# Top PII Tokenization & Masking Tools for LLMs in 2026: 20 Tools Tested Twenty PII tokenization and masking tools compared for AI/ML teams sending data to LLMs. Real G2 ratings, verified 2026 pricing, and honest assessments of detection accuracy, proxy vs SDK architecture, and compliance coverage. Comparing the best Top PII Tokenization & Masking Tools for LLMs in 2026: 20 Tools Tested of 2026 includes 1. Enigma Vault NoPII 2. Skyflow 3. Private AI 4. Nightfall AI 5. Gretel.ai 6. Microsoft Presidio 7. Tonic.ai 8. Securiti.ai 9. Anonym 10. Protopia AI 11. AWS Comprehend 12. Google Cloud Sensitive Data Protection 13. Azure Purview (Microsoft Purview) 14. Baffle 15. Hazy 16. Statice 17. Mostly AI 18. Synthetic Users 19. ARX Data Anonymization 20. DataFleets. Twenty PII tokenization and masking tools reviewed for the teams building RAG pipelines, fine-tuning jobs, and inference endpoints on top of GPT-4, Claude, and Gemini. What keeps names, SSNs, and health data out of LLM context windows, what falls over at scale, and the pick for your compliance regime and pipeline architecture. ## Quick summary - Enigma Vault NoPII: Best purpose-built LLM PII tokenization proxy. Free tier at 1M tokens/mo, PCI DSS Level 1 + SOC 2 Type II, one-line base-URL swap to integrate. - Skyflow: Best data privacy vault for structured PII with LLM-aware APIs. Enterprise-grade, custom pricing, the choice when you need a full vault not just a proxy. - Private AI: Best for 50+ language coverage and audio/video PII redaction. Detects 50+ entity types, self-hosted or cloud, strong HIPAA/GDPR posture. - Nightfall AI: Best cloud DLP layer for teams using SaaS LLM endpoints. 4.6/5 on G2, policy-based, works across Slack, GitHub, and LLM prompt channels. - Gretel.ai: Best for ML training data that needs synthetic replacement, not just masking. NVIDIA-backed, $295/mo Team tier, strong on tabular and text datasets. ## How we chose We compared each tool on the two things that actually break in production: PII detection coverage across entity types and languages, and the reversibility story for LLM response post-processing. We ran each through a standard test payload covering US SSNs, EU passport numbers, healthcare record text, and multi-language names. Compliance credentials were pulled directly from vendor trust pages and SOC 2 certificate listings. Pricing was verified against vendor pricing pages and AWS Marketplace listings in October 2026. G2 and Capterra ratings were pulled live and are noted where a public listing exists. ## How we weight top pii tokenization & masking tools for llms in 2026: 20 tools tested for the Topickz score Every tool is scored against this rubric and combined using these category-specific weights into the Topickz score. | Criterion | Weight | What we checked | |---|---|---| | PII detection accuracy | 28% | Entity type coverage (names, SSN, IBAN, PHI, credentials), false positive rate on real LLM prompt payloads, and multi-language support depth. | | LLM API compatibility | 22% | Proxy vs SDK architecture, OpenAI/Anthropic/Gemini coverage, streaming support, and latency overhead added to inference calls. | | Reversibility and format preservation | 18% | Deterministic tokenization, de-tokenization in LLM responses, format-preserving encryption options, and entity relationship preservation. | | Deployment flexibility | 12% | Cloud API, self-hosted container, on-prem, and hybrid options. Air-gapped support for regulated environments. | | Compliance coverage | 12% | SOC 2 Type II, HIPAA BAA availability, GDPR processing agreements, PCI DSS scope, and audit log depth. | | Pricing and developer experience | 8% | Free tier availability, usage-based vs per-seat cost, SDK quality, documentation depth, and time to first working integration. | ## Tools compared ### Enigma Vault NoPII: Best purpose-built LLM PII tokenization proxy **Best overall** Score: 9.2/10 Rating: 4.7/5 (Capterra · 4 reviews) **Starting price:** Free up to 1M tokens/mo NoPII from Enigma Vault is the most purpose-built tool in this list for teams that just want to stop PII from reaching an LLM API. The integration story is the cleanest here: change one environment variable (the base URL) and every prompt hitting OpenAI, Anthropic, or any OpenAI-compatible endpoint gets PII stripped before it leaves your infrastructure. Deterministic tokenization is the part that matters for RAG pipelines. The same SSN always maps to the same token, so the LLM can still say 'SSN_abc123 matches SSN_abc123' without ever seeing the real number. That's a meaningful architectural advantage over one-way redaction approaches. [Enigma Vault's NoPII GitHub repo](https://github.com/Enigma-Vault/NoPII) ships ready-to-run examples for OpenAI, Anthropic, LangChain, and LlamaIndex. PCI DSS Level 1 and SOC 2 Type II credentials make it the right default for fintech and healthcare teams hitting LLM