# Limina on-premises Guide

A source-qualified guide to when Limina on-premises fits the locally deployable text redaction job.

Limina on-premises is the commercial local-deployment option for organizations that want vendor-supported de-identification.

Site: Field Guides
Category: Software
Version: 2026-10-18.1
Published: 2026-10-18
Updated: 2026-10-18
Freshness: Checked 2026-10-18 against the cited sources. Official product claims, independent review/community signals, and evidence limits are reported; no hands-on benchmark, installation test, or privacy audit is claimed.
Tags: software, privacy, pii, redaction, limina-local-redaction
Canonical: https://fieldguides.co/g/limina-local-redaction
Markdown: https://fieldguides.co/g/limina-local-redaction.md
Versioned Markdown: https://fieldguides.co/g/limina-local-redaction/2026-10-18.1.md
JSON: https://fieldguides.co/api/guides/limina-local-redaction

## Quick Read

- Verdict: Choose it for supported on-premises de-identification. - Limina on-premises is a commercial local-deployment option for organizations that want vendor-supported de-identification. Local availability and commercial terms require a vendor agreement.
- Workflow: De-identify text within an agreed local deployment. - Confirm the on-premises arrangement with Limina, then process representative text and review the detected sensitive spans. Check coverage against your own data before relying on the output.
- Boundary: Check the limit before committing - The key constraint is Local availability and commercial terms require vendor agreement. No source here proves a universal performance, privacy, or reliability outcome.

## Decision

**Choose Limina on-premises when its documented boundary matches the job.**

Limina on-premises is a commercial local-deployment option for organizations that want vendor-supported de-identification. The cited sources describe it as a locally deployable redaction option. Choose it when vendor-supported local deployment is the job. Local availability and commercial terms require a vendor agreement.

## Setup

**Start by confirming deployment and the data path.**

Confirm this setup detail before you build a routine: Container in customer on-premises environment or VPC. Then verify the processing boundary — Commercial entity detection and de-identification engine — and keep the first sample low-risk until the account, device, or runtime behavior matches the source description.

## Workflow

**Workflow: detect and de-identify sensitive data; this comparison covers text.**

Use Limina on-premises to detect and de-identify sensitive data; this comparison covers text. Customization is described as enterprise model-version pinning and guided integration/tuning. Keep the workflow small enough to review its output and control settings; the cited sources do not establish a universal quality or speed result.

## Alternatives

**Compare Limina on-premises by the constraint that decides the work.**

The five-guide table retains the current guide and four siblings in the locally deployable text redaction group. Values come from the cited source cells; an Unknown cell means the sources did not support a stronger statement.

| Guide | Workflow | Deployment | Input | Entry cost | Method | Customization | Limitation |
| --- | --- | --- | --- | --- | --- | --- | --- |
| OpenAI Privacy Filter | Model-based text redaction with a local CLI and Python API. | Local CPU or GPU; checkpoints downloaded if absent. | Text strings, files and pipes. | Open repository; compute and operational costs remain. | Token classification with span decoding. | Runtime precision/recall presets, evaluation and fine-tuning commands. | Validate detection on the target corpus; community scores change with matching criteria. |
| Presidio | PII identification followed by configurable anonymization. | Python module or self-hosted HTTP service/container. | Text and detected entity spans. | Open-source deployment; self-hosting/integration cost separate. | Analyzer plus separate anonymizer operators. | Replace, mask, redact or encrypt; reversible operators need corresponding metadata and keys. | Anonymizer acts on supplied detections; missing detections are not repaired by replacement. |
| scrubadub | Replace personally identifiable spans in free text. | Python package invoked inside local scripts. | Free-text strings. | Apache-2.0 software; dependency and operation costs separate. | Detectors identify spans; postprocessors control replacement. | Optional address, spaCy and Stanford detector packages; locale configuration. | Extra detector dependencies are needed for some entity types. |
| Limina on-premises | Detect and de-identify sensitive data; this comparison covers text. | Container in customer on-premises environment or VPC. | Text; product also supports documents, images and audio. | Enterprise custom pricing; free hosted Starter is not the local-deployment price. | Commercial entity detection and de-identification engine. | Enterprise model-version pinning and guided integration/tuning. | Local availability and commercial terms require vendor agreement. |
| Philter UCSF | Remove protected health information from clinical plain text. | Local Python command-line package. | Plain-text file or directory of clinical notes. | BSD-3-Clause software; integration and compute separate. | Configurable filtering pipeline with optional evaluation annotations. | Filter configuration and input/output paths. | Clinical-domain focus; general chat/email quality is not established. |

## Review signal

**Independent context for Limina on-premises is bounded.**

The bounded cited sources contain no independent current-version review or community evaluation for Limina on-premises. Its fit is consequently a documented capability read, with no tested ranking attached.

