,
          secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
        },
      },
      condition: (req) => req.userRegion === "eu",
    },

    // Asia Pacific
    {
      name: "bedrock-asia",
      priority: 1,
      config: {
        region: "ap-southeast-1",
        credentials: {
          accessKeyId: process.env.AWS_ACCESS_KEY_ID,
          secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
        },
      },
      condition: (req) => req.userRegion === "asia",
    },
  ],
  failoverConfig: { enabled: true },
});

---

Model Selection Guide

Default Model Updated

The SDK default fallback is now anthropic.claude-sonnet-4-6. The previous default (anthropic.claude-3-sonnet-20240229-v1:0) is deprecated. You can still override the model by setting BEDROCK_MODEL in your environment or passing the model parameter in code.

Anthropic Claude Models

// Claude 4.6 Opus - Latest, most capable (1M context)
const opus46 = await ai.generate({
  input: { text: "Most difficult reasoning task" },
  provider: "bedrock",
  model: "anthropic.claude-opus-4-6-v1:0",
});

// Claude 4.5 Sonnet - Balanced performance (recommended)
const sonnet45 = await ai.generate({
  input: { text: "Complex analysis task" },
  provider: "bedrock",
  model: "anthropic.claude-sonnet-4-5-20250929-v1:0",
});

// Claude 3.5 Haiku - Fast and affordable
const haiku35 = await ai.generate({
  input: { text: "Quick simple query" },
  provider: "bedrock",
  model: "anthropic.claude-3-5-haiku-20241022-v1:0",
});

Claude Model IDs:

Model ID	Series	Context
anthropic.claude-opus-4-6-v1:0	Claude 4.6 Opus	1M
anthropic.claude-sonnet-4-6	Claude 4.6 Sonnet	1M
anthropic.claude-opus-4-5-20251124-v1:0	Claude 4.5 Opus	200K
anthropic.claude-sonnet-4-5-20250929-v1:0	Claude 4.5 Sonnet	200K
anthropic.claude-haiku-4-5-20251001-v1:0	Claude 4.5 Haiku	200K
anthropic.claude-opus-4-1-20250805-v1:0	Claude 4.1 Opus	200K
anthropic.claude-sonnet-4-20250514-v1:0	Claude 4 Sonnet	200K
anthropic.claude-3-7-sonnet-20250219-v1:0	Claude 3.7 Sonnet	200K
anthropic.claude-3-5-sonnet-20241022-v1:0	Claude 3.5 Sonnet	200K
anthropic.claude-3-5-haiku-20241022-v1:0	Claude 3.5 Haiku	200K
anthropic.claude-3-opus-20240229-v1:0	Claude 3 Opus (legacy)	200K
anthropic.claude-3-haiku-20240307-v1:0	Claude 3 Haiku (legacy)	200K

Tip: For Claude 4+ models, AWS recommends cross-region inference profile IDs (e.g., us.anthropic.claude-sonnet-4-6) instead of bare model IDs. Cross-region profiles enable automatic routing across regions for better availability. See the Bedrock cross-region inference docs for details.

Amazon Nova Models

Amazon Nova is the first-party model family from AWS, spanning text, multimodal, media generation, speech, and embeddings.

// Nova Pro - Multimodal, balanced (300K context)
const novaPro = await ai.generate({
  input: { text: "Analyze this data" },
  provider: "bedrock",
  model: "amazon.nova-pro-v1:0",
});

// Nova Micro - Text only, fastest (128K context)
const novaMicro = await ai.generate({
  input: { text: "Quick classification" },
  provider: "bedrock",
  model: "amazon.nova-micro-v1:0",
});

// Nova 2 Lite - Next-gen multimodal (1M context)
const nova2Lite = await ai.generate({
  input: { text: "Long document analysis" },
  provider: "bedrock",
  model: "amazon.nova-2-lite-v1:0",
});

Nova Model IDs:

Model ID	Type	Context	Notes
amazon.nova-premier-v1:0	Text + Multimodal	1M+	Most capable Nova
amazon.nova-pro-v1:0	Text + Multimodal	300K	Balanced
amazon.nova-lite-v1:0	Text + Multimodal	300K	Cost-effective
amazon.nova-micro-v1:0	Text only	128K	Fastest, cheapest
amazon.nova-2-lite-v1:0	Text + Multimodal	1M	Gen 2, extended context
amazon.nova-2-sonic-v1:0	Speech-to-speech	--	Gen 2 voice
amazon.nova-sonic-v1:0	Speech-to-speech	--	Real-time voice
amazon.nova-canvas-v1:0	Image generation	--	Text-to-image
amazon.nova-reel-v1:0	Video generation	--	Text-to-video
amazon.nova-reel-v1:1	Video generation	--	Improved v1.1
amazon.nova-2-multimodal-embeddings-v1:0	Embeddings	--	Multimodal embeddings

Meta Llama Models

// Llama 4 Scout - Massive 10M context window
const scout = await ai.generate({
  input: { text: "Analyze this very long document" },
  provider: "bedrock",
  model: "meta.llama4-scout-17b-instruct-v1:0",
});

// Llama 4 Maverick - 1M context, higher quality
const maverick = await ai.generate({
  input: { text: "Complex reasoning task" },
  provider: "bedrock",
  model: "meta.llama4-maverick-17b-instruct-v1:0",
});

// Llama 3.3 70B - Strong general-purpose
const llama33 = await ai.generate({
  input: { text: "General task" },
  provider: "bedrock",
  model: "meta.llama3-3-70b-instruct-v1:0",
});

Llama Model IDs:

Model ID	Series	Context	Notes
meta.llama4-scout-17b-instruct-v1:0	Llama 4 Scout	10M	Largest context window on Bedrock
meta.llama4-maverick-17b-instruct-v1:0	Llama 4 Maverick	1M	Higher quality
meta.llama3-3-70b-instruct-v1:0	Llama 3.3	128K	Strong general-purpose
meta.llama3-2-90b-instruct-v1:0	Llama 3.2	128K	Multimodal (vision)
meta.llama3-2-11b-instruct-v1:0	Llama 3.2	128K	Multimodal (vision)
meta.llama3-2-3b-instruct-v1:0	Llama 3.2	128K	Lightweight
meta.llama3-2-1b-instruct-v1:0	Llama 3.2	128K	Edge / mobile
meta.llama3-1-405b-instruct-v1:0	Llama 3.1	128K	Largest open model
meta.llama3-1-70b-instruct-v1:0	Llama 3.1	128K	Balanced
meta.llama3-1-8b-instruct-v1:0	Llama 3.1	128K	Fast, cheap
meta.llama3-70b-instruct-v1:0	Llama 3 (legacy)	8K	--
meta.llama3-8b-instruct-v1:0	Llama 3 (legacy)	8K	--

Mistral AI Models

// Mistral Large 3 (675B) - Most capable
const mistralLarge3 = await ai.generate({
  input: { text: "Complex reasoning" },
  provider: "bedrock",
  model: "mistral.mistral-large-3-675b-instruct",
});

// Pixtral Large - Vision + text
const pixtral = await ai.generate({
  input: { text: "Describe this image" },
  provider: "bedrock",
  model: "mistral.pixtral-large-2502-v1:0",
});

Mistral Model IDs:

Model ID	Type	Notes
mistral.mistral-large-3-675b-instruct	Large 3 (675B)	Most capable
mistral.mistral-large-2407-v1:0	Large 2	Previous gen
mistral.mistral-large-2402-v1:0	Large (legacy)	--
mistral.magistral-small-2509	Magistral Small	Reasoning-focused
mistral.ministral-3-14b-instruct	Ministral 14B	Mid-size
mistral.ministral-3-8b-instruct	Ministral 8B	Lightweight
mistral.ministral-3-3b-instruct	Ministral 3B	Edge / mobile
mistral.pixtral-large-2502-v1:0	Pixtral Large	Vision + text
mistral.voxtral-small-24b-2507	Voxtral Small 24B	Audio + text
mistral.voxtral-mini-3b-2507	Voxtral Mini 3B	Audio (lightweight)
mistral.devstral-2-123b	Devstral 2 (123B)	Coding-focused
mistral.mistral-7b-instruct-v0:2	Mistral 7B (legacy)	--
mistral.mixtral-8x7b-instruct-v0:1	Mixtral 8x7B (legacy)	--