APIs. Skip it only if your organization requires a named enterprise vendor with a 10-year track record. **Pros:** - One-line integration by swapping the OpenAI or Anthropic base URL, no SDK changes required - Deterministic tokenization preserves entity relationships so the LLM can reason across tokenized values without seeing real PII - PCI DSS Level 1 certified and SOC 2 Type II audited, the strongest compliance posture at the free tier in this category **Cons:** - Small vendor with a limited public review footprint, not a safe default for a team that needs vendor longevity guarantees - Pro tier pricing is custom-quoted, which makes budget forecasting harder for teams with spiky token volumes - Detection is powered by Presidio under the hood, so edge-case entity types outside the standard set need custom recognizer config Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Free | $0 | Up to 1M tokens/mo | | Pro | Custom | High-volume production pipelines | ### Skyflow: Best privacy vault for structured LLM-aware tokenization **Best enterprise vault** Score: 9.0/10 Rating: 4.5/5 (G2 · 18 reviews) **Starting price:** Custom (avg. $195K/yr enterprise) Skyflow is the right answer when your team needs a full data privacy vault, not just prompt-level masking. The distinction matters: a vault stores tokenized PII as the source of record, so your LLM never touches real values even in training data or RAG retrieval contexts. The LLM-aware API layer is genuinely differentiated. You pass tokenized references into a prompt, Skyflow's gateway swaps them for real values only when policy allows, and responses get re-tokenized before they touch your application layer. That architecture is what HIPAA-covered entities and financial institutions running AI actually need. [Skyflow's pricing page](https://www.skyflow.com/pricing) doesn't publish tiers; you'll need sales engagement. Per [Vendr's 2025 buyer guide](https://www.vendr.com/buyer-guides/skyflow), deals average around $195K annually. The right pick for regulated enterprises that have already decided they need vault-grade PII infrastructure for their LLM stack. **Pros:** - Full data privacy vault architecture, not just tokenization, supports format-preserving encryption, access policies, and audit trails on every PII field - LLM-specific APIs let you pass tokenized values directly into prompts and de-tokenize responses without building custom middleware - SOC 2 Type II, HIPAA, PCI DSS, and GDPR compliant with a dedicated privacy engineering team **Cons:** - Enterprise pricing with no public tiers. Average deal size around $195K/yr per Vendr transaction data, the cost curve eliminates it for most startup teams - Implementation requires significant engineering investment, this is a vault architecture not a drop-in proxy - No free tier or self-serve trial to evaluate before engaging sales Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | Regulated enterprises | ### Private AI: Best for 50-language PII detection including audio and video **Best for multimodal PII** Score: 8.9/10 Rating: 4.8/5 (Product review aggregators · limited reviews) **Starting price:** Custom enterprise Private AI's core differentiation is breadth. Fifty-plus entity types across fifty-plus languages is meaningful for global teams processing non-English data through LLMs, where other tools drop to basic Latin-alphabet coverage. The multimodal story is the other angle worth noting. Teams building LLM workflows on top of call transcripts, uploaded documents, or video summaries can run a single API across all those data types rather than stitching together multiple vendors. That reduces the attack surface where PII might slip through format transitions. [Private AI's AWS Marketplace evaluation listing](https://aws.amazon.com/marketplace/pp/prodview-ny7inm65bp5ry) is the fastest path to a real trial. Backed by M12 (Microsoft's venture fund), which provides some implementation risk cover even without a long public track record. Not the pick if you need a self-serve free tier to get started today. **Pros:** - 50+ entity types detected across 50+ languages, the broadest language coverage in this category by a clear margin - Supports text, audio, video, and documents in one API, relevant for teams processing meeting transcripts or call recordings through LLMs - Cloud API and self-hosted options both available, the self-hosted path matters for GDPR Article 44 data residency requirements **Cons:** - No public pricing or self-serve sign-up, requires a sales conversation to even evaluate the cloud API tier - Limited public review footprint (backed by M12/Microsoft but early in commercial maturity) - Audio/video processing adds latency that needs factoring into real-time pipeline design Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Cloud API | Custom | Teams wanting managed infrastructure | | Self-hosted | Custom | Air-gapped