Note: Private AI officially rebranded to Limina AI (getlimina.ai) in March 2026. The company, product line, and technology are unchanged — privateai.com now redirects to the new domain. We refer to it as "Private AI" below since that is the name most readers searching for a comparison will recognize.
Feature comparison
Competitor data from Private AI / Limina AI public documentation, 2026 — verify before relying.
| Feature | anonym.plus | Private AI |
|---|---|---|
| Data leaves your device | Never — 100% on-device, by default, on the single machine running it | Depends on deployment: the container can run in your own VPC or on-prem (data stays inside your infrastructure, but still travels over your internal network to reach the API), or you can call Private AI's hosted cloud API, in which case documents leave to their servers |
| Deployment | Single installable desktop app (Windows/macOS/Linux) | Docker container you deploy yourself, or a hosted REST API/SDK — built for developers to integrate into an application, not to install and use directly |
| Offline / air-gap | Yes, by default — verified with no internet connection at all | The container can in principle run without outbound internet, but it is engineered as a network service that other applications call over the network — not a standalone offline tool for a non-developer to run on their own laptop |
| Entity types | 340+ built-in | 50+ entity types spanning PII, PHI, and PCI, across 52 supported languages, per Private AI's own documentation |
| Encryption | Local AES-256-GCM with an offline key vault | Not primarily an encryption product — offers de-identification operators instead (mask, marker/redact, pseudonymize, reversible tokenization, and synthetic-data replacement), with authorized "re-hydration" of tokenized values |
| Account / login required | No account for core use — internet is needed only once, for initial license activation | Yes — an API key and developer account are required to call the service or license the container |
| Pricing model | One-time license, no subscription | Not publicly listed — enterprise/usage-based licensing, quoted through a sales conversation (also available via AWS/Azure marketplace listings) |
| Setup / DevOps | None — download, install, run. Built on Presidio + spaCy under the hood | Requires integrating an API/SDK into your own application and, for self-hosting, provisioning and operating the container (compute, networking, upgrades) |
| Compliance angle | On-device processing removes the processor/transfer question entirely — because documents are processed locally, there is no egress to any third party, which you can verify by running the app with the network disconnected | Markets itself as helping meet GDPR/HIPAA/PCI-DSS obligations by keeping de-identification inside the customer's own VPC or on-prem environment when self-hosted — a genuinely privacy-conscious architecture, but your organization remains the operator and controller of that infrastructure |
| Data residency / cross-border transfer | Not applicable — no document data is transmitted, so GDPR Art. 44-49 transfer-mechanism questions do not arise | Relevant whenever the hosted cloud API is used, or when a self-hosted container's VPC sits in a different region/jurisdiction than the data subjects |
| Billing granularity | One-time per-seat license; cost is fixed regardless of document volume | Usage-based/enterprise licensing — cost scales with API calls, containers, or data volume, quoted through sales |
Private AI strengths
- Broad entity coverage across PII, PHI, and PCI categories (50+ types per its own documentation), with multilingual support across 52 languages including code-switched text
- Self-hosted container option means enterprises can keep de-identification inside their own VPC or on-prem network, rather than sending data to a third-party API
- Purpose-built products for LLM workflows — "PrivateGPT Headless" redacts PII from prompts before they reach ChatGPT or similar models and re-populates it in the response, useful for teams wiring PII protection into an AI product
- Flexible de-identification operators (mask, marker, pseudonymize, reversible tokenization, synthetic replacement) that go beyond simple redaction, including authorized re-identification for downstream workflows
- Cloud-marketplace availability (AWS, Azure) simplifies procurement for teams already committed to those ecosystems
Private AI limitations
- Built for developers, not end users — there is no packaged desktop application; every user-facing workflow (upload a document, redact it, get a file back) has to be built on top of the API by your own engineering team
- Self-hosting the container still means running and maintaining server infrastructure — provisioning, networking, upgrades, and monitoring are on you, not eliminated the way they are with a local desktop app
- Requires an API key/developer account and, in most integrations, a network call from the calling application to the container or cloud endpoint — genuinely offline, single-machine, zero-network use is not the primary design target
- Pricing is not public — teams have to go through a sales process to learn what it costs, which slows down evaluation for smaller teams or individuals
- The recent rebrand to Limina AI (March 2026) means some existing integrations, bookmarks, and documentation links tied to the "Private AI" / privateai.com name are in transition
Why choose anonym.plus
- 100% on-device processing — no document content ever leaves your machine, verifiable by disconnecting the network and running it anyway
- No API keys, no container to provision, no server to operate — anonym.plus is a no-code desktop app built for people who need to redact a document today, not a development team with weeks to spend on integration
- 340+ PII entity types detected out of the box, no custom recognizer or integration code required
- Local AES-256-GCM encryption with an offline key vault — keys never touch a server
- Zero outbound network calls during document processing — verifiable by running the app on an air-gapped machine with the network disconnected
- One-time license, no subscription and no usage-based billing to negotiate — internet is needed only once, for initial activation
How the data actually flows
The architectural difference between the two products is not a matter of degree — it's a difference in where the computation happens. Private AI is architected as a network service: a piece of software you call, not a piece of software you run. anonym.plus is architected as a local process: a piece of software that runs entirely inside your machine's memory space, with no network dependency for its core function.