DeepSeek, Qwen, and Other Models

// DeepSeek R1 - Deep reasoning
const deepseek = await ai.generate({
  input: { text: "Solve this step by step" },
  provider: "bedrock",
  model: "deepseek.r1-v1:0",
});

// Qwen 3 (235B) - Multilingual, coding
const qwen = await ai.generate({
  input: { text: "Write a Python function" },
  provider: "bedrock",
  model: "qwen.qwen3-235b-a22b-2507-v1:0",
});

Additional Model IDs:

Model ID	Provider	Notes
deepseek.r1-v1:0	DeepSeek	Deep reasoning (R1)
deepseek.v3-v1:0	DeepSeek	General-purpose (V3)
qwen.qwen3-235b-a22b-2507-v1:0	Qwen	Flagship (235B MoE)
qwen.qwen3-coder-480b-a35b-v1:0	Qwen	Coding (480B MoE)
qwen.qwen3-coder-30b-a3b-v1:0	Qwen	Coding (lightweight)
qwen.qwen3-32b-v1:0	Qwen	Dense 32B
qwen.qwen3-next-80b-a3b	Qwen	Next-gen MoE
qwen.qwen3-vl-235b-a22b	Qwen	Vision-language
ai21.jamba-1-5-large-v1:0	AI21 Labs	Jamba 1.5 Large (256K context)
ai21.jamba-1-5-mini-v1:0	AI21 Labs	Jamba 1.5 Mini
google.gemma-3-27b-it	Google	Gemma 3 27B
google.gemma-3-12b-it	Google	Gemma 3 12B
google.gemma-3-4b-it	Google	Gemma 3 4B
nvidia.nemotron-nano-3-30b	NVIDIA	Nemotron Nano 30B (256K context)
writer.palmyra-x5-v1:0	Writer	Palmyra X5 (1M context)
openai.gpt-oss-120b-1:0	OpenAI (open source)	GPT-OSS 120B

Embedding and Reranking Models

// Nova 2 Multimodal Embeddings (text + image)
const novaEmbed = await ai.embed({
  texts: ["Document 1", "Document 2"],
  provider: "bedrock",
  model: "amazon.nova-2-multimodal-embeddings-v1:0",
});

// Cohere Embed v4 - Latest embeddings
const cohereEmbed = await ai.embed({
  texts: ["Query text"],
  provider: "bedrock",
  model: "cohere.embed-v4:0",
});

// Titan Embeddings v2 (legacy)
const titanEmbed = await ai.embed({
  texts: ["Document"],
  provider: "bedrock",
  model: "amazon.titan-embed-text-v2:0",
});

Embedding & Reranking Model IDs:

Model ID	Type	Notes
amazon.nova-2-multimodal-embeddings-v1:0	Embedding	Multimodal (text + image)
cohere.embed-v4:0	Embedding	Latest Cohere embeddings
cohere.embed-english-v3	Embedding	English-optimized
cohere.embed-multilingual-v3	Embedding	100+ languages
amazon.titan-embed-text-v2:0	Embedding	1024 dimensions
amazon.titan-embed-text-v1	Embedding	1536 dimensions
amazon.titan-embed-image-v1	Embedding	Multimodal (legacy)
cohere.rerank-v3-5:0	Reranking	Relevance scoring for RAG
amazon.rerank-v1:0	Reranking	AWS-native reranking

Cohere Generation Models

// Command R+ - RAG optimized
const commandRPlus = await ai.generate({
  input: { text: "Search and summarize documents" },
  provider: "bedrock",
  model: "cohere.command-r-plus-v1:0",
});