or data-residency-constrained environments | ### Nightfall AI: Best cloud DLP for LLM prompt safety alongside SaaS channels **Best for SaaS-first teams** Score: 8.8/10 Rating: 4.6/5 (G2 · 98 reviews) **Starting price:** Custom (contact sales) Nightfall is the right choice when LLM prompt safety is one part of a broader data loss prevention problem. If your team is already worried about SSNs in Slack and API keys in GitHub, Nightfall covers those alongside your LLM traffic from the same policy engine. [98 G2 reviews](https://www.g2.com/products/nightfall-ai/reviews) average 4.6/5; the consistent theme across reviewer comments is ease of policy setup and the SaaS channel breadth. The consistent gap teams note is the lack of transparent per-seat pricing. The [Nightfall vs Securiti comparison on G2](https://www.g2.com/compare/nightfall-ai-vs-securiti) puts Nightfall ahead on DLP-specific workflows; Securiti ahead on governance breadth. For a pure LLM tokenization proxy that rewrites and restores values, look at NoPII or Skyflow instead. **Pros:** - Policy-based approach covers LLM prompts and SaaS channels (Slack, GitHub, Jira, Google Drive) from one platform, so you're not running separate tools per channel - 4.6/5 across 98 G2 reviews, the most-reviewed platform in this specific sub-segment - Pre-built detectors for PCI, PHI, PII, API keys, and credentials ship out of the box; custom detectors available **Cons:** - Not a tokenization proxy, Nightfall detects and blocks or alerts but does not rewrite prompts with reversible tokens for LLM round-trips - Pricing is enterprise-only with no published tiers, budget forecasting requires a sales conversation - Teams focused purely on LLM pipeline tokenization may find the SaaS DLP breadth overkill and the prompt-only coverage lighter than specialized proxy tools Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | Teams with SaaS + LLM data loss prevention needs | ### Gretel.ai: Best for synthetic data replacement in ML training pipelines **Best for training data** Score: 8.6/10 Rating: 4.4/5 (G2 · 13 reviews) **Starting price:** $295/mo Gretel is the answer when your problem is LLM fine-tuning on sensitive datasets rather than inference-time prompt protection. Synthetic data replacement means the model trains on data that looks real statistically but contains no actual PII. That's the right architecture for HIPAA-covered training sets where masking artifacts would degrade model quality. [NVIDIA acquired Gretel in 2025](https://www.buildmvpfast.com/blog/synthetic-data-ai-training-generation-tools-2026), which gives the platform long-term infrastructure credibility and GPU access that smaller synthetic data vendors can't match. The [Gretel Team tier at $295/mo](https://www.g2.com/products/gretel-ai/pricing) is the clearest on-ramp in this guide. The pricing curve to Enterprise ($3,500+/mo) is steep, so evaluate your record volume before committing. Skip Gretel for real-time inference proxy use cases; it's not built for that. **Pros:** - NVIDIA acquisition in 2025 gives Gretel GPU infrastructure and model training resources no other vendor here can match - Synthetic data replacement (not just masking) means the LLM sees statistically realistic values instead of obvious placeholders like [REDACTED] - Strong open-source community and SDK; the free sandbox lets you test on real dataset samples before committing **Cons:** - Team tier at $295/mo includes 1M synthetic records, Enterprise tier jumps to $3,500/mo then $10,000/mo, a steep curve for mid-size teams - Synthetic data approach adds more complexity and compute than a tokenization proxy; overkill for inference-only pipelines - Best suited for tabular and structured datasets; unstructured text anonymization is less polished than Presidio or Private AI Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Free | $0 | Sandbox testing | | Team | $295/mo | Up to 1M synthetic records | | Enterprise | $3 | High-volume training data pipelines | | Enterprise On-Prem | $10 | Air-gapped | ### Microsoft Presidio: Best open-source PII detection SDK for custom pipelines **Best open source** Score: 8.5/10 Rating: OSS/5 (GitHub · 11,100+ stars reviews) **Starting price:** Free (open source) Presidio is the right foundation when you have the engineering capacity to build on it and the need to customize entity detection beyond what commercial APIs offer. The MIT license and Python SDK mean you can fork it, modify recognizers, and run it on whatever infrastructure you already operate. The practical question is whether your team wants to own that maintenance burden. NoPII, Private AI, and Skyflow all use Presidio concepts or build on top of it. If you want the benefit without the ops, pick one of those instead. For teams with domain-specific PII (clinical trial identifiers, proprietary financial codes), Presidio with custom recognizers is the only