Most “best AI companion app for privacy” lists rank apps by how good the privacy copy sounds. That’s backwards. Privacy isn’t a feeling or a slogan — it’s an architectural property you can grade. So this page does something different: it defines a strict, five-point rubric, scores the major companion apps against it, and assigns letter grades you can actually defend. No invented breaches, no fearmongering. Just a clear answer to a simple question: if this company gets breached, subpoenaed, or acquired tomorrow, what happens to everything you typed?
The short version, stated up front so you can stop reading if you want: no cloud companion app earns an A on privacy, because the architecture won’t allow it. The only A-tier answers are local-first (the model runs on your own machine, nothing uploads) and a narrow slice of no-sell, zero-retention hosted options. Everything else is a B at best, and a lot of it is C or worse. Here’s the work behind that verdict.
The grading rubric: five things that actually decide privacy
Marketing copy is noise. These five questions are signal. Each is worth one point; five points is a perfect (and, spoiler, basically impossible-for-cloud) score.
- End-to-end privacy of message content (E2E). Can anyone other than you read your plaintext? For a chat app this usually means encryption in transit and at rest. For a companion app there’s a brutal catch: the model itself must read your plaintext to reply, so true end-to-end encryption against the provider is essentially impossible in the cloud. The only way to score full marks here is for the inference to happen on hardware you control.
- Training on your conversations. Does the company reserve the right to use your chats — even “de-identified” — to train or improve models? If the policy doesn’t contain an explicit “we do not train on your conversations,” treat the door as open. This is the line that changes most often between versions and jurisdictions.
- Real deletion. Can you delete everything, including backups and logs, and get confirmation? A “delete” button that doesn’t mention backups is marking a row, not erasing your history.
- Third-party routing. Does your data pass through advertisers, analytics SDKs, model-API subprocessors, or moderation vendors? Every additional party is another place your intimate text exists and another potential leak you opted into.
- Account / identity requirement. Must you attach an email, phone, payment method, or — worst case — a selfie or ID to use it? A breach of an anonymous service leaks text; a breach of an identity-linked service leaks your text next to your name.
A few ground rules for the scoring. I grade the architecture and the published policy, not vibes. Where a company’s terms are vague, that counts against it — vagueness is a choice. And I hedge where the public record is thin; “as reported” means exactly that. For the full threat model behind this rubric, see are AI girlfriend apps safe and private.
The grading table: major apps scored against the rubric
Scores are out of 5, one point per rubric item. They reflect the general cloud architecture plus each company’s published posture as of writing — always read the current policy yourself, because these change. “Cloud companion” rows share the same structural ceiling; the differences are in policy and data hygiene.
| App | E2E vs provider | No training on you | Real deletion | No 3rd-party routing | Anonymous use | Grade |
|---|---|---|---|---|---|---|
| Local-first (Ollama-based, e.g. Ember) | Yes — runs on your machine | Yes — nothing leaves device | Yes — delete the files | Yes — loopback only | Yes — no account | A |
| No-sell zero-retention hosted (e.g. Freya) | No (cloud) | If committed in writing | Per policy | Minimal by design | Account required | B+ |
| Kindroid | No (cloud) | Per policy — read current terms | Per policy | Cloud subprocessors likely | Account required | B / B− |
| Nomi.ai | No (cloud) | Per policy — read current terms | Per policy | Cloud subprocessors likely | Account required | B− |
| Replika | No (cloud) | Per policy — historically scrutinized | Per policy | Analytics/ads historically flagged | Account required | C+ |
| Character.AI | No (cloud) | Uses chats to improve service per policy | Per policy | Large-platform analytics | Account required | C+ |
| Candy AI | No (cloud) | Per policy — read current clause | Per policy | Payment/moderation subprocessors | Account required | C+ |
| Janitor AI | No (cloud) | Routes to third-party model APIs | Depends on backend | Third-party model providers | Account/key often required | C / C− |
| “Free” ad-supported companion apps | No (cloud) | Often yes (you’re the product) | Often weak/vague | Ad networks + trackers | Email/phone required | D / F |
Per-app deep dives, where they exist: is Kindroid safe and private, is Nomi.ai private, does Replika sell your data, and is Candy AI safe and private.
