open-source
anonym.digital vs spaCy
Side-by-side comparison of PII anonymization capabilities, updated March 2026.
Feature Comparison
| Feature | anonym.digital | spaCy |
|---|---|---|
| Entity Types | 285+ | 4–18 (NER) |
| Languages | 48 | 25 |
| Detection | Hybrid (spaCy + Stanza + XLM-RoBERTa + regex) | CNN / Transformer NER |
| Methods | Replace, Mask, Redact, Hash, Encrypt, Custom | N/A |
| Deployment | Web, Desktop, Office, Chrome, MCP, API, LibreOffice | Python library, Docker |
| File Formats | Text, PDF, DOCX, XLSX, CSV, Images, JSON | Text |
| Compliance | GDPR, HIPAA, CCPA, FOIA, PCI-DSS, ISO 27001 | None listed |
| Air-Gap | Yes (Desktop App) | Yes |
| Pricing | Free tier (200 tokens/mo) | Free ($0) |
| Zero-Knowledge | Yes (Argon2id + AES-256-GCM) | No |
spaCy Strengths
- Industry standard for production NLP
- Excellent speed/accuracy trade-off
- 25+ language models
- Used as Presidio detection backend
- Well-documented with commercial support
spaCy Limitations
- NER only — zero anonymization capability
- Entity types are NER labels, not PII-specific
- No regex/pattern matching built-in
- Text-only input (no PDF/DOCX/images)
- Requires building complete pipeline
Why Choose anonym.digital
- 285+ entity types vs 4–18 (NER)
- 48 languages vs 25
- 7 platforms (Web, Desktop, Office, Chrome, LibreOffice, MCP, API)
- Zero-knowledge encryption — vendor cannot access your data
- Free tier available — no credit card required