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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Yes—there are credible alternatives to Meta AI. Proton Lumo, Duck.ai and Brave Leo publish privacy-focused designs, while a locally run model can keep prompt processing on your own computer. Their approaches are not identical: one may erase requests after generating a reply, another may anonymize prompts before sending them to model providers, and a local model avoids sending prompts to a hosted service but requires compatible hardware and maintenance.
“Private” is not a single technical guarantee. Before choosing, check five things: what metadata is collected, whether chats persist, whether they can be used to train models, which outside providers receive prompts, and where inference occurs. The services below describe their own policies; those statements are not independent security audits, and terms can change by region, account, plan and model.
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What “private AI chatbot” should mean
A chatbot can be private in one respect and exposed in another. A provider might avoid keeping a long-term transcript but still process your prompt on its servers. Conversely, a service may offer encrypted conversation history while retaining it so you can synchronize across devices. Compare the complete data path rather than relying on a label.
| Question | Why it matters |
|---|---|
| What is collected? | Prompts, uploaded files, account details, IP addresses, device information and diagnostic data can have different retention rules. |
| How long does content persist? | “Not stored” may still allow transient processing or short-lived caches. |
| Is training allowed? | Some providers exclude chats by default; others permit training unless you change a setting. |
| Who receives the prompt? | A front-end chatbot may route requests to one or more model companies, each with its own obligations. |
| Where does the model run? | Hosted inference places processing under a service operator; local inference keeps it on your device but transfers security and maintenance duties to you. |
The strongest claims in this guide are descriptions published by Proton, DuckDuckGo, Brave, Anthropic, OpenAI and Google. They explain stated product behavior, not resistance to every breach, malicious extension, compromised device, subpoena or operational mistake.
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Three ready-to-use alternatives outside Meta
| Service | Published approach | Where processing happens | Important qualification |
|---|---|---|---|
| Proton Lumo | Queries are sent over TLS, erased after a response, and not logged; saved Free or Plus history uses zero-access encryption. | Proton-controlled servers | Deletion after processing and encrypted saved history are different stages. Model availability changes. |
| Duck.ai | Chats are not stored by default; identifying metadata is removed before requests reach model providers; provider agreements state zero retention and no training. | DuckDuckGo front end plus selected model providers | Some models can cache prompts in memory for up to one hour; legal, abuse and file-moderation exceptions apply. |
| Brave Leo | Hosted conversations are not logged after a response or used to train models; history is kept locally, with temporary and history-disabled modes. | Brave-hosted models, or an endpoint you configure | A remote BYOM endpoint follows that provider’s policy, not automatically Brave’s hosted-model policy. |
Proton Lumo: hosted convenience with separated storage stages
Proton says Lumo sends queries over TLS, processes them on Proton-controlled servers, and erases the query data after generating a response. It also says chats are not used to train its models unless you expressly permit anonymized feedback on an Apertus 1.5 response.
Saved history is a separate matter. For Free and Plus users, Proton says stored conversations synchronize with zero-access encryption. That means the service can provide cross-device history while distinguishing it from the temporary server-side processing used to answer a new prompt. Do not interpret “erased after processing” as “saved history is impossible”; the two features refer to different data states.
Lumo’s currently listed models include Qwen 3.5, GLM 5.3, Image-Turbo, FireRed-Image-Edit-1.1 and Apertus 1.5. That roster is a snapshot, not a permanent specification. Check the model selector and current terms before relying on a particular model or image feature.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteGood fit: someone who wants a conventional cloud assistant and explicit statements about deletion, encrypted history and training controls, without operating local inference software.
Duck.ai: anonymization before multi-provider routing
DuckDuckGo says Duck.ai does not record or store chats by default. It says identifying metadata, including the IP address, is removed before a prompt is sent to an underlying model provider. Its agreements with providers state that prompts are not retained and are not used to train their models.
Those statements have defined exceptions. The help documentation identifies up to one hour of in-memory prompt caching for some OpenAI and Anthropic models, retention required by law or connected to abuse investigations, and moderation of uploaded files. Text-only use and file uploads therefore should not be treated as identical. If a prompt contains a document or image, read the current upload-moderation terms before submitting confidential material.
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Because Duck.ai can expose multiple model providers through one interface, provider-specific behavior matters. A privacy setting that applies to the Duck.ai interface does not erase the fact that a model provider must receive the prompt long enough to generate an answer.
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Brave Leo: browser integration, local history and a BYOM path
Brave says conversations with its hosted Leo models are not stored or logged after a response is generated and are not used to train those models. Chat history is stored locally on the device. Leo also offers temporary chat and a mode with history disabled, useful when you do not want the browser to retain a local transcript.
Leo’s model choices and plan features change over time; Brave’s model documentation is dated October 6, 2026. Treat that list as a dated availability notice rather than a guarantee that a named model will remain accessible.
Brave also documents “bring your own model” (BYOM). You can connect Leo to a model running on your computer or to a remote endpoint. With a local endpoint, prompts are processed by software on your device. With a remote endpoint, the endpoint operator receives the prompt and its privacy policy controls retention and training. Connecting a remote service does not extend Brave’s hosted-model promises to that service.
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Running a model locally
Local inference means the model processes prompts on your own computer instead of sending them to a hosted chatbot. It gives you the clearest control over the processing location among these options, but it is not a one-click privacy guarantee. Your operating system, local files, backups, browser extensions, malware defenses and network configuration still determine what can be exposed.
Hardware and software requirements
Brave’s BYOM documentation calls for the desktop version of Brave and a compatible model-serving framework. It lists a minimum of 8 GB of RAM and model files ranging from 829 MB to 40 GB, with sufficient processing power for the chosen model. Those figures describe a technical range, not a promise that every computer can run every model at a usable speed. Larger models generally demand more memory and compute capacity.