Cohere Generation Model IDs:

• cohere.command-r-plus-v1:0 - Command R+ (best for RAG)
• cohere.command-r-v1:0 - Command R (lighter)

---

IAM Roles & Permissions

EC2 Instance Role

# Create trust policy
cat > trust-policy.json <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "ec2.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}
EOF

# Create role
aws iam create-role \
  --role-name BedrockEC2Role \
  --assume-role-policy-document file://trust-policy.json

# Attach Bedrock policy
aws iam attach-role-policy \
  --role-name BedrockEC2Role \
  --policy-arn arn:aws:iam::ACCOUNT_ID:policy/BedrockInvokePolicy

# Create instance profile
aws iam create-instance-profile \
  --instance-profile-name BedrockEC2Profile

# Add role to profile
aws iam add-role-to-instance-profile \
  --instance-profile-name BedrockEC2Profile \
  --role-name BedrockEC2Role

Lambda Execution Role

# Lambda trust policy
cat > lambda-trust.json <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}
EOF

# Create Lambda role
aws iam create-role \
  --role-name BedrockLambdaRole \
  --assume-role-policy-document file://lambda-trust.json

# Attach policies
aws iam attach-role-policy \
  --role-name BedrockLambdaRole \
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole

aws iam attach-role-policy \
  --role-name BedrockLambdaRole \
  --policy-arn arn:aws:iam::ACCOUNT_ID:policy/BedrockInvokePolicy

EKS Service Account

# eks-service-account.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: bedrock-sa
  namespace: default
  annotations:
    eks.amazonaws.com/role-arn: arn:aws:iam::ACCOUNT_ID:role/BedrockEKSRole

# Create IRSA (IAM Roles for Service Accounts)
eksctl create iamserviceaccount \
  --name bedrock-sa \
  --namespace default \
  --cluster my-cluster \
  --attach-policy-arn arn:aws:iam::ACCOUNT_ID:policy/BedrockInvokePolicy \
  --approve

---

VPC & Private Connectivity

VPC Endpoint (PrivateLink)

# Create VPC endpoint for Bedrock
aws ec2 create-vpc-endpoint \
  --vpc-id vpc-12345678 \
  --service-name com.amazonaws.us-east-1.bedrock-runtime \
  --route-table-ids rtb-12345678 \
  --subnet-ids subnet-12345678 subnet-87654321 \
  --security-group-ids sg-12345678

Security Group Configuration

# Create security group
aws ec2 create-security-group \
  --group-name bedrock-endpoint-sg \
  --description "Security group for Bedrock VPC endpoint" \
  --vpc-id vpc-12345678

# Allow HTTPS inbound from VPC CIDR
aws ec2 authorize-security-group-ingress \
  --group-id sg-12345678 \
  --protocol tcp \
  --port 443 \
  --cidr 10.0.0.0/16

Private Endpoint Usage

// Use VPC endpoint URL
const ai = new NeuroLink({
  providers: [
    {
      name: "bedrock",
      config: {
        region: "us-east-1",
        endpoint:
          "https://vpce-12345678.bedrock-runtime.us-east-1.vpce.amazonaws.com",
      },
    },
  ],
});

---

Monitoring & Logging

CloudWatch Metrics

import { CloudWatch } from "@aws-sdk/client-cloudwatch";

const cloudwatch = new CloudWatch({ region: "us-east-1" });

async function logMetric(tokens: number, cost: number) {
  await cloudwatch.putMetricData({
    Namespace: "Bedrock/Usage",
    MetricData: [
      {
        MetricName: "TokensUsed",
        Value: tokens,
        Unit: "Count",
        Timestamp: new Date(),
      },
      {
        MetricName: "Cost",
        Value: cost,
        Unit: "None",
        Timestamp: new Date(),
      },
    ],
  });
}

const ai = new NeuroLink({
  providers: [{ name: "bedrock", config: { region: "us-east-1" } }],
  onSuccess: async (result) => {
    await logMetric(result.usage.totalTokens, result.cost);
  },
});

