Integrate Narev with Helicone for LLM Cost Optimization

The Problem with Observability Alone

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

• Which requests are most expensive
• Where latency bottlenecks occur
• Which models you're using and how often
• Total costs broken down by user, session, or endpoint
• Detailed request and response metrics

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

When Helicone 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 + Helicone Work Together

Narev and Helicone are the perfect pairing for LLM optimization:

The workflow:

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

Integration Guide

Step 1: Export Production Traces from Helicone

Narev integrates directly with Helicone 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 Helicone:

1. In Narev, go to Import Traces

2. Select Helicone as your provider

3. Enter your Helicone project credentials:

   • Project Name: Your Helicone project identifier
   • API Key: Your Helicone API key (starts with sk-helicone-...)

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

Step 3: Create Experiments with Real Production Data

Let's say Helicone shows you're spending heavily on a content generation 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 Helicone 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 Helicone 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 Helicone traces
import Anthropic from "@anthropic-ai/sdk";
 
const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
  baseURL: "https://anthropic.helicone.ai",
  defaultHeaders: {
    "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,
  },
});
 
const response = await anthropic.messages.create({
  model: "claude-3-5-haiku-20241022", // ← Old model
  messages: [{ role: "user", content: userMessage }],
});
 
// After: Switch to validated alternative
import OpenAI from "openai";
 
const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://oai.helicone.ai/v1",
  defaultHeaders: {
    "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,
  },
});
 
const response = await openai.chat.completions.create({
  model: "gpt-4o-mini", // ← Tested winner
  messages: [{ role: "user", content: userMessage }],
});

Monitor the impact in Helicone:

• Cost reduction appears immediately in your Helicone 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 Helicone 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 Helicone?

The Helicone → Narev → Production Loop

Common Helicone + Narev Use Cases

Real Example: Chat Application Optimization

Scenario: Helicone dashboard shows your chat application costs $22,000/month using GPT-4 for all conversations.

Step 1: Export traces from Helicone for your chat sessions
Step 2: Import 30 days of traces to Narev (5,124 conversations)
Step 3: Create experiment:

• Variant A: gpt-4-turbo for all responses (current)
• Variant B: gpt-4o-mini for simple queries + gpt-4-turbo for complex ones
• Variant C: gpt-4o-mini for all responses

Results after 20-minute experiment:

Decision: Deploy smart routing (classify complexity, then route appropriately)
Savings: $17,300/month (79% reduction)
Quality impact: -1% (acceptable)
User experience: 19% faster responses

Frequently Asked Questions

Getting Started

Start Optimizing Today

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