Every team that adds AI to a product or workflow eventually faces the same choice: send requests to a hosted model through an API, or run an open-weight model on hardware you control. Neither is universally better. The right answer depends on how much you use the model, how sensitive your data is, how good the answers need to be and how much engineering effort you can spare. This comparison walks through the five dimensions that usually decide it.

The two options in brief

A cloud API means a provider hosts the model and you pay per use, typically per million input and output tokens, or through a subscription. You get the provider’s best models, no hardware to manage and fast scaling. A local model means you download open weights and run them on your own laptop, workstation or server, using a runtime such as Ollama, which the project describes as a way to access open models locally or in the cloud, with installers for macOS and Windows, a Docker image and a local API. You manage the hardware and the model, but your data stays with you.

There is also a middle path: open-weight models hosted by a third party or in your own cloud account, which gives you control of the model with someone else’s hardware.

Cost

Cloud APIs have almost no upfront cost and a variable bill that grows with usage. Local models have the reverse shape: a fixed hardware cost, plus electricity and maintenance, and then near-zero marginal cost per request.

A simple break-even example shows the logic. These numbers are illustrative, not quotes from any provider. Suppose a workflow uses 100 million tokens a month and the API price blends to $5 per million tokens, so the bill is $500 a month. A workstation with a capable GPU that costs $3,000 would pay for itself in about six months if it could handle the whole load at acceptable quality, ignoring electricity and your time. If the workflow uses only 5 million tokens a month, the API costs $25 and the workstation never pays back. The break-even point moves with your actual volume, the price of the model you would otherwise use and how much of your traffic a smaller local model can handle. Our guide to cutting your API bill covers caching and routing, which can shift the comparison substantially before you buy hardware.

Privacy and control

This is the strongest argument for local models. With a local setup, prompts and documents never leave your machine, which simplifies compliance for sensitive data such as medical notes, legal files or unreleased source code. You also control the model version: it will not change or be retired without notice, a risk covered in our piece on model deprecations.

Cloud providers offer contractual protections, such as data processing agreements and options not to train on your data, and many organizations are comfortable with those. But you are trusting a third party, and you need to read the terms. If you handle data that cannot legally or contractually leave your environment, local or private hosting may not be optional.

Speed and scale

Latency depends on both hardware and distance. A hosted model on data-center GPUs usually generates text faster than a laptop can, and can serve thousands of parallel users. A local model avoids the network round trip and works offline, which helps for interactive tools and edge devices, but a consumer machine runs only a modest-sized model at modest speed and handles few simultaneous requests. For a rough sense of what fits, see our GPU memory sizing guide. Techniques such as quantization shrink models so they fit on smaller hardware, at some cost in quality.

Quality

Frontier hosted models are generally stronger than what fits on a laptop, especially on complex reasoning, long documents and difficult coding tasks. Open-weight models have improved, and for focused tasks such as classification, extraction, summarization of short texts and drafting, a well-chosen smaller model is often good enough. The only reliable way to know is to test both on your own examples: build a small evaluation set of real inputs, run each option and compare. Do not rely on leaderboards alone, as we explain in why benchmark scores don’t pick your model.

Effort and reliability

A hosted API is a few lines of code and a key. Running your own model means installing software, choosing and updating models, monitoring memory and speed, and handling failures. For a hobbyist this is part of the fun; for a company it is an ongoing cost in engineer time. Cloud APIs bring their own operational risks, such as rate limits, outages and price or policy changes, which you handle by having a fallback.

A quick decision guide

  • Choose a cloud API when quality matters most, your volume is modest or unpredictable, you want to ship quickly and your data is not highly restricted.
  • Choose a local model when data cannot leave your control, usage is high and steady, you need offline operation or you want a pinned, unchanging model.
  • Use both by routing easy or sensitive requests to a local model and hard ones to a hosted model, with clear rules about what data may be sent where.

If you want to try local, our step-by-step guide to running a local LLM with Ollama takes about twenty minutes.

What to watch next

  • Smaller open-weight models closing the quality gap on everyday tasks.
  • New laptops and desktops with more memory and dedicated AI hardware.
  • Hosted providers adding private, single-tenant or on-premises options.
  • Changes in API pricing that move the break-even point.

Key takeaways

  • Cloud APIs win on quality, scale and setup speed; local models win on privacy, control and cost at high steady volume.
  • Break-even depends on your token volume, the model you would otherwise use and hardware costs.
  • Always test both options on your own data.
  • A hybrid setup often gives the best balance.
  • Factor in engineering time, not just hardware and API bills.

Frequently asked questions

Is running a model locally always cheaper?

No. At low usage a hosted API is usually cheaper, because you avoid buying hardware. Local becomes attractive at high, steady volume.

Are local models private by default?

Prompts stay on your machine, but you still need to secure the device and any tools connected to the model.

Can a laptop run a useful model?

Yes, for many focused tasks. Capability depends on your memory and the model size; larger tasks usually need a hosted or more powerful machine.