CloudWatch Logs

import { CloudWatchLogs } from "@aws-sdk/client-cloudwatch-logs";

const logs = new CloudWatchLogs({ region: "us-east-1" });

async function logRequest(data: any) {
  await logs.putLogEvents({
    logGroupName: "/aws/bedrock/requests",
    logStreamName: "production",
    logEvents: [
      {
        timestamp: Date.now(),
        message: JSON.stringify(data),
      },
    ],
  });
}

const ai = new NeuroLink({
  providers: [{ name: "bedrock", config: { region: "us-east-1" } }],
  onSuccess: async (result) => {
    await logRequest({
      model: result.model,
      tokens: result.usage.totalTokens,
      latency: result.latency,
      cost: result.cost,
    });
  },
});

---

Cost Management

Pricing Overview

Pricing varies by model and changes frequently. Check the Bedrock pricing page for current rates. Representative examples:

Claude 4.5 Sonnet:
- Input:  $3.00 per 1M tokens
- Output: $15.00 per 1M tokens

Claude 3.5 Haiku:
- Input:  $0.80 per 1M tokens
- Output: $4.00 per 1M tokens

Nova Pro:
- Input:  $0.80 per 1M tokens
- Output: $3.20 per 1M tokens

Nova Micro:
- Input:  $0.035 per 1M tokens
- Output: $0.14 per 1M tokens

Llama 4 Scout:
- Input:  $0.17 per 1M tokens
- Output: $0.17 per 1M tokens

Llama 3.1 405B:
- Input:  $2.65 per 1M tokens
- Output: $3.50 per 1M tokens

Mistral Large 3:
- Input:  $2.00 per 1M tokens
- Output: $6.00 per 1M tokens

DeepSeek R1:
- Input:  $1.35 per 1M tokens
- Output: $5.40 per 1M tokens

Cost Budgets

# Create budget for Bedrock
aws budgets create-budget \
  --account-id ACCOUNT_ID \
  --budget file://budget.json

# budget.json
cat > budget.json <<EOF
{
  "BudgetName": "BedrockMonthlyBudget",
  "BudgetLimit": {
    "Amount": "1000",
    "Unit": "USD"
  },
  "TimeUnit": "MONTHLY",
  "BudgetType": "COST",
  "CostFilters": {
    "Service": ["Amazon Bedrock"]
  }
}
EOF

Cost Tracking

class BedrockCostTracker {
  private monthlyCost = 0;

  calculateCost(
    model: string,
    inputTokens: number,
    outputTokens: number,
  ): number {
    const pricing: Record<string, { input: number; output: number }> = {
      "anthropic.claude-sonnet-4-5-20250929-v1:0": { input: 3.0, output: 15.0 },
      "anthropic.claude-3-5-haiku-20241022-v1:0": { input: 0.8, output: 4.0 },
      "amazon.nova-pro-v1:0": { input: 0.8, output: 3.2 },
      "amazon.nova-micro-v1:0": { input: 0.035, output: 0.14 },
      "meta.llama4-scout-17b-instruct-v1:0": { input: 0.17, output: 0.17 },
    };

    const rates = pricing[model] || { input: 1.0, output: 1.0 };
    const cost =
      (inputTokens / 1_000_000) * rates.input +
      (outputTokens / 1_000_000) * rates.output;

    this.monthlyCost += cost;
    return cost;
  }

  getMonthlyTotal(): number {
    return this.monthlyCost;
  }
}

---

Production Patterns

Pattern 1: Multi-Model Strategy

const ai = new NeuroLink({
  providers: [
    // Cheap for simple tasks (Nova Micro - $0.035/1M input)
    {
      name: "bedrock-nova-micro",
      config: { region: "us-east-1" },
      model: "amazon.nova-micro-v1:0",
      condition: (req) => req.complexity === "low",
    },

