Step 1: What Is Local Deployment? Local LLMs vs Cloud APIs in Plain Terms

Updated 2026-09-11 · Verified 2026-09-11

Two ways to use an LLM

Cloud API: you hit enter, your prompt travels across the network to a provider’s data center, gets answered there, and comes back. You pay per use — usually per token (the chunks of text a model reads; roughly three-quarters of an English word).

Local deployment: you download an open-weight model file (a few GB to tens of GB) onto your own machine, and your own GPU does the math. Once it’s set up, nothing passes through anyone else’s server.

You can try it with a single command. Install Ollama — a free tool for running models locally — from ollama.com, then open a terminal and type:

ollama run llama3.1:8b

It downloads an 8-billion-parameter model (~4.9GB) and starts a chat. If it answers, you’ve just done your first local deployment.

Day to day, the setup stays simple: Ollama sits in the background (a small menu-bar icon), and you chat through its window or the terminal. It also exposes a local API, which means your other software — note-taking apps, translators, editor plugins — can call the model running on your own machine directly.

The four differences

Privacy: your data never leaves the machine. With an API, every prompt — contracts, medical notes, private code, unpublished drafts — goes to the provider. Locally, data travels from keyboard to GPU to screen, entirely inside your own hardware. There’s a simple proof: unplug the network. If the model still answers, nothing was being sent out.

Cost: the more you use it, the more local saves. An API is a taxi — cheap for the occasional ride, painful as a daily commute. Local is buying the car: hardware money up front, then the marginal cost per use is just electricity — a 350W GPU at 50% average load costs roughly $19 a month (at $0.15/kWh). So the answer depends on volume. Take Llama 3.1 8B: its cheapest hosted APIs run about $0.03 per million tokens, so 10 million tokens a month costs pocket change, and buying hardware is pointless. But if you generate heavily every day — batch-summarizing documents, running automation scripts — the API bill climbs until hardware amortization plus electricity undercuts it. There is no universal answer; you have to run your own numbers, which is exactly what the comparator below does.

Offline availability. Planes, corporate intranets, flaky Wi-Fi, provider outages — a local model is immune to all of them. The file is on your disk, the power is in the wall, nothing else is required.

Full control. API models get upgraded, repriced, and behavior-tuned without asking you; the same prompt can answer differently next month. A local model file is a version you pinned — reproducible forever. You can change the system prompt, tune the parameters, even fine-tune the model, with no platform rules in the way.

When you should just use the API

Local deployment is a tool, not a religion. Two cases where the API honestly wins:

• Occasional use. At low volume, your monthly API bill may be a few dollars — less than a GPU’s electricity. Buying hardware is pure waste.
• Chasing the frontier. The strongest closed models (GPT-5, Claude class) have no open weights; they cannot run locally at all. Open models are catching up, but the ceiling is still in the cloud.

Many people end up hybrid: sensitive and bulk work runs local, the hardest questions go to the API.

What if my computer is modest?

It still works, with trade-offs. A model needs to fit in GPU memory to run fast; if it doesn’t, you can “offload” part of it to system RAM — at a brutal speed penalty. Whether that trade is worth it, in depth: CPU Offload: Is Partial Offloading Worth It?. How much memory you actually need, and why it comes first, is the subject of the next step.

Enough concepts — now run your own numbers: fill your usage, electricity price, and budget into the comparator below and let the math decide.