Integrate Narev with AWS Bedrock for LLM Cost Optimization

The Problem with AWS Bedrock Alone

AWS Bedrock is an excellent managed service—it provides secure access to foundation models with enterprise features like VPC support, AWS IAM integration, and CloudWatch monitoring. But that access creates a new challenge: choosing the right model.

With multiple foundation models available (Claude, Llama, Titan, Command, Jurassic), teams often:

• Stick with expensive defaults because switching feels risky
• Test models manually by deploying to production and hoping for the best
• Guess at which model offers the best cost-quality-latency tradeoff
• Miss optimization opportunities because testing is time-consuming

The result? Most teams overspend on LLMs by 10-100x because they lack systematic testing.

How Narev + AWS Bedrock Work Together

Narev and AWS Bedrock complement each other perfectly:

The workflow:

1. Export production usage data from AWS CloudWatch or application logs
2. Test alternative model configurations in Narev with A/B experiments
3. Deploy winners to AWS Bedrock with confidence
4. Monitor results using CloudWatch and repeat continuously

Integration Guide

Step 1: Export Your AWS Bedrock Usage Data

Narev works with your existing Bedrock usage patterns to create realistic test scenarios. Export your recent prompts, model selections, and response patterns from CloudWatch Logs or your application traces to build experiments that reflect your actual production workload.

Step 2: Create Your First Experiment

You can test any models through Narev, even if you're considering models not available on Bedrock. For example, comparing Claude 3.5 Haiku with GPT-4o Mini:

Create an experiment in Narev testing:

Narev will test both variants on the same prompts and measure:

• Cost per request and per million tokens
• Latency (time to first token, total response time)
• Quality (accuracy, completeness, tone)

Step 3: Analyze Results with Confidence

Narev provides clear data on which model performs best:

Step 4: Update Your AWS Bedrock Configuration

With data-backed confidence, update your Bedrock integration:

Option A: Using boto3 (Python)

# Before: Using Claude 3.5 Sonnet
import boto3
import json
 
bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')
 
response = bedrock.invoke_model(
    modelId='anthropic.claude-3-5-sonnet-20241022-v2:0',  # ← Old default
    body=json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "messages": [{"role": "user", "content": "Hello"}],
        "max_tokens": 1024
    })
)
 
# After: Switch to Claude 3.5 Haiku based on Narev results
response = bedrock.invoke_model(
    modelId='anthropic.claude-3-5-haiku-20241022-v1:0',  # ← Tested winner
    body=json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "messages": [{"role": "user", "content": "Hello"}],
        "max_tokens": 1024
    })
)

Option B: Using AWS SDK (Node.js)

// Before: Using Claude 3.5 Sonnet
import { BedrockRuntimeClient, InvokeModelCommand } from "@aws-sdk/client-bedrock-runtime";
 
const client = new BedrockRuntimeClient({ region: "us-east-1" });
 
const command = new InvokeModelCommand({
  modelId: "anthropic.claude-3-5-sonnet-20241022-v2:0", // ← Old default
  body: JSON.stringify({
    anthropic_version: "bedrock-2023-05-31",
    messages: [{ role: "user", content: "Hello" }],
    max_tokens: 1024
  })
});
 
// After: Switch to Claude 3.5 Haiku based on Narev results
const command = new InvokeModelCommand({
  modelId: "anthropic.claude-3-5-haiku-20241022-v1:0", // ← Tested winner
  body: JSON.stringify({
    anthropic_version: "bedrock-2023-05-31",
    messages: [{ role: "user", content: "Hello" }],
    max_tokens: 1024
  })
});

Step 5: Monitor and Iterate

AWS CloudWatch will show you the real-world performance and costs. Use Narev to:

• Test new models as they're added to Bedrock
• Experiment with prompt variations
• Validate cross-region model performance
• A/B test temperature and parameter changes

Why Test Before Deploying to AWS Bedrock?

AWS Bedrock Features Narev Helps You Optimize

1. Model Selection

AWS Bedrock gives you: Access to Claude, Llama, Titan, Command, Jurassic, and more
Narev tells you: Which model actually works best for your use case

2. Regional Deployment

AWS Bedrock gives you: Models available in multiple AWS regions
Narev tells you: Which models provide optimal latency and quality for your workload

3. Provisioned Throughput

AWS Bedrock gives you: Option for provisioned throughput vs on-demand
Narev tells you: Whether you can use cheaper on-demand models and still meet SLAs

4. Model Versioning

AWS Bedrock gives you: Multiple versions of each model
Narev tells you: Whether newer versions improve quality or if older versions suffice

5. Cost Management

AWS Bedrock gives you: CloudWatch cost tracking
Narev tells you: How to reduce those costs by 50-99% without sacrificing quality

Common AWS Bedrock + Narev Use Cases

Pricing: Narev + AWS Bedrock

AWS Bedrock pricing: Pay-per-use based on input/output tokens (varies by model)
Narev pricing: Free for experimentation, no fees on top of your model costs

Combined value: Test $1 worth of prompts in Narev to validate a configuration that saves $10,000/month in AWS Bedrock costs.

Getting Started

Frequently Asked Questions

Start Optimizing Your AWS Bedrock Costs Today

Stop guessing which models to use. Start testing systematically.