I Spent 12 Hours Optimizing My LLM Stack. Here's What I Found.

I run a trading agent with five AI models. Last week I pulled my billing data and realized something was wrong.

Claude Opus 4.5 was supposed to cost me $200/month. The actual bill was $73. Why? Pricing had changed without anyone telling me.

And the bigger problem: I was running older models that had already been replaced by better versions at the same price.

The fix took 12 hours of digging through OpenRouter pricing, benchmark leaderboards, and model release notes. I even commissioned six LLMs to cross-check my analysis — two of them made up models that don't exist.

The result: 22% cost reduction + better reasoning quality.

Here's the part that bothered me: none of this happened automatically. No alert. No dashboard. The models just quietly fell behind while my agent kept running.

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The Numbers

One model (Claude Opus) was 96.8% of my bill. The risk manager alone (which runs every 5 minutes) was $50/month of that.

The kicker: it's doing a simple classification task (output one of four fixed recommendations). I was using the most expensive model on the market for something that doesn't need it.

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Why This Matters

Three things silently degraded my setup in three months:

1. Pricing changed - repricing I never knew about
2. New models dropped - better versions at same price (1M context vs 200K)
3. Landscape shifted - Grok 4.1 Fast emerged as a serious contender

Your stack is doing the same thing right now. Not because you made bad decisions, but because the landscape moved and nobody told you.

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What I'm Building

I built ModelOptix because this shouldn't require 12 hours.

Connect your usage data. It tells you when a better model is available for your specific use case (cheaper, faster, or more capable).

Key difference: I'm not paid by model providers. No referral fees. No investors pushing vendors. Subscriptions only, so the only incentive is giving you recommendations that are actually right.

Join the waitlist: modeloptix.com