Integrate Narev with OpenRouter for LLM Cost Optimization
Use Narev to test and validate model configurations before deploying to OpenRouter. Reduce LLM costs by 99% while maintaining quality through systematic A/B testing.
OpenRouter routes your traffic. Narev tells you what to route. OpenRouter gives you access to 400+ models through a single API. But which models should you use? What's the actual cost difference? Will quality suffer if you switch? Narev answers these questions before you change production.
The Problem with OpenRouter Alone
OpenRouter is an excellent LLM gateway—it makes switching between models as simple as changing a configuration. But that simplicity creates a new challenge: decision paralysis.
With 400+ models to choose from, teams often:
- Stick with expensive defaults (GPT-4) 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 + OpenRouter Work Together
Narev and OpenRouter complement each other perfectly:
Tool | Purpose | When You Use It |
---|---|---|
Narev | Test models systematically to find optimal configuration | Before changing production |
OpenRouter | Route production traffic to chosen models | In production, after testing |
The workflow:
- Export production traces from OpenRouter
- Test alternative configurations in Narev with A/B experiments
- Deploy winners to OpenRouter with confidence
- Monitor results and repeat continuously
Integration Guide
Step 1: Export Your OpenRouter Usage Data
Narev works with your existing OpenRouter logs to create realistic test scenarios. You can use your recent prompts, model selections, and response patterns to build experiments that reflect your actual production workload.
Step 2: Create Your First Experiment
Let's say you're currently using openai/gpt-4o-mini
through OpenRouter and want to explore if Claude 3.5 Haiku offers better performance.
Create an experiment in Narev testing:
Variant A (Baseline)
anthropic/claude-3-5-haiku-20241022
Variant B
openai/gpt-4o-mini
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 OpenRouter Configuration
With data-backed confidence, update your OpenRouter integration:
// Before: Using GPT-4o-Mini
const client = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
});
const response = await client.chat.completions.create({
model: "openai/gpt-4o-mini", // ← Old default
messages: [...],
});
// After: Switch to Claude 3.5 Haiku based on Narev results
const response = await client.chat.completions.create({
model: "anthropic/claude-3-5-haiku-20241022", // ← Tested winner
messages: [...],
});
Step 5: Monitor and Iterate
OpenRouter's analytics will show you the real-world performance. Use Narev to:
- Test new models as they're added to OpenRouter
- Experiment with prompt variations
- Validate routing strategies before deploying them
- A/B test temperature and parameter changes
Why Test Before Deploying to OpenRouter?
Without Narev: Risky Approach
- "Should we try Claude instead of GPT-4?"
- Deploy directly to OpenRouter production
- Hope quality doesn't drop
- Wait days/weeks for enough data
- Quality issues surface → rollback
- Lost time + degraded user experience 💸
With Narev: Data-Driven Approach
- "Should we try Claude instead of GPT-4?"
- Test in Narev with production-like prompts
- Get results in minutes with statistical confidence
- Deploy winner to OpenRouter ✅
- Monitor with confidence
- Realize savings immediately 💰
OpenRouter Features Narev Helps You Optimize
1. Model Selection
OpenRouter gives you: 400+ model options
Narev tells you: Which one actually works best for your use case
2. Provider Choice
OpenRouter gives you: Same model from multiple providers
Narev tells you: Which provider offers better latency or uptime for your region
3. Fallback Configuration
OpenRouter gives you: Automatic fallback routing
Narev tells you: Which fallback models maintain quality without breaking budget
4. Cost Management
OpenRouter gives you: Per-request cost tracking
Narev tells you: How to reduce those costs by 50-99% without sacrificing quality
Common OpenRouter + Narev Use Cases
🎯 Model Migration
Test whether switching from GPT-4 to Claude-3.5 or GPT-4o-mini maintains quality for your specific prompts before updating OpenRouter config
⚡ Latency Optimization
Compare the same model across different providers in OpenRouter to find the fastest option for your users' regions
💰 Cost Reduction
Systematically test cheaper alternatives to expensive defaults and validate they meet your quality bar
🔧 Parameter Tuning
A/B test temperature, max_tokens, and other parameters to optimize OpenRouter responses for cost and quality
Pricing: Narev + OpenRouter
OpenRouter pricing: Pay-per-use based on model chosen (no markup)
Narev pricing: Free for experimentation, no fees on top of your OpenRouter costs
Combined value: Test $1 worth of prompts in Narev to validate a configuration that saves $10,000/month in OpenRouter production costs.
Getting Started
Step 1: Sign Up for Narev
Sign up - no credit card required.
Step 2: Connect Your OpenRouter Account
Import your existing prompts, connect your tracing platform or start with sample data to create your first experiment.
Step 3: Run Your First Test
Compare your current OpenRouter model against 2-3 alternatives. Results in minutes.
Step 4: Deploy Winners
Update your OpenRouter configuration with confidence based on real data.
Frequently Asked Questions
Start Optimizing Your OpenRouter Costs Today
Stop guessing which models to use. Start testing systematically.
Next Steps: - Read the 3-Step FinOps Framework for AI - See how to reduce costs by 99% by switching models - See how to reduce costs by 24% by prompt engineering