Integrate Narev with Langfuse for LLM Cost Optimization

The Problem with Observability Alone

Langfuse 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

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

When Langfuse 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 + Langfuse Work Together

Narev and Langfuse are the perfect pairing for LLM optimization:

The workflow:

1. Monitor production with Langfuse to identify optimization opportunities
2. Import traces from Langfuse 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 Langfuse and repeat

Integration Guide

Step 1: Export Production Traces from Langfuse

Narev integrates directly with Langfuse 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 Langfuse:

1. In Narev, go to Import Traces

2. Select Langfuse as your provider

3. Enter your Langfuse project credentials:

   • Project Name: Your Langfuse project identifier
   • Secret Key: Your Langfuse secret key (sk-lf-...)
   • Public Key: Your Langfuse public key (pk-lf-...)
   • Host: Your Langfuse instance URL (default: https://cloud.langfuse.com)

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 Langfuse to spot areas where optimization would have the biggest impact:

Step 3: Create Experiments with Real Production Data

Let's say Langfuse 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 Langfuse 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 Langfuse 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 Langfuse traces
import { anthropic } from '@ai-sdk/anthropic';
import { generateText } from 'ai';
 
const result = await generateText({
  model: anthropic('claude-3-5-haiku-20241022'), // ← Old model
  prompt: userMessage,
});
 
// After: Switch to validated alternative
import { openai } from '@ai-sdk/openai';
 
const result = await generateText({
  model: openai('gpt-4o-mini'), // ← Tested winner
  prompt: userMessage,
});

Monitor the impact in Langfuse:

• Cost reduction appears immediately in your Langfuse 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 Langfuse 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 Langfuse?

The Langfuse → Narev → Production Loop

Common Langfuse + 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.