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PII GUI vs Rampart

Which one should you try?

PII GUI

Free

Best for Users needing offline PII redaction

Visit PII GUI See full review

Rampart

Free

Best for Developers building privacy-preserving AI interfaces

Visit Rampart See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

PII GUI

PII GUI

The app runs detection locally using on-device models, so nothing is uploaded at any point — no sign-up, no server round-trip, no cloud dependency. You review every flagged item in context before committing to a redaction, which means you catch the false positives before they become permanent holes in a legal document. Custom regex lets you add patterns the model won't know: internal case IDs, account number formats, bespoke identifiers. The export produces a PDF with sensitive text actually gone, not layered over. Where it breaks: single-file, single-session workflow with no batch processing described in the docs, so teams processing hundreds of support logs daily will hit a throughput ceiling fast.

Rampart

Rampart

Rampart runs a two-layer pipeline entirely in the browser: a 14.7 MB ONNX token-classification model from Hugging Face combined with a deterministic recognizer layer that catches what the model misses. Nothing leaves the client unredacted — the architecture makes server-side PII exposure structurally impossible, not just policy-dependent. The npm package ships as a complete, reproducible artifact, so your redaction behavior is auditable and consistent across builds. The ceiling arrives when your entity types fall outside what the bundled model was trained to recognize — at that point you are retraining or replacing the model, not tweaking a config. Teams needing real-time redaction across high-volume server-side pipelines will hit the browser-only constraint immediately.

AttributePII GUIRampart
PricingFreeFree
Pricing checkedNot checked yetNot checked yet
Free trialNot statedNot stated
Open sourceYesYes
Has APINoNo
Self-hosted optionYesYes
PlatformsMac, Windows, LinuxBrowser, Node.js
Pros
  • On-device detection with local models, so documents never leave the machine — which means you can process medical records or legal files that contractually cannot touch a third-party server.
  • Inline review before any redaction is committed, so you catch the false positives that a blind auto-redact would permanently remove from a contract.
  • Custom regex support for account numbers, case IDs, and proprietary identifiers, so the model's blind spots don't become your compliance gaps.
  • Export produces PDFs with text genuinely removed rather than covered, so a downstream recipient cannot recover the original content by manipulating the file.
  • No account, no sign-up, and no trial expiry, so the tool is available when you need it without an approval cycle or a billing conversation.
  • Client-side ONNX inference means PII never leaves the browser unredacted, so a misconfigured server filter or a mid-request failure cannot expose raw user text to your LLM provider.
  • Defense-in-depth pipeline — model layer plus deterministic recognizer — so structured entities like emails and phone numbers are caught even when the token classifier is uncertain, reducing the false-negative surface area compared to single-pass approaches.
  • Ships as a versioned npm package with an eval suite and bundled model weights, so redaction behavior is reproducible across builds and auditable by commit — which matters when a compliance reviewer asks what changed between releases.
  • Open-source with MIT or equivalent license (per the repository), so your legal team can read the full implementation rather than trusting a vendor's privacy attestation on a black-box API.
  • Hugging Face model loading with an in-repository fallback, so air-gapped or reproducibility-sensitive deployments do not depend on an external CDN staying up.
Cons
  • No batch processing is described anywhere in the docs or page content — the workflow is one document opened and reviewed at a time. A team processing hundreds of support logs daily will be clicking through files manually, and at that volume they move to a scripted pipeline built on an NLP library like spaCy or Presidio instead.
  • No API surface is available, so redaction cannot be inserted into an automated document ingestion workflow. Any team that needs redaction to happen programmatically — before files hit a storage bucket, for example — cannot use this tool as-is and will need a self-hosted server-side solution.
  • The local model downloads on first use, which means the first run on an air-gapped machine or a machine with restricted outbound access requires planning. The docs describe it as a one-time download, but teams in strict network-controlled environments need to account for that step.
  • The pipeline is architected for browser execution — teams running redaction server-side in Node.js, Python, or a backend compliance layer have no supported path and need a different tool entirely.
  • The bundled model covers the entity types it was trained on; when your domain introduces entity patterns outside that distribution — internal employee IDs, proprietary product codes, jurisdiction-specific ID formats — the model does not adapt without retraining, and the deterministic layer only catches patterns explicitly coded into it.
  • At 14.7 MB, the ONNX model adds a non-trivial initial load cost in browser contexts; applications targeting low-bandwidth users or requiring sub-second first-interaction readiness need to measure this against their performance budget before committing.
  • No API, no hosted option, and no server-side SDK means teams that want managed PII redaction with SLA guarantees or audit logging infrastructure will switch to a commercial data-loss-prevention service — Rampart's local-first design is exactly what those teams cannot use.

Made up your mind?

PII GUI

Free

Best for Users needing offline PII redaction

Visit PII GUI See full review

Rampart

Free

Best for Developers building privacy-preserving AI interfaces

Visit Rampart See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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