Why even the ‘best’ cloud apps cap out at a B
This is the part most ranking lists won’t tell you: the ceiling for a cloud companion is structural, not a matter of effort. A hosted companion is, mechanically, a chat app with a server in the middle, and that server must do three things to function:
- Receive your plaintext. The model reads your words to reply, so they exist decrypted inside the company’s infrastructure. That alone forfeits rubric point #1 — you can never get true E2E privacy against the provider in the cloud.
- Store conversation history. Memory is the whole point of a companion; persistence is the liability that comes with it. Your logs sit in a database governed by their retention schedule, their access controls, and their lawful-disclosure obligations.
- Attach it to an identity. Email, phone, payment, device ID. A breach doesn’t spill anonymous text; it spills attributable text.
So the very best a cloud app can do is be honest and disciplined within those constraints: commit in writing not to train on you, minimize subprocessors, and run a genuine zero-retention design. That earns a B+, and it’s a real, meaningful B+. But it’s still trust, not proof — you cannot audit a server’s memory from the outside, and a privacy policy is a promise that can be rewritten with a version bump or an acquisition. That’s the difference between private by policy and private by design. The same logic explains why cloud AI censors and refuses you: once the computation lives on someone else’s machine, their rules — and their risk — ride along with it.
C-tier and worse: the apps to avoid for anything sensitive
A C grade doesn’t mean a company is acting in bad faith. It means the defaults work against you, so the app is a poor choice for anything you’d be devastated to see leaked.
Watch for these patterns:
- “We may use your data to improve our services.” This is the most common phrasing, and it’s a green light to retain and potentially train on your content. Several large companion and chatbot platforms have, per their own policies, used conversations to improve their models. That’s legal and disclosed — and it’s exactly why these apps sit at C+ rather than B.
- Third-party model routing. Some apps (Janitor AI is a frequently-searched example) act as front-ends that pass your prompts to external model APIs. Your data’s privacy is then only as good as whichever backend you route to — and that’s often outside the app’s control or yours.
- “Free” companion apps. If you’re not paying, the monetization is usually your data. Mozilla’s Privacy Not Included reviewers have repeatedly flagged the romantic-AI category as among the worst they’d tested for data practices — excessive collection, vague deletion, and tracker/ad sharing. Ad-supported free apps are the clearest F-risk in the category.
- Identity escalation. Any app that asks for a selfie, voice clip, or ID upload for “personalization” is stockpiling breach catastrophe. Linked intimate logs are raw material for sextortion and doxxing, and unlike a password, you can’t rotate a transcript of your private conversations.
The honest rule: if an app fails the first two rubric questions — plaintext on their server, and no clear “we don’t train on you” — the rest barely matters. The architecture already decided your privacy. More on the broader pattern in our AI companion privacy guide.
The A-tier: local-first and no-sell hosted
Only two designs break the cloud ceiling.
Local-first (the only true 5/5). Run an open-weight model on your own machine and nothing is uploaded — there’s no server, no account, and no log to breach. Tools like Ollama make this genuinely approachable. The install is one line:
curl -fsSL https://ollama.com/install.sh | shThen you pull a model and run it:
ollama run <model>Inference happens entirely on loopback (127.0.0.1:11434), an address that never touches the public internet. The “memory” of your companion is a file on your disk, under your control, deletable for real. You can prove it works offline: pull the plug on your network and the companion still talks back. This is the only setup that scores all five rubric points — and it does so by architecture, not promise. Ember is the productized version of this for people who don’t want to assemble the stack themselves: an uncensored companion that runs 100% on your machine via Ollama, bought once, no subscription, no cloud, no logs.
