AI & Automation
Running an LLM Locally vs Paying for an API
The real trade-off isn't cost per token — it's control, latency, and what you're actually optimizing for.
"Local is cheaper" and "APIs are cheaper" are both true and both useless without the workload. Here's the framework I actually use to decide.
The three axes that matter
Forget cost-per-token as the headline. Decide on:
- Control — data residency, no vendor lock, offline capability.
- Latency — local can be lower for small models, higher for big ones.
- Total cost — hardware amortization vs metered usage at your volume.
When local wins
break-even ≈ (GPU + power + ops) / (api_price_per_call × calls_per_month)
If you're running millions of small, predictable calls — classification, extraction, embeddings — a box you own amortizes fast. And you get privacy for free.
The hidden cost of local
Ops. A local model is a server you now babysit — drivers, quantization, uptime. That's real engineering time the API price already includes.
When the API wins
Frontier reasoning, spiky traffic, and small teams. You are renting a capability that would cost millions to reproduce, and paying only when you use it.
How it works
- WORKLOAD
- PREDICTABLE & PRIVATE? → LOCAL
- SPIKY & FRONTIER? → API
- MEASURE REAL COST
- REVISIT QUARTERLY
Key takeaway
Run the boring, high-volume, privacy-sensitive work locally. Rent frontier reasoning from an API. Most serious setups end up hybrid — and the split shifts as models get cheaper and hardware gets faster.