Integrate Narev with Google Vertex AI for LLM Cost Optimization

The Problem with Vertex AI Alone

Google Vertex AI is an excellent managed service—it provides secure access to Gemini and other foundation models with enterprise features like Google Cloud IAM integration, VPC Service Controls, and Cloud Logging. But that access creates a new challenge: choosing the right model.

With multiple foundation models available (Gemini Pro, Gemini Flash, PaLM 2, Claude on Vertex), 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 + Vertex AI Work Together

Narev and Vertex AI complement each other perfectly:

The workflow:

1. Export production usage data from Cloud Logging or application traces
2. Test alternative model configurations in Narev with A/B experiments
3. Deploy winners to Vertex AI with confidence
4. Monitor results using Cloud Logging and repeat continuously

Integration Guide

Step 1: Export Your Vertex AI Usage Data

Narev works with your existing Vertex AI usage patterns to create realistic test scenarios. Export your recent prompts, model selections, and response patterns from Cloud Logging 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 switching from one provider to another. 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 Vertex AI Configuration

With data-backed confidence, update your Vertex AI integration:

Option A: Using Vertex AI SDK (Python)

# Before: Using Gemini 1.5 Pro
from vertexai.generative_models import GenerativeModel
import vertexai
 
vertexai.init(project="your-project-id", location="us-central1")
 
model = GenerativeModel("gemini-1.5-pro")  # ← Old default
response = model.generate_content("Hello, how are you?")
 
# After: Switch to Gemini 2.0 Flash based on Narev results
model = GenerativeModel("gemini-2.0-flash-exp")  # ← Tested winner
response = model.generate_content("Hello, how are you?")

Option B: Using REST API

// Before: Using Gemini 1.5 Pro
const response = await fetch(
  `https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/publishers/google/models/gemini-1.5-pro:generateContent`,
  {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${accessToken}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      contents: [{ role: 'user', parts: [{ text: 'Hello' }] }]
    })
  }
);
 
// After: Switch to Gemini 2.0 Flash based on Narev results
const response = await fetch(
  `https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/publishers/google/models/gemini-2.0-flash-exp:generateContent`,
  {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${accessToken}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      contents: [{ role: 'user', parts: [{ text: 'Hello' }] }]
    })
  }
);

Step 5: Monitor and Iterate

Cloud Logging and Vertex AI's monitoring dashboard will show you the real-world performance and costs. Use Narev to:

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

Why Test Before Deploying to Vertex AI?

Vertex AI Features Narev Helps You Optimize

1. Model Selection

Vertex AI gives you: Access to Gemini Pro, Flash, PaLM 2, Claude, and Model Garden
Narev tells you: Which model actually works best for your use case

2. Regional Deployment

Vertex AI gives you: Models available in multiple Google Cloud regions
Narev tells you: Which models provide optimal latency and quality for your workload

3. Multimodal Capabilities

Vertex AI gives you: Text, image, video, and audio processing with Gemini
Narev tells you: Which multimodal configuration balances cost and quality

4. Model Versions

Vertex AI gives you: Stable and latest model versions
Narev tells you: Whether experimental versions improve quality enough to justify risks

5. Cost Management

Vertex AI gives you: Cloud Billing cost tracking
Narev tells you: How to reduce those costs by 50-99% without sacrificing quality

Common Vertex AI + Narev Use Cases

Pricing: Narev + Vertex AI

Vertex AI pricing: Pay-per-use based on input/output tokens and characters (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 Vertex AI costs.

Getting Started

Frequently Asked Questions

Start Optimizing Your Vertex AI Costs Today

Stop guessing which models to use. Start testing systematically.