Integrate Narev with Weights & Biases Weave for LLM Cost Optimization

The Problem with Observability Alone

Weights & Biases Weave is an excellent LLM observability platform—it gives you complete visibility into your production LLM usage. You can see exactly:

• Which prompts are most expensive
• Where latency bottlenecks occur
• Which models you're using and how often
• Total costs broken down by endpoint, user, or feature
• Detailed performance metrics and token usage

But observability alone doesn't solve the problem. Seeing the problem isn't the same as fixing it.

When Weave shows you're spending $10,000/month on GPT-4, you're left wondering:

• Can I switch to a cheaper model without breaking quality?
• Which of the 400+ available models would work for my specific use case?
• Will GPT-4o Mini handle my prompts as well as GPT-4?
• Should I adjust my prompts or change models?

The result? Teams have full observability but still overspend by 10-100x because they lack a systematic way to test alternatives.

How Narev + W&B Weave Work Together

Narev and W&B Weave are the perfect pairing for LLM optimization:

The workflow:

1. Monitor production with W&B Weave to identify optimization opportunities
2. Import traces from Weave into Narev
3. Test alternative models, prompts, and parameters with A/B experiments
4. Deploy validated optimizations to production with confidence
5. Verify improvements in Weave and repeat

Integration Guide

Step 1: Export Production Traces from W&B Weave

Narev integrates directly with W&B Weave to import your production traces. These traces become the test dataset for your experiments—ensuring you're testing against real-world usage patterns.

To connect W&B Weave:

1. In Narev, go to Import Traces

2. Select W&B Weave as your provider

3. Enter your W&B Weave project credentials:

   • Entity: Your W&B entity (username or team name)
   • Project Name: Your Weave project identifier
   • API Key: Your W&B API key (starts with your-api-key)

4. Select your date range (default: last 7 days)

5. Click Save Project to import traces

Narev will import your prompts, model configurations, and usage patterns to create realistic test scenarios.

Step 2: Identify Optimization Opportunities

Use W&B Weave to spot areas where optimization would have the biggest impact:

Step 3: Create Experiments with Real Production Data

Let's say Weave shows you're spending heavily on a customer support feature using GPT-4. Import those traces to Narev and test alternatives:

Create an experiment comparing:

Narev will run both variants on your actual production prompts from Weave and measure:

• Cost savings in dollars and percentage
• Latency differences (time to first token, total time)
• Quality metrics (accuracy, completeness, formatting)

Step 4: Analyze Results with Statistical Confidence

Narev provides clear, data-backed answers:

Example results:

• ✅ GPT-4o Mini costs 49% less ($18.36 vs $35.85 per 1M requests)
• ✅ Quality improved by 33% (80% vs 60%)
• ✅ Latency improved by 13% (623.4ms vs 713.4ms)

Projected savings: Based on your Weave volume data, switching to GPT-4o Mini reduces costs by nearly 50% while improving both quality and latency.

Step 5: Deploy and Monitor

With validated results, confidently deploy your optimization:

# Before: Current model from Weave traces
import weave
from anthropic import Anthropic
 
client = Anthropic()
 
@weave.op()
def generate_response(message: str) -> str:
    response = client.messages.create(
        model="claude-3-5-haiku-20241022",  # ← Old model
        messages=[{"role": "user", "content": message}],
        max_tokens=1024
    )
    return response.content[0].text
 
# After: Switch to validated alternative
from openai import OpenAI
 
client = OpenAI()
 
@weave.op()
def generate_response(message: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o-mini",  # ← Tested winner
        messages=[{"role": "user", "content": message}]
    )
    return response.choices[0].message.content

Monitor the impact in W&B Weave:

• Cost reduction appears immediately in your Weave dashboards
• Track quality through user feedback and error rates
• Compare before/after metrics to validate experiment predictions

Step 6: Continuous Optimization

Use this workflow continuously:

1. Weekly: Review Weave for new optimization opportunities
2. Test: Import the highest-cost traces into Narev
3. Validate: Run experiments on new models or prompt variations
4. Deploy: Roll out proven optimizations
5. Repeat: As new models launch or usage patterns change

Why Import from W&B Weave?

The W&B Weave → Narev → Production Loop

Common W&B Weave + Narev Use Cases

Frequently Asked Questions

Getting Started

Start Optimizing Today

Stop wondering if you can reduce costs. Start testing systematically with your real production data.