Private AI's request/response flow
- Your application (or a developer on your team) sends a document or text payload to Private AI's API — either the hosted cloud endpoint or a self-hosted container running in your own VPC/on-prem network
- If you use the hosted endpoint, that payload transits the public internet to reach Private AI's infrastructure; if self-hosted, it still crosses your internal network to reach the container process
- The de-identification engine detects entities, applies the requested operator (mask, redact, pseudonymize, reversible tokenization, or synthetic replacement), and returns a response
- For reversible operators, a token-to-value mapping is retained so an authorized later call can "re-hydrate" the original value — meaning the original PII is stored somewhere, not just discarded
anonym.plus's on-device flow
- You open a file directly in the desktop app on your own machine — no upload step exists because there is nowhere to upload to
- The same class of NLP entity-detection pipeline that cloud services rely on (built on Microsoft Presidio + spaCy) runs inside the local application process
- Detected entities across 340+ types are redacted, masked, or replaced directly in the file, in local memory, without ever serializing the document to a network socket
- Any encryption uses a local AES-256-GCM key vault stored on the device — keys are generated, stored, and used locally, and are never transmitted to a server
The practical test is simple: disconnect the network entirely and run each product. anonym.plus keeps working, because it was never calling out. Private AI's hosted API stops working immediately, and even its self-hosted container is engineered to be reached over a network by other software — it is not designed to be launched and used directly by a person on an air-gapped laptop.
When Private AI makes sense vs when anonym.plus wins
Neither product is "better" in the abstract — they're built for different operators solving different problems.
Private AI is the right tool when
- An engineering team needs to redact PII automatically inside an existing pipeline — for example, scrubbing large volumes of inbound support tickets or chat logs before they reach an LLM or analytics store
- The workflow needs coverage across 52 languages, including code-switched text, at a scale no single desktop user would process by hand
- Downstream systems need authorized re-identification — pseudonymized or tokenized values must later be reversed back to the original value for a legitimate business process
- The organization already operates cloud infrastructure and DevOps capacity, and usage-based billing tied to API call volume is an acceptable cost model
anonym.plus is the right tool when
- A single professional — a lawyer, paralegal, clinician, or HR manager — needs to anonymize a document today, with no engineering team or API integration available
- The work happens on an air-gapped or otherwise network-restricted machine, where calling any external API is against policy, not just inconvenient
- The organization wants a fixed, one-time cost rather than a metered bill that grows with document volume or API calls
- The requirement is genuinely "no data leaves this machine," not "data leaves this machine but stays inside our cloud account"
The key difference is not detection quality — both approaches build on well-established NLP entity-recognition techniques. The key difference is where the computation happens: inside a process you control completely, or inside infrastructure — yours or Private AI's — that the data has to reach over a network first.
Data residency, cross-border transfer, and Schrems II
Cross-border transfer of personal data out of the EU/EEA is governed by GDPR Articles 44-49, which require an approved transfer mechanism — an adequacy decision, Standard Contractual Clauses, or another recognized safeguard — before personal data can lawfully leave the EEA. This is defined as the "transfer question," and it only arises when personal data is actually transmitted somewhere.
In Schrems II (Court of Justice of the European Union, Case C-311/18, judgment of 16 July 2020), the Court invalidated the EU-US Privacy Shield framework, holding that U.S. surveillance law did not guarantee EU data subjects protection essentially equivalent to the GDPR. The ruling significantly raised the compliance burden for any organization transferring personal data to U.S.-based processing infrastructure, requiring supplementary safeguards on top of Standard Contractual Clauses.
Using Private AI's hosted cloud API means document content is transmitted to Private AI's processing infrastructure — precisely the kind of transfer Articles 44-49 and Schrems II address. Self-hosting the container inside your own VPC reduces but does not eliminate this: your organization, as controller, still has to determine whether that VPC's region and cloud provider create a cross-border transfer relative to where your data subjects are located.
anonym.plus removes the question rather than answering it: because no document content is ever transmitted anywhere, there is no transfer for Articles 44-49 to regulate. The factual answer to "does personal data leave the EEA" is no, independent of contracts, adequacy decisions, or supplementary safeguards.