How PIIGhost compares¶

This table is not neutral: the rows are the capabilities PIIGhost was built for, so it naturally comes out ahead. It is here to show what most other tools leave out, not to declare a winner. For a job PIIGhost is not built for, such as releasing a whole dataset, a k-anonymity tool is the right choice.

Capability	PIIGhost	Presidio	LangChain PII	Cloud (AWS/Azure)	Google DLP	pii-redactor
Detection	regex / NER / LLM	NER + regex + rules + checksum	regex + validators	ML/NER	ML + infoTypes	regex + NER
PII handling	reversible token (cache/DB)	mask / token	mask / hash	mask	crypto token (stateless)	reversible token (vault)
Transparent restore for the user	✅	⚠️ manual (`decrypt`)	❌	❌	⚠️ re-id via API	✅
Consistent across a conversation	✅ per thread	❌	❌	❌	✅ deterministic	✅ per session
Tool boundary (tool gets the real value, LLM gets the token)	✅	❌	✅	❌	❌	❌
Streaming (token-by-token restore)	✅	❌	✅	❌	❌	✅
Configurable post-detection stages (linking, fuzzy, expansion, guard)	✅	⚠️ operators only	❌	❌	⚠️ transforms	❌
Unit processed	text / conversation	text	text / conversation	text / documents	text / dataset	text / conversation
Self-hosted (OSS)	✅	✅	✅	❌ cloud	❌ cloud	✅
License	MIT	MIT	MIT	Commercial	Commercial	MIT

Notes: LangChain PII here is the Python PIIMiddleware. The JS piiRedactionMiddleware is the opposite trade-off, reversible but no streaming. Cloud groups AWS Comprehend and Azure AI Language, and Azure's Conversation mode only detects, it does not restore.

Detection-only models (spaCy, GLiNER, Piiranha) and dataset anonymizers (ARX, Amnesia) sit in a different category. The first only find PII, the second transform a whole tabular dataset with k-anonymity or differential privacy, so they are not de-identifying a live conversation.

See Limitations for what PIIGhost does not do and the trade-offs behind these choices.