I've been running large language models locally for a few months now, and honestly? I'm not going back to API-only workflows.

The Cost Math

Cloud AI APIs charge per token. If you're doing anything beyond the occasional "summarize this email," it gets expensive fast. I was burning $200/month on API calls. That's $2,400 a year for something my own hardware can handle.

My workstation runs a 60-billion parameter model at 60 tokens per second. Not a toy. That's real, production-grade code generation and analysis. The hardware paid for itself inside a year.

Privacy Is Non-Negotiable

Cloud APIs mean trusting someone else with your data. For side projects, fine. For client work or business strategy? No thanks. I'd rather keep it on my own metal.

Local inference means nothing leaves my network. No shifting terms of service, no data retention surprises, no third-party breach putting my clients at risk.

The Quality Gap Is Closing

A year ago, local models were noticeably worse. Today the gap is maybe 10-15% for most tasks. For code generation specifically, open-source models with good fine-tuning produce output that's basically indistinguishable from the top cloud providers.

I fine-tuned a model on my own codebase. Go handlers, Docker configs, IaC templates. The result writes code that looks like mine. Same patterns, same error handling, same abstractions. A cloud API would need pages of prompting to get close.

The Setup Isn't That Hard

The tooling has come a long way. Download a model, point an inference server at it, done. No Kubernetes cluster, no GPU drivers, no CUDA headaches. Apple Silicon makes it especially easy since the CPU and GPU share the same memory pool. A 60GB model just loads and runs.

When I Still Use the Cloud

I'm not a purist about this. Complex reasoning, long-context analysis, anything that needs absolute peak quality still goes to a cloud API. Local handles maybe 65% of my workload: the routine stuff, repetitive code gen, quick analysis. The cloud gets the hard 35%.

That split lets me drop to a lower API tier while still having the best model available for tasks that actually need it. Best of both worlds.

The Bottom Line

If you're spending real money on AI APIs, take a hard look at what you're actually using them for. Most of those calls could probably run locally. Zero marginal cost, faster, better privacy. The frontier models still win on raw reasoning, but for the daily grind? Local is the move.