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A private AI chatbot sounds like a simple product category. It is not. In August 2026, DuckDuckGo published a survey of AI users and reported that 56% of AI enthusiasts, and a third of all users, tell chatbots things they keep from other people. That is a vendor survey, so read it with care, but the underlying behavior matches what anyone sees in their own chat history. People paste in medical worries, contract drafts, relationship problems and source code. Those pastes land on servers run by companies with very different rules about who can read them.
This comparison puts six options side by side as of October 26, 2026: Duck.ai, Venice AI, Proton Lumo, Maple, ChatGPT and Claude. The first four sell privacy as their reason to exist. The last two are the mainstream baseline, and they still matter because most readers already use one of them. If you only want to know how the big three differ on free message limits, the free-tier message caps comparison covers that. This article asks a narrower question: who can read what you type, and can you check?
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A method note before the tables. Nothing here comes from packet captures or lab tests. The comparison rests on each company’s published pricing and policy pages, plus an October 2026 buyer’s guide from Maple, which also makes one of the six apps. That conflict of interest matters, so every claim that depends on Maple’s reading of a rival’s policy is labeled as such. Where the sources go quiet, this article says so instead of filling the gap.
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A chatbot is private when it limits who can read your prompts, and when you have some way to confirm that limit holds. Almost every hosted app encrypts the connection between your device and the server. The real differences start once the prompt arrives. Some services pass the text to a third-party model provider. Others run the model themselves and promise not to keep logs. A small group decrypts prompts only inside hardware-isolated enclaves that your app can check before sending anything. For a sense of how many people use each mainstream assistant, see the usage share comparison.
Maple’s guide sorts private-AI products into five models, and the taxonomy is the most useful frame I found for this topic. I keep it here with one warning: the author ranks his own product first, so treat the ordering as an argument rather than a verdict.
The first three ask you to trust a promise. The fourth lets you check it. The fifth removes the question, at the cost of smaller models and your own hardware. None of these categories says anything about answer quality, which is why a private pick still has to survive a normal usability test.
An app that cannot read your chats can still know which account sent them and how you paid. Maple makes this point in its own guide, and it holds for every product in this article. Duck.ai hides your identity from the model provider but not the text. Lumo encrypts saved history but processes each prompt in readable form. If your threat model includes traceability, you need to think about sign-up method and payment as well as the chatbot. Maple, for example, says you can register without an email and pay with Bitcoin.
The table below compresses the facts that matter most. Privacy-model labels and the can the company or provider read prompts row follow Maple’s October 2026 guide unless noted, and prices come from each vendor’s own pricing page where I could fetch one. Single-source details are kept out of the table and discussed in the sections that follow.
Two rows deserve a second look. The can you verify row separates the field more cleanly than price or model count does. And the company base row is a reminder that a private chatbot is only as durable as the business behind it. Venice raised outside capital for the first time in 2026, while Proton and DuckDuckGo are established privacy brands with other revenue lines.
Duck.ai is DuckDuckGo’s chat product, and its design is the simplest to explain. DuckDuckGo removes metadata such as your IP address and forwards your prompt to a model provider. Maple’s guide, citing DuckDuckGo’s own page on what Duck.ai shares, lists OpenAI, Anthropic and together.ai as the providers. The provider sees your text but not who sent it. Two open models run inside a trusted execution environment, which DuckDuckGo does not let users verify.
On training, DuckDuckGo’s terms say providers do not train on prompts and delete them within 30 days, with limited exceptions. The help page, per Maple, says deletion happens right after the reply, again with exceptions. The two statements differ, and a careful reader should check the current wording before pasting anything sensitive.
The newest product change I found is dated October 7, 2026. DuckDuckGo added a way to share a Duck.ai chat through a link, and says anyone who receives the link can read the chat, continue it or save it without an account. Shared chats are encrypted, according to the company. The feature is handy, but any link you create is another place a conversation can leak, so treat it like sharing a document.
On plans, earlier free-only descriptions of Duck.ai may be outdated. Maple’s table lists paid plans for several of these tools, but it is a third-party comparison, not a vendor price list. One search result reportedly puts DuckDuckGo Pro at $19.99 a month, but I could not confirm that figure on a vendor page, so check duck.ai before you buy.
Best fit: people who want frontier models from the big labs without tying prompts to an account. Weak spot: the model provider still reads the text, and you cannot audit the promise.
Venice AI pitches itself as private and unrestricted, and it has the busiest catalog of the six. On July 1, 2026, Venice announced a $65 million Series A at a $1 billion valuation, its first outside capital. The company now lists text, image, video, audio and embedding models, plus an OpenAI-compatible API. In the weeks before October 26 it added GPT-6.1 Sol Ultrafast (October 8), Kling 4.0 video (October 15) and an abliterated reasoning model built on DeepSeek V4.1 Flash (October 23).
