Both of these run open-weight models on your own computer. Both are free for that, without a cap and without an account. So the comparison people actually want is narrower than it looks, and it comes down to three things: what you type into, who can read the code, and what happens if you ever pay.
The short answer
| Ollama | LM Studio | |
|---|---|---|
| Interface | Terminal first | Desktop application |
| Open source | Yes | Not claimed |
| Local use | Free, unlimited, no account | Free, unlimited, no account |
| Works offline | Yes, once a model is pulled | Yes, once a model is downloaded |
| Local API | HTTP, widely targeted | OpenAI compatible |
| Voice | Text only | Transcription, on device |
| Paid tier | Subscription, from 20 dollars a month | Billed per token |
| Operator | Ollama Inc., United States | Element Labs, Inc., United States |
Both were last verified on 8 August 2026.
The interface is the whole decision for most people
Ollama leads with a command. That is an accurate promise rather than a criticism: the terminal is the product, and its own home page shows it. If you would rather script model use than click through a window, this is the one, and the local HTTP API is why so much other local tooling assumes it is installed.
LM Studio leads with an application. It browses models, downloads them, and gives you somewhere to type, with no separate backend to start first. It is the shorter path from nothing to a model answering you.
Nothing else in the two records diverges as sharply as this, and nothing else matters as much to a first attempt.
What the paid tier buys, which is the part usually reported wrongly
Neither product paywalls local use. Both sell hosted inference beside it, and they price it differently.
Ollama Cloud is a subscription. Pro is 20 US dollars a month or 200 a year, Max is 100 a month and was paused for new signups when we checked, and Team is 25 per seat a month with a five seat minimum, carrying a stated zero data retention and no logging position. Its cloud models run in the United States, Europe and Singapore.
LM Studio sells capacity by the token: 0.13 to 3.00 US dollars per million input tokens and 0.26 to 15.00 per million output, depending on model, described as United States based with stated zero data retention.
Read either as a separate hosted service under its own terms, because that is what both are. Choosing one means prompts leaving your machine, which is the exact thing the local mode exists to avoid.
Privacy: the same answer twice
For local use the question mostly dissolves for both. Models run on your hardware, and neither product trains on what you type. Ollama states plainly that your data is never trained on. LM Studio states that no data ever leaves your device, voice transcription included. Both support anonymous use, because neither needs an account at all.
There is no retention period to compare here, and that is the finding rather than a gap in it. A tool holding nothing has no policy to weigh.
The caveat applies to both equally and belongs in the same breath: the moment you opt into either hosted tier, a vendor’s terms apply to everything you send, and those terms are not the local ones.
What neither of them decides
Filtering. Both records show no output filter applied by the software, because a local runtime receives no content to moderate. What a model refuses is a property of the model you pulled, and both catalogues carry heavily aligned models and permissive ones. Switching between these two tools changes the ergonomics and nothing about the output.
Hardware, too. A model has to fit in memory before it can run, so a weak machine makes a good model feel bad on either one. If that is your constraint, the answer is a hosted service such as Venice AI rather than a different local runner.
If you want neither
Jan is the same shape as LM Studio and open source, which is the combination neither tool on this page offers on its own. GPT4All aims at running something on ordinary hardware with nothing to learn first. Open WebUI is server shaped rather than desktop shaped, and expects a backend such as Ollama rather than bundling one, so it pairs with the winner here instead of replacing it.
More comparisons: uncensored AI chat tools and how we verify.