Integrate Narev with LiteLLM for LLM Cost Optimization

The Problem with LiteLLM Alone

LiteLLM is an excellent unified interface—it lets you call any LLM provider with OpenAI-compatible syntax and provides essential features like load balancing, fallbacks, and cost tracking. But that flexibility creates a new challenge: too many options.

With 100+ models accessible through the same interface, 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 + LiteLLM Work Together

Narev and LiteLLM complement each other perfectly:

The workflow:

1. Export production logs from LiteLLM (proxy logs or application traces)
2. Test alternative configurations in Narev with A/B experiments
3. Deploy winners to LiteLLM with confidence
4. Monitor results and repeat continuously

Integration Guide

Step 1: Export Your LiteLLM Usage Data

Narev works with your existing LiteLLM logs to create realistic test scenarios. If you're using LiteLLM Proxy, you can export request logs. If you're using the library directly, export your prompts and responses to build experiments that reflect your actual production workload.

Step 2: Create Your First Experiment

Let's say you're currently using gpt-4o-mini through LiteLLM and want to explore if Claude 3.5 Haiku offers better performance.

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 LiteLLM Configuration

With data-backed confidence, update your LiteLLM integration:

Option A: Using LiteLLM Library

# Before: Using GPT-4o-Mini
from litellm import completion
 
response = completion(
    model="gpt-4o-mini",  # ← Old default
    messages=[{"role": "user", "content": "Hello"}]
)
 
# After: Switch to Claude 3.5 Haiku based on Narev results
response = completion(
    model="claude-3-5-haiku-20241022",  # ← Tested winner
    messages=[{"role": "user", "content": "Hello"}]
)

Option B: Using LiteLLM Proxy Config

# Before: config.yaml
model_list:
  - model_name: gpt-4o-mini
    litellm_params:
      model: gpt-4o-mini
      api_key: os.environ/OPENAI_API_KEY
 
# After: Update based on Narev results
model_list:
  - model_name: claude-3-5-haiku
    litellm_params:
      model: claude-3-5-haiku-20241022
      api_key: os.environ/ANTHROPIC_API_KEY

Step 5: Monitor and Iterate

LiteLLM's built-in cost tracking will show you the real-world savings. Use Narev to:

• Test new models as they're added to LiteLLM's supported list
• Experiment with prompt variations
• Validate load balancing and fallback configurations
• A/B test temperature and parameter changes

Why Test Before Deploying to LiteLLM?

LiteLLM Features Narev Helps You Optimize

1. Model Selection

LiteLLM gives you: Access to 100+ models through unified interface
Narev tells you: Which model actually works best for your use case

2. Load Balancing

LiteLLM gives you: Round-robin and weighted load balancing
Narev tells you: Which models to include in your load balancer and at what weights

3. Fallback Configuration

LiteLLM gives you: Automatic fallback when models fail
Narev tells you: Which fallback models maintain quality without breaking budget

4. Cost Tracking

LiteLLM gives you: Automatic cost calculation per request
Narev tells you: How to reduce those costs by 50-99% without sacrificing quality

5. Router Optimization

LiteLLM gives you: Smart routing based on latency or cost
Narev tells you: Optimal routing strategy based on actual quality metrics

Common LiteLLM + Narev Use Cases

Pricing: Narev + LiteLLM

LiteLLM pricing: Open source and free (proxy also free for self-hosting)
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 LiteLLM production costs.

Getting Started

Frequently Asked Questions

Start Optimizing Your LiteLLM Costs Today

Stop guessing which models to use. Start testing systematically.