Privacy on Venice varies by model, and that is the part buyers miss. Venice labels each model as Anonymized, Private or E2EE. Maple’s reading of Venice’s privacy page is blunt. With Anonymized models, the third-party provider sees and likely saves your prompt. The default Private mode runs on Venice or partner GPUs and relies on trust. The TEE and E2EE modes on Pro use enclaves verified by remote attestation, and Maple counted 12 text models in E2EE mode. Venice’s own privacy policy, in Maple’s reading, does not address training on chat content.
Pricing is the cleanest part. The Free plan allows 10 text prompts and 15 image prompts a day. Pro costs $18 a month, with unlimited text prompts and a monthly credit allotment for video, music and premium models. Pro Plus and Max add bigger allotments and credit banking, equal to $75 and $225 of monthly API spend. Credits are priced at 100 per dollar. Venice also sells its VVV token, which lets developers stake for reserved compute, a detail that tells you who the company expects its power users to be.
Best fit: creators and developers who want one account for chat, images and video, and who will pick enclave models for sensitive work. Weak spot: you must check the mode label on every model, because the default is not the strongest option.
Lumo is Proton’s assistant, and it inherits the brand’s reputation for email and VPN privacy. Proton says Lumo does not log conversations, does not train on them and protects saved chats with zero-access encryption. The models run on servers Proton controls in Europe. The company is based in Geneva, which matters to buyers who care about jurisdiction.
The subtlety is what that encryption covers. Maple’s guide cites Proton’s own security model post, which says the language model sees the decrypted user message in order to process it, and that the server forgets the request once the answer is generated. In other words, zero-access encryption protects stored history, not the moment of inference. Proton’s CEO makes a similar point in a quote I use later in this article.
Product momentum is real. Proton announced Lumo 2.0 around the end of June 2026, then shipped a canvas called Creations on October 22 that lets Lumo create and edit documents, code and interactive web pages beside the chat. On October 15 the company’s autumn and winter roadmap promised a Lumo API, a desktop app and mobile improvements, though none has an exact date. The wordmark also changed to Lumo AI on October 23, a branding tweak and not a retirement.
Pricing is published on Proton’s page. Free costs $0 and you can start chatting without a Proton account. Lumo Plus is $12.99 a month, or $9.99 a month on a yearly plan billed at $119.88 every 12 months. Plus removes the chat, history and image limits and adds unlimited projects and custom Lumos. Proton’s documentation, as of July 1, listed Qwen 3.5, GLM 5.2 and two image models as the lineup behind Lumo, so expect that list to move. To see how those paid tiers stack up against the big three, the Pro plan comparison breaks down limits and context windows.
Best fit: existing Proton users and Europeans who want a vendor with a long privacy record. Weak spot: the no-logs promise cannot be independently checked today.
Maple is the only product here that describes itself as unable to read your chats by design. According to Maple’s own blog, chats are encrypted on your device and decrypted only inside hardware-isolated enclaves running open-source code, and the app checks the enclave’s attestation before sending anything. The code is on GitHub under the MIT license. Maple adds that users still rely on the hardware behind each enclave, the published code and Maple’s signing key, which is an honest limit.
The trade-offs sit in the model list and the audit trail. Maple offers open-weight models only, so there is no GPT or Claude. The October 2026 catalog includes GLM 5.3, Kimi K3 (added October 12), DeepSeek V4.1 Flash and GPT-OSS 120B, and Maple published guides for Gemma 4 31B and GPT-OSS 120B on October 21. By its own admission, Maple has no BAA and no independent audit yet. It says it is working toward a BAA and a SOC 2 audit. Until those land, the verification story depends on people reading the code.
Coverage across devices is the strongest of the group: web, iPhone, Android, Mac, Windows and Linux, plus the Maple Proxy API. Pricing is Free at $0 with web search, Pro at $20 a month, Max at $100 a month with ten times Pro’s usage, and Team at $30 per user. Pro unlocks all models, image and document upload on the apps, voice recording and API access. API rates run per million tokens. DeepSeek V4.1 Flash costs $0.65 input and $1.45 output, GPT-OSS 120B costs $0.30 and $1.20, and Kimi K3 costs $8.00 and $25.00.
Best fit: anyone who wants the strongest checkable privacy claim and can live with open models. Weak spot: no frontier proprietary models, no external audit, and a vendor guide that also serves as its own marketing.
