Lumo 2.0 vs Duck.ai: Privacy Architecture and Data Boundaries Compared

Lumo 2.0 and Duck.ai both present themselves as privacy-conscious AI assistants, but they do not reach that goal through the same architecture. Lumo centers processing on Proton’s infrastructure and encrypts saved history so Proton says it cannot decrypt it. Duck.ai acts as an intermediary: it removes identifying metadata before sending prompts to model providers and offers encrypted synchronization for saved conversations.

This comparison attributes those descriptions to the vendors. It does not treat policy text, open-source code, encryption terminology, or a provider agreement as equivalent to a product-specific independent privacy audit. There is therefore no objective privacy winner here. The useful question is which data boundary better matches your threat model.

Privacy architecture at a glance

Where Lumo draws the boundary

Proton’s support documentation says stored history is protected with zero-access encryption, transmission uses TLS, and prompts are asymmetrically encrypted so only Lumo GPU servers can decrypt them. This is a vertically controlled design claim: the plaintext processing boundary is described as Proton’s Lumo compute rather than an unrelated model-provider endpoint.

That boundary is not absolute for every optional feature. Lumo’s privacy policy says web search can transmit a simplified request to selected partner APIs, although it says the full query is not shared. A cautious workflow is therefore to leave web search off for prompts whose sensitivity outweighs the value of current web results.

Where Duck.ai draws the boundary

DuckDuckGo’s documentation describes a proxy-style design. It says chats are anonymized and metadata containing personal information, including an IP address, is removed before the model provider receives the request. This reduces direct linkage between the provider and the user, but the chosen provider still participates in processing the prompt.

Duck.ai also documents an optional location boundary. Its approximate-location setting can send city-level context to providers, but DuckDuckGo says that setting is off by default and does not expose the exact location or IP address. Users who do not need localized answers can keep that option disabled.

For history, Duck.ai’s optional Sync & Backup feature stores conversations on a DuckDuckGo server and makes them accessible through end-to-end encryption. That is distinct from the live inference path: encrypted history storage does not mean an underlying model provider never processes the plaintext needed to produce an answer.

Duck.ai’s general privacy policy also says provider agreements require deletion when information is no longer needed to produce output, at most within 30 days, with limited safety and legal exceptions. That policy-level ceiling should not be silently converted into a promise that every provider route always has zero retention.

Capability context, not a privacy score

The following launch snapshot uses three directly comparable Artificial Analysis Intelligence Index values reported by PacketNebula. The required 0–100 display axis is shared by all rows. These values describe model capability, not encryption, anonymity, retention, or audit quality.

The chart is useful only as context for why someone might consider the newer Lumo models. It cannot establish that Lumo is more private than Duck.ai.

Which boundary fits your use case?

Choose Lumo when your priority is a vendor-described inference path confined to Proton-controlled Lumo servers and zero-access-encrypted history. Treat optional web search as a separate disclosure decision because partner APIs can receive a simplified request.

Choose Duck.ai when you want access mediated through an anonymizing service and value the ability to use different underlying providers without giving those providers your IP address. Review the selected model’s current disclosure, leave approximate location disabled when it adds no value, and distinguish encrypted history from live provider processing.

For either service, do not paste secrets, authentication material, unpublished customer data, or information you are not authorized to disclose. Encryption and anonymization reduce particular risks; they do not correct an inappropriate disclosure at the moment a prompt is submitted.

Readers comparing broader subscriptions can also consult the monthly AI plan workflow guide. If the goal is specifically meeting transcription rather than general chat, the free AI meeting-notes comparison covers a different set of data-handling decisions.