    // Balanced for medium tasks
    {
      name: "bedrock-sonnet",
      config: { region: "us-east-1" },
      model: "anthropic.claude-sonnet-4-5-20250929-v1:0",
      condition: (req) => req.complexity === "medium",
    },

    // Premium for complex tasks (1M context)
    {
      name: "bedrock-opus",
      config: { region: "us-east-1" },
      model: "anthropic.claude-opus-4-6-v1:0",
      condition: (req) => req.complexity === "high",
    },
  ],
});

Pattern 2: Guardrails

// Enable Bedrock Guardrails
const ai = new NeuroLink({
  providers: [
    {
      name: "bedrock",
      config: {
        region: "us-east-1",
        guardrailId: "abc123xyz", // Created in Bedrock console
        guardrailVersion: "1",
      },
    },
  ],
});

const result = await ai.generate({
  input: { text: "Your prompt" },
  provider: "bedrock",
  model: "anthropic.claude-sonnet-4-5-20250929-v1:0",
});
// Content filtered by guardrails

Pattern 3: Knowledge Base Integration

# Create Knowledge Base in Bedrock
aws bedrock-agent create-knowledge-base \
  --name my-kb \
  --role-arn arn:aws:iam::ACCOUNT_ID:role/BedrockKBRole \
  --knowledge-base-configuration '{
    "type": "VECTOR",
    "vectorKnowledgeBaseConfiguration": {
      "embeddingModelArn": "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0"
    }
  }' \
  --storage-configuration '{
    "type": "OPENSEARCH_SERVERLESS",
    "opensearchServerlessConfiguration": {
      "collectionArn": "arn:aws:aoss:us-east-1:ACCOUNT_ID:collection/abc",
      "vectorIndexName": "my-index",
      "fieldMapping": {
        "vectorField": "embedding",
        "textField": "text",
        "metadataField": "metadata"
      }
    }
  }'

---

Best Practices

1. ✅ Use IAM Roles Instead of Keys

// ✅ Good: EC2 instance role (no keys)
const ai = new NeuroLink({
  providers: [
    {
      name: "bedrock",
      config: { region: "us-east-1" },
      // Credentials from instance metadata
    },
  ],
});

2. ✅ Enable VPC Endpoints

# ✅ Good: Private connectivity
aws ec2 create-vpc-endpoint \
  --service-name com.amazonaws.us-east-1.bedrock-runtime

3. ✅ Monitor Costs

// ✅ Good: Track every request
const cost = costTracker.calculateCost(model, inputTokens, outputTokens);

4. ✅ Use Appropriate Model for Task

// ✅ Good: Match model to complexity
const model = complexity === "low" ? "claude-haiku" : "claude-sonnet";

5. ✅ Enable CloudWatch Logging

// ✅ Good: Comprehensive logging
await logs.putLogEvents({
  /* ... */
});

---

Troubleshooting

Common Issues

1. "Model Access Denied"

Problem: Model not enabled in your account.

Solution:

# Enable via console
# https://console.aws.amazon.com/bedrock → Model access

# Or check status
aws bedrock list-foundation-models --region us-east-1

2. "Throttling Exception"

Problem: Exceeded rate limits.

Solution:

# Request quota increase
aws service-quotas request-service-quota-increase \
  --service-code bedrock \
  --quota-code L-12345678 \
  --desired-value 1000

3. "Invalid Model ID"

Problem: Wrong model identifier.

Solution:

# List available models
aws bedrock list-foundation-models --region us-east-1

# Use exact model ID
model: 'anthropic.claude-sonnet-4-5-20250929-v1:0'  # ✅ Correct

---

Related Documentation

• Provider Setup - General configuration
• Multi-Region - Geographic distribution
• Cost Optimization - Reduce costs
• Compliance - Security

---

Additional Resources

• AWS Bedrock Docs - Official documentation
• Bedrock Pricing - Pricing details
• Bedrock Console - Manage models
• AWS CLI Reference - CLI commands

---

Need Help? Join our GitHub Discussions or open an issue.