The mainstream pair earn their place because they are what most people already use. ChatGPT’s pricing page lists Free, Go, Plus, Pro, Business and Enterprise plans. OpenAI began rolling out GPT-6 with Intelligent UI on October 7, 2026. Plus, Pro, Business and Enterprise run GPT-6 Sol, and Free and Go run GPT-6 Luna. Pro now spans three tiers at $100, $200 and $500 a month, according to OpenAI’s help center. For how the two assistants compare on everything except privacy, see the ChatGPT vs Claude vs Gemini head-to-head.
On privacy, Maple summarizes OpenAI’s consumer data page this way. Authorized staff and trusted providers can access content for abuse investigations, support, legal matters and model improvement unless you opt out. Chats on personal plans may train models unless you turn that off in data controls. Even after you opt out, a thumbs up or down on a reply lets OpenAI use that whole conversation. Temporary chats may be kept for up to 30 days. I could not confirm those settings from a first-party page in my own fetch, so verify them in your account before you rely on them.
Claude is similar in kind but different in default. Per Maple, Anthropic uses your chats for training only if you choose to allow it, and keeps incognito chats for 30 days by default. Anthropic’s lineup is the Claude 5.5 family: Opus 5.5, Sonnet 5.5 and Haiku 5.5. The US Pro plan costs $20 a month on Anthropic’s pricing page, and the Max tiers are reportedly $100 and $200. Anthropic also published an updated Usage Policy on October 8 that reportedly takes effect on November 12, 2026, so read the new terms.
Neither product is built so the company cannot read your chat. These settings change what happens to your chats, not who can read them. That is a legitimate position for work that is merely personal, and a poor one for material you would be uncomfortable seeing in a legal discovery request. If you stay on these tools, turn off training where you can and avoid pasting secrets. For persistent memory, the memory comparison across ChatGPT, Claude and Gemini explains what each assistant retains.
Every product here has a free entry point, and the paid tiers cluster around $10 to $20 a month. The spread appears at the top. Venice’s Max and OpenAI’s Pro 500 serve heavy users, while Maple’s Max at $100 is a usage multiplier, not a different privacy class. Prices below are monthly billing in US dollars, and any figure that did not come from the vendor’s own page is labeled.
Two details change the math. First, ChatGPT’s Pro 200 subscribers who held the plan during a September 22 to 29 window keep their old allowance through October 29, after which the allowance drops while the price stays at $200. Second, Venice charges separately for video and other credit-based features, so a $18 plan does not cap your spending the way a flat plan does. For a wider look at what the big assistants charge, the subscription pricing comparison lays out the plan ladder.
On price alone, the cheapest credible private option is Lumo Plus on a yearly plan at $9.99 a month. On price per verifiable guarantee, Maple Pro at $20 is the only paid plan in this group that buys an attestable design. Duck.ai’s low entry price buys anonymity from the model provider, which is a different thing.
A normal comparison would include benchmark charts here. For privacy, no independent benchmark exists that I could find, and I will not invent one. Model-quality leaderboards say nothing about who reads your prompt. What a reader can do is grade how strong the evidence is behind each product’s privacy claim. The table below is my editorial grading of the sources I reviewed, not a lab result.
Three sources shape this grading. The first is Maple’s October 2026 guide, which is detailed and dated but comes from a competitor. The second is each vendor’s own pricing and product pages, which are reliable for plan details and unreliable as proof of privacy. The third is reporting, such as the Wired interview with Proton’s CEO quoted below. What is missing from all three is an external audit of any consumer chatbot in this group. Until one appears, private remains a claim with different grades of support.
Some facts remain open. Venice’s policy does not state whether chat content trains models, per Maple. The exact current retention terms for ChatGPT and Claude should come from their own help centers. And the $19.99 Duck.ai Pro price rests on a single report. Each of these is an easy check, and each belongs on your to-do list before you commit data to a service.
Specs tables hide the awkward details, so these cases show where claims bend. Every one comes from the sources reviewed above, and those relying on Maple’s guide are marked that way.
The pattern across all six: marketing language promises privacy, and the details live in what is encrypted, who holds the keys and which metadata survives. Read the details before you read the slogan.
Few executives in this market speak plainly about the limits of their own product. Proton’s Andy Yen, co-founder and CEO, did so in an interview with Andy Greenberg published on August 18, 2026. Yen described Lumo’s privacy as a promise from Proton, and put the promise this way in the Wired interview: “we’re not gonna look at these logs. In fact, we’re not gonna log this at all.”
