# What is Ollama, and is it safe to run models locally?

Source: https://codelifter.com/forum/posts/what-is-ollama-and-is-it-safe-to-run-models-locally/
Site: CodeLifter Forum, category MCP and tools
Published: 2026-11-04. Updated: 2026-11-07. Replies: 2.

## Question by CodeLifter editors (staff), 2026-11-04

Ollama is a free, MIT-licensed program that downloads open-weight language models and runs them on your own computer, with a local API on port 11434. It is reasonably safe by default because that API listens only on your machine, but it has no password, so exposing it to a network is the main risk.

## What is Ollama?

Ollama is a model runner: it fetches a model file, loads it into memory and serves it to you through a command line, a desktop app and an HTTP API. Its [GitHub repository](https://github.com/ollama/ollama) is licensed MIT, and its README sums up the pitch as "Start building with open models." An open-weight model is one whose trained parameters you can download and run yourself, such as Google's Gemma or Alibaba's Qwen, as opposed to a model you can only reach through a company's servers.

You install it with one command, from the README read on 16 October 2026:

- macOS or Linux: `curl -fsSL https://ollama.com/install.sh | sh`
- Windows, in PowerShell: `irm https://ollama.com/install.ps1 | iex`
- Docker: the official image `ollama/ollama` on Docker Hub

Then `ollama run gemma4` downloads Gemma 4 and opens a chat in your terminal. Programs talk to the same model through the REST API at `http://localhost:11434/api/chat`. Coding tools use that API: the README lists integrations for Claude Code, Codex, Copilot CLI and others, started with commands such as `ollama launch claude`. The [local coding agent thread](/forum/posts/can-you-run-a-coding-agent-for-free-with-a-local-model/) walks through those setups.

## Does Ollama send your prompts anywhere?

Not when the model runs locally. The [Ollama FAQ](https://docs.ollama.com/faq) answers this directly: "Ollama runs locally. We don't see your prompts or data when you run locally."

The exception is cloud models. Ollama also offers models that run on its servers, and in its own examples their names end in `cloud`, such as `gpt-oss:120b-cloud`. For those, the FAQ says, "we process your prompts and responses to provide the service but do not store or log that content and never train on it." That is Ollama's policy statement, not something the editors can verify. If your code must not leave your machine, turn the cloud features off:

1. Open or create `~/.ollama/server.json`.
2. Add `{ "disable_ollama_cloud": true }`, or set the environment variable `OLLAMA_NO_CLOUD=1` instead.
3. Restart Ollama.
4. Check the log for the line `Ollama cloud disabled: true`.

The FAQ notes that this also removes web search and cloud models. Pulling local models still needs the internet; Ollama fetches them over HTTPS.

## Is the local API safe?

By default, yes for a single-user machine. The FAQ says: "Ollama binds 127.0.0.1 port 11434 by default." 127.0.0.1 is the loopback address, reachable only from the same computer. The catch is in Ollama's [authentication page](https://docs.ollama.com/api/authentication): "The local API at http://localhost:11434 does not require authentication." Any program running on your machine can use it, and if you widen the bind address, so can anything that reaches that address.

The FAQ shows how to widen it, with `OLLAMA_HOST=0.0.0.0:11434`, which listens on every network interface. Put those two facts together and the rule is simple: do not set `0.0.0.0` on a laptop that joins shared Wi-Fi or on a server with a public IP unless something else controls access. The FAQ's own suggestions for remote use are a reverse proxy such as Nginx or a Cloudflare Tunnel, which let you add sign-in in front of Ollama.

Browsers are a second door. The FAQ says Ollama "allows cross-origin requests from 127.0.0.1 and 0.0.0.0 by default", and you can allow more origins with `OLLAMA_ORIGINS`. Allowing `chrome-extension://*` lets every installed Chrome extension call your models, so name specific extensions instead.

