LLM Inference Cost Optimization: Run Any Model for Pennies
LLM inference cost optimization guide. Compare Ollama, vLLM, llama.cpp quantization. Reduce API costs by 90%+. 3 benchmarks, 6 deployment methods.
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- Updated 2026-06-16
The first time I saw an OpenAI API bill for $47.32, I stared at my screen for a full minute. Not because it was a lot of money. But because I had been running experiments for 4 hours on a $20/month GPU that I found on a discount deal.
That’s when I realized: we’re all paying too much for LLM inference.
Every developer who’s used ChatGPT API or Claude API has felt this pain. The per-token pricing looks reasonable — until you actually use it. Then the numbers add up fast.
This is not a tutorial. This is what I learned after testing every major inference engine for 3 months, measuring actual costs, and building a comparison that doesn’t rely on benchmarks from the companies selling you the solution.
Get a DigitalOcean account for running this at scaleThe Real Cost of LLM Inference (Not What Companies Tell You) #
Let’s be honest about pricing. Here’s what you actually pay per million tokens for the most common models:
| Model | Input ($/M tokens) | Output ($/M tokens) | Cost per 1K tokens | |
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