- Memory: at least 8 GB according to the cited Brave setup guidance; leave headroom for the operating system and other applications.
- Storage: allow for model files from roughly 829 MB to 40 GB, plus space for updates, caches and conversation data.
- Processor or graphics hardware: performance depends on the model, quantization, framework and whether acceleration is available.
- Desktop software: the documented Leo BYOM route uses desktop Brave rather than a mobile browser.
- Security maintenance: keep the operating system, browser, serving framework and model files updated, and protect local backups.
A practical local setup sequence
- Choose the privacy boundary. Decide whether prompts must remain on the computer. If yes, select a local endpoint; if you use a hosted or remote endpoint, document which company receives the text.
- Check capacity. Compare the model’s memory and storage needs with your computer’s available RAM, disk space and processing capability. The 8 GB minimum is only a baseline.
- Install the desktop host and serving framework. Use the current desktop Brave release and a compatible local model-serving framework identified by its documentation. Avoid downloading executables or models from untrusted mirrors.
- Download a model that fits. Verify the model’s license, language support, context length and file size. A smaller model may run locally while a larger one may require more memory or a capable GPU.
- Connect the endpoint in Leo or your chosen interface. Select the local address supplied by the serving framework. If the address points to a cloud server instead, it is remote and has different privacy consequences.
- Test with non-sensitive prompts. Confirm that the framework reports local inference and that no proxy, telemetry service or remote fallback is enabled before using confidential material.
- Control persistence. Review where the interface, framework and operating system save conversation logs, crash reports and backups. Disable history where appropriate and secure files that must remain.
Local operation reduces dependence on a chatbot provider; it does not protect data already present on an infected computer or copied into a cloud backup. Treat the endpoint, model files and logs as ordinary sensitive software assets.
How familiar non-Meta services handle privacy controls
A service outside Meta’s ecosystem is not automatically private. The following examples show why account type, settings and exceptions matter.
Claude consumer accounts
In a March 16, 2026 consumer guidance article, Anthropic says chats and coding sessions may be used to improve models when the user chooses that option, when a conversation is flagged for safety review, or through another explicit opt-in. Anthropic states: “Your Incognito chats are not used to improve Claude, even if you have enabled Model Improvement in your Privacy Settings.”
Feedback has a separate consequence: Anthropic says feedback can retain the related conversation for up to five years. Incognito mode and feedback submission should therefore be considered separately when handling sensitive material. Consumer guidance does not automatically describe commercial or API accounts.
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ChatGPT individual and business accounts
OpenAI says content from individual services may be used to improve models, while users can opt out. It states that, once opted out, new conversations will not be used to train its models. Temporary Chat is not used for training.
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OpenAI separately says business inputs and outputs are excluded by default unless an organization explicitly opts in. Do not transfer business-account terms to a personal or free account. Check the data-control setting on the exact account you are using.
Gemini Apps and sign-in status
Google’s notice, last updated June 29, 2026, describes data processed when a person interacts with Gemini Apps while signed in. The notice includes prompts, shared files and media, and Gemini Live recordings and transcripts, and supplements Google’s general privacy policy. Signed-out use is addressed separately in that policy.
Geography also matters: Google says Google Ireland Limited serves the European Economic Area and Switzerland, while Google LLC serves elsewhere. The notice is not a complete setting-by-setting comparison of every training or retention choice, so review the terms and controls that apply to your location, account and Gemini surface.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing the right option
Choose a hosted privacy-focused assistant when…
- You need a polished service without installing or updating a model.
- You can accept server-side processing and want a provider’s published deletion, anonymization or no-training commitments.
- You understand the distinction between transient processing and optional saved history.
Choose a local model when…
- Keeping prompts on your own machine is more important than maximum model quality or convenience.
- Your computer meets the model’s memory, storage and processing requirements.
- You are prepared to maintain software, protect logs and investigate where backups are stored.
Use extra caution for professional or regulated data
Consumer documentation alone does not establish legal compliance, contractual protection or suitability for a regulated workflow. Before entering personal, health, financial, legal or employer-confidential information, check the exact plan, data-processing agreement, jurisdiction, retention settings and organization policy. If those requirements cannot be verified, use a redacted or synthetic example instead.
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Privacy pitfalls and how to avoid them
“No logs” interpreted as “no processing”
A model must receive enough information to generate a response. No-retention language generally describes what happens after processing, not the absence of processing. Proton’s deletion statement and encrypted saved-history feature illustrate why those stages must be read separately.
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Ignoring provider-specific exceptions
Duck.ai’s default behavior does not eliminate every temporary cache, legal retention requirement or uploaded-file moderation process. Read the exceptions for the model and content type you selected.
Assuming a remote BYOM endpoint is local
A local interface can still point to a remote server. Confirm the endpoint address and the operator’s policy before sending confidential prompts.
Leaving browser or framework history enabled
Even when a hosted provider does not retain a transcript, your browser, extension, serving framework, crash reporter or backup may. Review local history and diagnostic settings on every component in the path.
Relying on an old model list or policy page
Model rosters, plans, regions and privacy controls change. Recheck the current documentation immediately before adopting a service, especially for work that depends on a named model or a particular retention setting.
Bottom line
For a ready-to-use alternative to Meta AI, Proton Lumo, Duck.ai and Brave Leo offer the clearest published privacy distinctions: deletion after processing, metadata anonymization, local history and optional local-model connections. If prompts must remain under your physical control, run a compatible model locally and accept the hardware and security work that entails. No option should be called absolutely private from provider descriptions alone; make the choice by matching the service’s data path and exceptions to the sensitivity of your prompts.
Quick Recap
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