- No independent current-version review or community evaluation was included for Limina on-premises among these cited sources.
- Treat official capability statements as the decision input and run your own representative check before a consequential rollout.

## Caveats

**Keep the source boundary visible while you decide.**

The current cited cost statement is: Enterprise custom pricing; free hosted Starter is not the local-deployment price. Privacy or processing control is described as: Privacy controls are not fully described in the cited sources. The principal product boundary is: Local availability and commercial terms require vendor agreement. These are source-qualified statements, not an installation result, benchmark, security audit, or retention guarantee.

- No independent current-version benchmark or installed container verification.
- Underlying detector implementation and exact local hardware requirements not captured.

## First check

**Make the first adoption check specific to Limina on-premises.**

Run a labeled sample from the target corpus through detection and replacement, then inspect misses, over-redaction, and logs before sending real data through the pipeline.

## Claim Table

| Where | Claim | Confidence | Source | Caveat |
| --- | --- | --- | --- | --- |
| Workflow | Detect and de-identify sensitive data; this comparison covers text. | High | Limina (limina-plans) | Source-qualified product description; no independent performance test was run. |
| Setup | Container in customer on-premises environment or VPC. | High | Limina (limina-plans) | Source-qualified product description; no independent performance test was run. |
| Setup | Text; product also supports documents, images and audio. | High | Limina (limina-plans) | Source-qualified product description; no independent performance test was run. |
| Decision | Enterprise custom pricing; free hosted Starter is not the local-deployment price. | High | Limina (limina-plans) | Observed source wording; regional pricing, billing cadence, and future plan changes are not inferred. |
| Workflow | Commercial entity detection and de-identification engine. | High | Limina (limina-plans) | Source-qualified product description; no independent performance test was run. |
| Workflow | Enterprise model-version pinning and guided integration/tuning. | High | Limina (limina-plans) | Source-qualified product description; no independent performance test was run. |
| Caveats | Local availability and commercial terms require vendor agreement. | High | Limina (limina-plans) | Do not infer that the free API tier includes the container. |

## Sources

- [Privacy Filter repository and CLI](https://github.com/openai/privacy-filter) - OpenAI; Official; accessed 2026-10-18
- [Presidio overview](https://presidio.dataprivacystack.org/) - Data Privacy Stack; Official; accessed 2026-10-18
- [Presidio Anonymizer](https://presidio.dataprivacystack.org/anonymizer/) - Data Privacy Stack; Official; accessed 2026-10-18
- [scrubadub README and license](https://github.com/LeapBeyond/scrubadub) - LeapBeyond; Official; accessed 2026-10-18
- [scrubadub stable documentation](https://scrubadub.readthedocs.io/en/stable/) - scrubadub maintainers; Official; accessed 2026-10-18
- [Pricing and deployment FAQ](https://www.getlimina.ai/en/pricing) - Limina; Official; accessed 2026-10-18
- [Philter UCSF README](https://github.com/BCHSI/philter-ucsf) - UCSF / BCHSI; Official; accessed 2026-10-18
- [GLiNER large-v2.1 vs Privacy Filter case study](https://github.com/gauravvij/pii-filter-model-eval) - Gaurav Vij / GitHub; Community; accessed 2026-10-18
- [A certified de-identification system for all clinical text documents](https://pmc.ncbi.nlm.nih.gov/articles/PMC10320112/) - UCSF authors / JAMIA Open; Review; accessed 2026-10-18

## Agent Retrieval Notes

- This Markdown is generated from the same structured guide package as the public HTML page.
- MCP tools return Markdown text by default to avoid high-token JSON envelopes.
- Agent site map: https://fieldguides.co/llms.txt