No-sell, zero-retention hosted (the realistic B+). Not everyone has a GPU or wants to manage models. The honest middle path is a hosted companion that doesn’t retain your conversations — processed in memory, discarded after the reply, never written to a chat-log store, and never sold or used to train. It’s still trust, not proof, but it narrows the blast radius enormously: no chat-log database means a database breach has far less to spill. This is where Freya sits — zero setup, working in seconds, for the reader who wants it now.
Matching a pick to your situation
The right answer depends on two things: do you have a capable GPU, and how technical are you?
| Your situation | Best pick | Why |
|---|---|---|
| Have a GPU (8GB+ VRAM), comfortable with a one-time setup | Local-first (Ember / Ollama) | Maximal privacy, scores 5/5, zero ongoing cost |
| Have a GPU but not technical | Local-first packaged app (Ember) | Same architecture, none of the assembly |
| No GPU, want it now | No-sell zero-retention hosted (Freya) | B+ posture, instant access, no hardware |
| No GPU but privacy-maximalist | Cheap used GPU + local | A used RTX 3060 12GB or 3090 runs solid companions |
| Just experimenting, low stakes | Any reputable paid app | But don’t type anything you’d hate to see leaked |
On hardware: an 8GB card runs solid 7–8B models at a useful quantization like Q4_K_M; more VRAM means bigger, smarter models. If you’re sizing a machine, how much VRAM you need for a local AI companion and do I need a GPU for an AI companion walk through the real numbers. No GPU at all? Run local AI without a GPU covers the CPU-only fallback (slower, but private).
How to verify any app’s grade yourself
Don’t take my table on faith — or anyone’s. Grade any companion app yourself in about ten minutes:
- Find where inference happens. If it’s their server, rubric point #1 is gone. The only way to score it is local. You can confirm a local app by disconnecting from the internet and watching it keep working.
- Search the policy for “train” and “improve.” Use your browser’s find function on their privacy policy. No explicit “we do not train on your conversations”? Score it zero.
- Search for “delete” and “backup.” A delete process that never mentions backups or logs isn’t real deletion.
- Search for “third parties,” “partners,” “advertising,” “subprocessors.” Each named category is a routing point. Long lists drag the grade down.
- Check signup. Email only is one thing; phone, payment, or — red alert — a selfie/ID upload is identity escalation.
- Search the app name + “breach” or “leak.” Past incidents and the response to them are signal. Silence after an incident is its own answer.
If you want to go deeper on the principles, our AI data privacy guide lays out how to reason about all of this from first principles.
Final ranking and recommendation
Here’s the whole article in one ladder, best to worst on privacy:
- Local-first (Ember / Ollama) — A. The only 5/5. Nothing uploads, so there’s nothing to breach, train on, or subpoena. Costs you hardware and a one-time setup.
- No-sell zero-retention hosted (Freya) — B+. The best cloud posture possible: no retention, no selling, minimal subprocessors. Still trust, not proof, but a categorically better default than the field.
- Disciplined mainstream companions (Kindroid, Nomi) — B / B−. Reasonable engineering, but read the current terms; the cloud ceiling still applies.
- Large-platform companions (Replika, Character.AI, Candy AI) — C+. Fine security, weaker privacy defaults; assume retention and read the training clause carefully.
- Third-party-routing front-ends (Janitor AI) — C / C−. Your privacy is whatever the backend gives you.
- “Free” ad-supported apps — D / F. If you’re not paying, your data probably is.
The recommendation is simple and falls straight out of the rubric. If you have a capable PC and a free afternoon, go local — it’s the only choice where privacy is a fact of the architecture rather than a line in a policy you can’t audit. If you don’t have the hardware or the patience, pick a hosted option that commits in writing to no retention and no selling, and accept that you’re trading some control for instant access.
If you want the version that physically cannot leak, Ember runs an uncensored companion entirely on your own machine; if you’d rather skip the hardware and start talking in seconds, Freya is the no-setup hosted option built on the no-sell, zero-retention posture this whole grade rewards.