Narev AI
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What is Narev?

What is Narev?

Run rapid A/B tests on your LLM applications. Compare models, prompts, and parameters to find the optimal balance of cost, quality, and speed.

Most teams overspend on LLMs by 40-60% - afraid cheaper models will tank quality. Narev proves which models actually work for your use case. Test systematically, measure cost vs. quality tradeoffs, and deploy only configurations backed by data.

Why Narev exists?

⚠️ The problem most teams face

Most companies are overspending on LLMs by 40-60% because they're afraid to test cheaper alternatives.

  • Fear-driven spending - Stick with expensive flagship models "just to be safe"
  • Blind optimization - Swap to cheaper models and pray quality doesn't tank
  • DIY paralysis - Spend weeks building custom testing infrastructure

✨ The Narev approach

LLMs are black boxes—but we already know how to optimize them through controlled experimentation.

  • Centralize your spending - Consolidate LLM costs and attribute them to teams, tags, and applications

  • Apply proven methodology - Use A/B testing, the same approach that works for SEO, pricing, and marketing

  • Build systematically - Test rigorously. Measure ruthlessly. Deploy winners.

Narev brings rigor to LLM optimization

📊 Multi-dimensional tradeoff analysis

Don't optimize for cost alone. See exactly how each configuration balances cost, quality and latency. The best configurations cluster in the sweet spot.

👁️ Side-by-side comparison Review actual model outputs, not just scores. One model might be wordier. Another

faster. A third might nail your format requirements.

🔬 Scientific iteration Found a promising configuration? Duplicate it, change one variable, and run another

experiment. Know exactly what drove improvement.

🔌 Works with your existing stack

No rewrite required. Integrate via tracing import (Langfuse, Helicone, W&B Weave, LangSmith), API proxy, or simple file import. Start testing in minutes.

Who uses Narev?

Early-stage startups

Build efficiently from day one and stand out from other AI startups with strong unit economics and margins.

Growing products

LLM costs are scaling with usage. Find better and cheaper configurations before unit economics break.

Enterprise teams

Multiple applications, fragmented spend. Optimize systematically across teams.

Run your first experiment in under 10 minutes

Create an application and import your prompts

Start by creating your first application. Import prompts via file upload, manual entry, or connect your existing tracing tools.

Design an experiment with 2+ variants Each variant tests a different combination of model, prompt template, and parameters. Compare GPT-4 vs Claude vs cheaper alternatives.

Run and analyze results Execute your experiment and review the results. See cost, quality, and latency tradeoffs in multi-dimensional charts.

Deploy the winning configuration

Found a winner? Use Narev's API proxy to route production traffic to the best-performing configuration.

Run your first experiment

Follow our step-by-step guide to start optimizing your LLM costs.

How Narev fits your stack?