OpenAI Privacy Filter alternatives — 6 tools compared (2026)

1. Microsoft Presidio Open-source

Best for: teams that need self-hosted, fully air-gapped PII redaction with custom recognizers.

• 28+ recognizer types out of the box; custom recognizer SDK for domain-specific entities
• Supports English and 14+ additional languages via spaCy language models
• Runs entirely on-premises — no data leaves your infrastructure
• Operator complexity: you manage model updates, service scaling, and infra

Deep comparison: OpenAI Privacy Filter vs Microsoft Presidio.

2. AWS Comprehend PII Cloud

Best for: teams already on AWS that need audit logs, IAM integration, and multi-language support.

• 28 entity types including passport numbers, bank account numbers, driver's licenses
• English, Spanish, French, German, Italian, Portuguese, and Japanese
• Asynchronous batch jobs over S3 for large corpora
• CloudTrail + CloudWatch integration for compliance audit trails
• Custom entity recognizer training on domain vocabulary

Deep comparison: OpenAI Privacy Filter vs AWS Comprehend PII.

3. Nightfall AI Enterprise

Best for: large organizations that need DLP (Data Loss Prevention) across cloud storage, SaaS apps, and code repositories — not just API text processing.

• 150+ pre-built detectors covering PII, PCI, PHI, secrets, and credentials
• Integrations with Slack, GitHub, Google Drive, Confluence, Jira, S3
• Policy-based alerts and automated remediation workflows
• Priced per seat or data volume; typically $500–$5,000/month for mid-market teams

When to avoid: Nightfall is overkill (and overpriced) if you just need to scrub PII from text programmatically before sending it to an LLM.

4. spaCy NER Open-source

Best for: developers who already use Python for NLP and want fine-grained control over the entity extraction pipeline.

• Named entity recognition for PERSON, ORG, GPE, DATE, and more (not PII-specific)
• 60+ language models; best accuracy in English, German, French, Spanish
• Training your own NER model on domain-specific PII is documented and well-supported
• No out-of-the-box support for EMAIL, SSN, or PHONE — requires custom patterns or a complementary library

import spacy
nlp = spacy.load("en_core_web_trf")
doc = nlp("Send it to John Doe at john@example.com")
for ent in doc.ents:
    print(ent.text, ent.label_)

5. GLiNER Open-source

Best for: developers who need zero-shot or few-shot entity extraction with a small, fast model they can run locally.

• Generalist NER model — define entity types at inference time (no training required)
• Outperforms larger models on custom entity types with minimal examples
• Runs on CPU; ~400MB model size; suitable for edge deployment
• Less battle-tested than Presidio for structured PII (SSN format variants, IBAN)

from gliner import GLiNER
model = GLiNER.from_pretrained("urchade/gliner_mediumv2.1")
entities = model.predict_entities(
    "John called from +1-555-0123",
    labels=["PERSON", "PHONE_NUMBER"]
)
print(entities)

6. Scrubadub Open-source Free

Best for: quick Python scripts that need lightweight PII removal without infrastructure or cloud dependencies.

• Detects EMAIL, PHONE, CREDIT_CARD, DATE_OF_BIRTH, ADDRESS, NAME (via spaCy)
• Extensible with custom detectors in 20 lines of Python
• No API calls, no internet access required — runs fully offline
• Lower accuracy than LLM-based approaches for contextual PII

import scrubadub
text = "Contact John Doe at john@example.com"
print(scrubadub.clean(text))
# → "Contact {{NAME}} at {{EMAIL}}"