Why We Built Narev AI - Two Futures for AI Economics

Two Possible Futures

I've been thinking about this a lot lately. There's a version of the future where AI costs plummet so dramatically that none of this matters. Where running GPT-4 on every single interaction becomes as cheap as a database query today. Where the cost-speed-quality tradeoff disappears because everything is fast, cheap, and good enough.

I hope that future happens. It would be incredible.

But there's another future - one that feels more realistic to me right now. Where AI capabilities keep expanding, but so do our ambitions for what we want to build with them. Where new models are more powerful but also more expensive. Where the tradeoffs don't disappear, they just get more nuanced.

We built NarevAI for the second future.

The Optimization Reality

Even if costs drop 10x, we'll probably want to do 100x more with AI. The fundamental question remains: which tool for which job?

Do you really need Claude Sonnet for summarizing internal emails? Should your customer support chatbot use the same model as your code generation tool? When does the latency hit from a more powerful model actually hurt user experience more than the quality boost helps?

These aren't just cost questions - they're product questions. And right now, most teams are flying blind.

Why FOCUS 1.2 Matters

Here's what drove me to tackle this: AI costs don't fit into any existing framework. They're not like cloud infrastructure costs. They're not like traditional software licenses. They're something entirely new.

FOCUS 1.2 gives us a language to talk about AI costs the same way we talk about cloud costs. It standardizes the chaos so we can actually make informed decisions instead of just hoping for the best.

When you can see your token usage patterns, your model performance correlations, and your actual cost-per-outcome metrics in a standardized format, you start making different choices. Better choices.

The Missing Dashboard

Every AI team I talk to has some version of the same spreadsheet. Monthly costs broken down by provider, maybe some rough token counts, definitely a lot of manual work to generate.

What's missing is the real-time feedback loop. The ability to see immediately how a model change affects your cost structure. The data to answer questions like "is this latency optimization worth the 15% cost increase?" or "which of our use cases are actually driving 80% of our AI spend?"

That's the dashboard we wanted to exist. So we built it.

Self-Hosted by Design

We made NarevAI self-hosted because AI usage data is sensitive. Your model choices, your token patterns, your optimization strategies - this stuff reveals how you're building your competitive advantages.

But we also didn't want self-hosted to mean "good luck figuring it out yourself." The platform needs to work out of the box, integrate with your existing tools, and provide insights that actually change how you build.

Building for Both Futures

Here's the thing: if AI costs do plummet and optimization becomes irrelevant, NarevAI becomes a nice historical artifact. No harm done.

But if the optimization future is the real one - if choosing the right AI tool for each job becomes a core competitive advantage - then having the data infrastructure in place early makes all the difference.

I'd rather build the tools for thoughtful AI optimization and not need them than need them and not have them.

The future of AI isn't just about better models. It's about better decisions about when and how to use them.

Want to see how your AI spend breaks down? Check out NarevAI and start making those decisions with data instead of hope.