The same interview contains the technical caveat that explains why Lumo sits in the operator no-logs category. In Yen’s words: “The issue with LLMs is that the technology for doing that in a fully end-to-end encrypted way is not there yet.” That statement reads as a candid description of the problem Maple, Confer and Venice’s enclave models try to solve with trusted hardware. It also shows that the debate between Proton and enclave vendors is about engineering, not about intent.
Industry data adds the demand side. DuckDuckGo’s August 25, 2026 survey found that one in three AI users has told a chatbot something they kept from people in their lives, and the company says most people dislike how AI companies handle chats once they learn the details. The survey was run by a company that sells a privacy-oriented chatbot, so its framing favors its product. The behavior it measures is still a useful reminder of why these choices matter.
No single product wins every job. These six recommendations follow the privacy models above and the plan details from earlier sections.
If nothing may leave your machine, skip hosted apps entirely. LM Studio, Ollama and Jan run models on your own computer. Maple’s guide cites LM Studio’s recommendation of 16GB or more of RAM, and the models that fit on a laptop are smaller than the hosted ones. Local also means your chat history is only as safe as your device.
Switching does not need a weekend. Most people can move their sensitive work in an hour and keep the old assistant for everything else.
Developers have an easier path. Both Venice and Maple Proxy expose OpenAI-compatible APIs, so most existing code needs only a new base URL, key and model name. Take the exact base URL and model IDs from each provider’s docs, because the placeholders below are not real endpoints.
Budget with the published rates. Maple lists DeepSeek V4.1 Flash at $0.65 per million input tokens, and its API usage draws from your plan allowance before any extra credits. If cache pricing drives your costs, the API cache pricing comparison shows how the mainstream providers treat repeated context.
For most readers who want a real step up in privacy, Maple is the strongest pick on the evidence available. It is the only product in this group whose privacy claim you can check through attestation and open code, it runs on every major platform and its paid plan costs $20. The costs are the open-weight model list and the lack of an outside audit. Because the best evidence for its design comes from Maple itself, I treat the pick as provisional until an independent review lands.
Proton Lumo is the sensible second choice for Proton users and anyone who values jurisdiction and a long privacy record, with the honest caveat that its prompts are readable during inference. Duck.ai wins on simplicity and frontier-model access when anonymity from the provider is enough. Venice suits people who want one private-leaning account for chat, images and video. ChatGPT and Claude remain the most capable general tools, and they belong in a workflow that keeps secrets out of the prompt.
The takeaway: match the privacy model to the sensitivity of the task. Use an attested enclave for the material you could not bear to see leaked, an anonymizing proxy for casual frontier-model use, and a mainstream assistant with training switched off for the rest. More roundups of the assistants themselves live in the AI and machine learning section.
A local model on your own device is the only option with no company in the loop. Among hosted apps, attested-enclave designs such as Maple, Confer and Privatemode are built so the operator cannot read prompts. Even those still know your account and payment details.
Duck.ai removes your identity from requests, so the model provider does not learn who you are. The provider still receives the text of your prompt. DuckDuckGo’s terms say providers do not train on it and delete it within 30 days, with limited exceptions.
Proton says saved chats use zero-access encryption and that it keeps no logs of prompts. Per Proton’s own security model, as summarized by Maple, the model sees a decrypted message while generating an answer. Stored history is protected, but processing happens on readable text.
Both are policy-only. Per Maple’s summary, OpenAI may train on personal-plan chats unless you opt out, while Anthropic trains only if you allow it. Both companies can read content under their policies, so confirm current terms on their help pages.
Every option here has a free tier. Paid plans run from $9.99 (Lumo Plus on a yearly plan) to $20 a month for Maple Pro and Claude Pro, with Venice Pro at $18. Heavy-usage tiers reach $100 on Maple Max and $500 on ChatGPT Pro 500.
Maple lists apps for Mac, Windows and Linux alongside iPhone and Android, and said its Windows app became available in October 2026. Proton has a dedicated Lumo desktop app on its roadmap, so for now Lumo runs on the web and in mobile apps.
Choose local when nothing may leave your machine. LM Studio, Ollama and Jan run open-weight models on your hardware, and Maple’s guide cites a 16GB RAM recommendation from LM Studio. Expect smaller models than the hosted options and take responsibility for securing your own device.
Nadia Dubois is the AI & Innovation Editor at Tech Insider, where she tracks the rapid evolution of artificial intelligence, from foundation models to real-world enterprise deployment. She previously covered AI and startups for La Tribune and contributed to MIT Technology Review's European coverage. Nadia specializes in generative AI, AI regulation, and the intersection of technology and European industrial policy. She holds a dual degree in Computational Linguistics and Journalism from Sciences Po Paris.
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