You look at your provider dashboard and see one number: the total bill. It's like getting an electricity bill that just says "$5,000" with no breakdown of whether it was the AC, the fridge, or someone leaving the lights on all month.

tbh, most AI startups are flying blind right now. We recently looked into the cost breakdown for several teams and found something crazy: almost 43% of LLM API spend is completely wasted. It’s not about paying for usage; it’s about paying for bad architecture.

Here’s where the leaks are actually happening:

1. Retry Storms (34% of waste)
   Your agent fails to parse a JSON response, so it retries. And retries. Sometimes 5-10 times in a loop. You aren't just paying for the failure, you are paying for the massive context window sent every single time.

2. Duplicate Calls (85% of apps have this issue)
   Multiple users asking the exact same question, or internal systems running the same RAG pipeline on the same document. Without caching at the provider level, you're paying OpenAI to generate the identical tokens twice.

3. Context Bloat
   Sending the entire 50-page document history when the user just asked "what's the summary of page 2". RAG is great, but shoving everything into the prompt "just in case" is burning your runway.

4. Wrong Model Selection
   Using GPT-4o or Claude 3 Opus for simple classification tasks when Haiku or GPT-3.5-turbo would do it for a fraction of the cost.

You can't fix what you can't see. That's exactly why I built LLMeter (https://llmeter.org?utm_source=devto&utm_medium=article&utm_campaign=hidden-43-percent-llm-waste). It's an open-source dashboard that gives you per-customer and per-model cost tracking. Stop guessing who or what is draining your API budget.

Fwiw, just setting up basic budget alerts and seeing the breakdown by tenant usually drops a team's bill by 20% in the first week. Give it a try, it's open source (AGPL-3.0) and you can self-host or use the free tier.