# Cheaper & open models / self-hosting

> Cut My AI Spend — rank #9 of 10. Typical savings: 50–95% per token on suitable tasks. Effort: High for self-hosting, low for switching.

Frontier-model prices keep falling, and small models (Haiku-class, GPT-mini-class, Nova, open Llama/Qwen/Mistral weights) now handle classification, extraction, and routine drafting at a tiny fraction of frontier price. For high-volume, well-scoped tasks, a fine-tuned small model regularly beats a prompted frontier model on cost and matches it on quality.
Self-hosting open weights (with quantization) makes sense past sustained volume thresholds — but be honest about GPU, ops, and eval costs; the API price war means the crossover point is higher than most teams assume.

## How to do it

1. Benchmark your top-volume tasks on one tier down (and two tiers down) from your current model.
2. Fine-tune a small model on tasks with clear ground truth and high volume.
3. For self-hosting, price the full picture: GPUs, autoscaling headroom, ops time, and eval maintenance.
4. Re-benchmark quarterly — model prices and quality shift fast enough to change the answer.

## Frequently asked questions

### When does self-hosting pay off?

Rules of thumb vary, but sustained six-figure annual API spend on stable workloads is where serious evaluation starts. Below that, falling API prices usually beat owning GPUs.


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