Frequently Asked Questions

The free tier provides functional PII anonymization with no credit card required. The €3/month Basic plan covers most SMB use cases. The €15/month Pro plan handles high-volume processing. No six-figure contract, no implementation fees, no vendor lock-in. Hosting on Hetzner's ISO 27001-certified data centers and GDPR-aligned processing come at SMB-friendly prices; anonym.legal itself holds no ISO 27001 certificate. Example: A 5-person legal tech startup needs to anonymize client intake forms before logging them in their CRM. They cannot afford $30K/year enterprise tools. anonym.legal's free tier lets them evaluate on real documents. As they scale to 50 clients, the €15/month Pro plan adds 4,000 tokens per cycle — total annual cost €180 vs. $30,000 for alternatives.

anonym.legal is built on the Presidio engine but delivered as a fully managed SaaS and desktop product. Zero setup, zero DevOps, zero dependency management. The same ML accuracy (Presidio + XLM-RoBERTa enhancement) is available at €3/month. Users get Presidio-level detection without touching a terminal. Example: A small HR consulting firm wants to anonymize candidate CVs before sharing with clients. Their team has no engineers. Presidio setup is impossible without hiring a contractor (€2,000-5,000). anonym.legal Pro at €180/year provides the same ML accuracy through a web interface their HR team can use immediately.

The perpetually free tier (not a trial) provides real anonymization capability. For NGOs, academic institutions, and public interest organizations, the free tier covers foundational use cases. The €3/month Basic plan is accessible even on shoestring budgets. EU data residency and GDPR compliance ensure the tool itself meets the regulatory requirements these organizations face. Example: A refugee support NGO in Germany processes intake interviews containing names, nationalities, family details, and medical information. GDPR compliance is mandatory but their tech budget is €0. anonym.legal's free tier allows their caseworkers to anonymize case files before sharing with partner organizations, achieving GDPR compliance at zero cost.

The token-based pricing model (Free: 200 tokens, Basic: €3, Pro: €15, Business: €29) is specifically designed for this segment. A solo lawyer doing occasional document redaction uses the Basic plan at €3/month. A small law firm with regular document processing uses the Business plan at €29/month. This is 30-100x less expensive than enterprise alternatives.

The free tier with token allocation covers light freelance use at zero cost. The €3/month Basic plan serves most freelance data work. The token model is transparent — users understand exactly what they're paying for. No annual commitments, no minimum seats. Example: A freelance GDPR consultant processes 20-30 client document sets per month, each requiring anonymization before sharing findings. At €3/month (Basic), total annual cost is €36. The alternative — a per-seat enterprise tool — would require convincing each client to purchase their own license, creating friction in every engagement.

All pricing is publicly listed on the pricing page. Users can sign up for the free tier instantly, test the product fully, and upgrade without ever talking to a salesperson. No "contact sales" gate. Token allocation is clearly explained. This self-serve model is particularly appealing to developer and technical buyer audiences who distrust opaque pricing. Example: A compliance manager at a mid-size fintech needs to evaluate 5 PII tools in one week. Three require "contact sales" — they're immediately deprioritized. anonym.legal is on the short list because the manager can sign up, test on real data, and confirm the tool works in under an hour. Transparent pricing at €15/month closes the evaluation without procurement delays.

Batch processing (up to 20 files per batch on Business) with consistent anonymization settings. The Redact method (black bar replacement) matches FOIA redaction requirements. 273 entity types cover PII subject to Exemptions 6 and 7C. Presets allow teams to define standard FOIA redaction configurations once and apply consistently. Example: A county government receives a FOIA request for 2,500 email records from a city council investigation. The legal team uploads all 2,500 files to anonym.legal, applies a saved "FOIA Exemption 6" preset, and processes the entire batch.

Batch processing handles the redaction phase of DSAR responses. Upload all documents extracted from internal systems, apply consistent PII redaction settings, and produce clean output for the data subject. The Encrypt method (rather than Redact) can be used internally to preserve reversibility while the Redact method produces the final customer-facing response. Audit trails support compliance documentation. Example: A European e-commerce platform receives 200 DSARs per month. Each request involves 15-30 documents from order history, support tickets, and account records containing third-party customer names that must be redacted before delivery. Batch processing all 3,000-6,000 monthly documents takes 2-4 hours vs. 3 FTE working full-time manually. Annual savings: approximately €180,000 in labor costs.

Batch processing with healthcare-specific entity types including medical record numbers, SSNs, dates (HIPAA restricts all dates except year), geographic subdivisions smaller than state, phone numbers, fax numbers, email addresses, and account numbers. 273 entity types include all 18 HIPAA Safe Harbor categories. Processing records in batches within your plan's limits, large research datasets can be de-identified systematically. Example: An academic medical center's IRB-approved research project requires de-identification of 200,000 discharge records for a readmission prediction ML model. Using anonym.legal's batch processing in sequential batches, the full dataset is processed in under a week. Total tool cost: €180/year Pro plan. Alternative commercial HIPAA de-identification tool: $120,000/year. The research proceeds with a $119,820 annual savings.

Batch processing within your plan's limits with 273 entity types covers most e-discovery PII scenarios. The Redact method produces court-admissible redacted output. For very large matters (50,000+ documents), documents can be processed in sequential batches. Example: A litigation support specialist at a law firm uses anonym.legal to pre-screen e-discovery document sets before attorney review. The batch flags documents containing PII. Attorneys review only the flagged documents for context-specific redaction decisions.

Batch processing of CSV and JSON files (native data science formats) with 273 entity types applied automatically. Upload a dataset, select anonymization settings, download the anonymized version. The Replace method substitutes PII with realistic fake data, preserving dataset utility for ML training. The Encrypt method preserves reversibility for cases where the original data is needed later. No code required. Example: A healthcare AI company's data science team needs to anonymize 8,000 patient records before their US team can access them from the EU office (Schrems II cross-border restriction). Batch processing produces an anonymized dataset in 45 minutes vs. 2-3 days of custom Python scripting. The DPO approves the output, data sharing proceeds legally, and the ML timeline stays on track.

Desktop Application batch processing handles 5, 10 or 20 files per batch (Basic, Pro, Business) with parallel execution (1-5 concurrent processes). Mixed format support (PDF, DOCX, XLSX, TXT, CSV, JSON, XML) in single batch. ZIP packaging of processed files. CSV/JSON export with per-file processing metadata (entities found, methods applied, processing time). Progress tracking with error handling for corrupted files.

Batch processing supports CSV, JSON, and XML formats with consistent PII detection across all files in a batch. Processing metadata export (CSV/JSON) provides the data lineage report that compliance teams need. The same Presidio-based engine across all platforms ensures consistency between manual review (web/desktop) and automated batch processing.