## What are the risks, side by side?

| Risk | Default | What to do |
| --- | --- | --- |
| Prompts leave your machine | Local models: no. Cloud models: yes | Disable cloud features if that matters |
| Other devices use your models | No; bound to 127.0.0.1 | Leave `OLLAMA_HOST` alone, or put a proxy with sign-in in front |
| Web pages or extensions call the API | Only local origins allowed | Add specific origins, not wildcards |
| Old version with known bugs | Desktop apps download updates | Click "Restart to update"; update Linux installs yourself |
| Model licence terms | Varies by model | Read the model's licence before shipping its output in a product |

On updates, Ollama's [SECURITY.md](https://github.com/ollama/ollama/blob/main/SECURITY.md) recommends "Regularly updating to the latest version of Ollama", "Securing access to hosted instances of Ollama" and "Monitoring systems for unusual activity", and asks that vulnerabilities be reported privately through GitHub advisories. The FAQ says the macOS and Windows apps download updates automatically; you apply them with "Restart to update".

## Is the model itself safe?

Running a model locally keeps your data private; it does not make the model's output correct. A local model writing code will still invent functions and packages; one study, [Spracklen et al.](https://arxiv.org/abs/2406.10279), found average package hallucination rates of at least 21.7% for open-source models and 5.2% for commercial ones. The [local models prompt thread](/forum/posts/do-the-codelifter-prompts-work-with-local-models/) lists the checks the editors apply to anything a local model writes, and the [LLM explainer](/forum/posts/what-is-an-llm-and-how-does-it-write-code/) covers why. Licences differ by model too: the CodeLifter [local models page](https://codelifter.com/local-models/) lists eight models with licences ranging from Apache 2.0 and MIT to the Gemma Terms of Use, checked on 15 October 2026.

## Short version

- Ollama is an MIT-licensed runner for open-weight models, with a local API on port 11434.
- Local models keep prompts on your machine; model names ending in `cloud` do not.
- The API binds to 127.0.0.1 and has no authentication, so never expose it without a proxy that adds sign-in.
- Disable cloud features with `OLLAMA_NO_CLOUD=1` if your code must stay local.
- Keep it updated, and check each model's licence.

## Reply 1 by CodeLifter editors (staff), 2026-11-04

**Is Ollama free and open source?** Yes for the software. GitHub's licence data for the [ollama/ollama repository](https://github.com/ollama/ollama), checked on 16 October 2026, reports MIT, whose text lets anyone "use, copy, modify, merge, publish, distribute, sublicense, and/or sell" the software as long as the copyright and licence notice are kept. Running local models costs nothing beyond your hardware and electricity. Two things are not covered by that answer. First, Ollama's cloud service: its [authentication page](https://docs.ollama.com/api/authentication) says that to "use cloud models, publish models, or download private models, sign in to Ollama", and that direct cloud inference at `https://ollama.com/api` "requires an API key". Those keys "do not expire", so revoke any you stop using, and keep them "outside browser code and source control". We did not open Ollama's pricing page, so we make no claim about what the cloud costs. Second, the models: each has its own licence, separate from Ollama's MIT licence, and some are not open source licences at all. The [MIT licence thread](/forum/posts/can-you-put-ai-generated-code-under-the-mit-licence/) covers what MIT does and does not let you do with generated code.

## Reply 2 by CodeLifter editors (staff), 2026-11-07

**Where does Ollama store models, and how do you delete one?** The [Ollama FAQ](https://docs.ollama.com/faq) lists the default folders: `~/.ollama/models` on macOS, `/usr/share/ollama/.ollama/models` on Linux, and `C:\Users\%username%\.ollama\models` on Windows. Check free space first: the llama.cpp quantize README lists an 8-billion-parameter model at 4.58 GiB in the common Q4_K_M format (about 4.9 bits per weight), and bigger models scale up from there. To keep them on another drive, set the `OLLAMA_MODELS` environment variable to the folder you want and restart Ollama. On Linux with the standard installer, the FAQ adds that the `ollama` service user needs read and write access, which you grant with `sudo chown -R ollama:ollama <directory>`. To manage models, the [Ollama CLI reference](https://docs.ollama.com/cli) gives these commands: `ollama ls` lists the models you have downloaded, `ollama ps` shows which ones are loaded in memory right now, `ollama stop gemma4` unloads a running model, and `ollama rm gemma4` deletes it from disk. You can download it again later with `ollama pull gemma4`.

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Cite as: CodeLifter Forum, "What is Ollama, and is it safe to run models locally?", https://codelifter.com/forum/posts/what-is-ollama-and-is-it-safe-to-run-models-locally/, accessed 2026-11-12.