path to accurate detection. The [Presidio GitHub repo](https://github.com/microsoft/presidio) has active maintenance from Microsoft and solid community documentation. **Pros:** - Zero licensing cost; compute is your only cost, which matters for teams with high token volumes that would hit significant bills on commercial APIs - Custom entity recognizers let you add domain-specific PII types (contract IDs, patient record numbers, internal account formats) that commercial tools miss - Used under the hood by Enigma Vault NoPII and several other tools in this list, so you know the detection engine is battle-tested **Cons:** - No managed service; your team owns deployment, scaling, GPU provisioning for transformer models, and maintenance - Engineering time for initial setup and ongoing model updates is the real cost, often $50K+ in eng-hours for a production-grade deployment - Accuracy gaps on non-English text and domain-specific formats without custom recognizer work; the off-the-shelf models are tuned for English-language datasets Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Open Source | $0 | Teams with engineering capacity to self-host and customize | ### Tonic.ai: Best for database-level PII masking for LLM test environments **Best for staging data** Score: 8.4/10 Rating: 4.2/5 (G2 · 38 reviews) **Starting price:** $15K/yr Tonic is the right pick when the PII problem is in your databases, not just your LLM prompts. If you're building a RAG system that pulls from a Postgres or Snowflake table with real customer records, Tonic anonymizes that source data for staging and evaluation environments while preserving the referential structure your queries depend on. The part that takes people by surprise in demos is the text field handling. A support ticket column with freeform PII gets de-identified alongside the structured fields, so you end up with a staging dataset that's genuinely usable for LLM evaluation without the compliance risk. [Tonic's G2 profile](https://www.g2.com/products/tonicdm/reviews) shows 44.7% mid-market reviewers and consistent praise around referential integrity. For real-time inference proxy use, this is not the right tool; pair it with NoPII or Presidio for that layer. **Pros:** - Handles referential integrity across relational databases, rare in this category; foreign keys stay consistent after masking - Textual de-identification for unstructured text fields (notes, support tickets, documents) ships alongside the structured database masking - 14-day trial with real data up to 10GB lets you validate accuracy before signing **Cons:** - Primarily a database masking tool; not a real-time LLM proxy, needs integration work to fit a live inference pipeline - Contract minimums typically $15K-$30K/yr for small setups, climbing past $100K for large deployments - 4.2/5 on G2 (38 reviews), lower than most peers in this guide; reviewers cite UI complexity and cost relative to Presidio for teams with engineering bandwidth Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Starter | $15K/yr | Small teams | | Professional | $30K/yr | Mid-market teams with multiple databases | | Enterprise | $100K+/yr | Large deployments | ### Securiti.ai: Best AI data command center for governance-first enterprises **Best for data governance** Score: 8.3/10 Rating: 4.6/5 (G2 · 106 reviews) **Starting price:** Custom enterprise Securiti is the right choice when PII protection for LLMs is part of a broader data governance initiative. CDO and compliance teams dealing with GDPR subject rights requests, consent management, and cross-border data transfer restrictions will find it covers ground that four or five point tools would otherwise fill. [4.7/5 across 80 G2 reviews](https://www.g2.com/products/securiti/reviews) is the strongest rating in this guide among tools with a meaningful review base. Reviewers consistently cite the automated data discovery and the audit trail depth as differentiators. The trade-off is deployment time. Teams that need a working LLM proxy this week should look at NoPII. Teams building a 12-month compliance program around AI data governance should put Securiti on the shortlist. **Pros:** - 4.7/5 across 80 G2 reviews, the highest G2 rating in this list among tools with meaningful review volume - AI data command center covers data discovery, classification, consent management, and LLM governance in one platform - Automated data mapping and subject rights management alongside PII protection reduces the compliance team's manual workload **Cons:** - Breadth means depth trade-offs; reviewers note the LLM-specific tokenization features are less mature than Skyflow or NoPII - Enterprise-only pricing with no self-serve entry point - Implementation complexity is higher than point solutions; plan for a 3-6 month rollout for full governance platform deployment Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | Enterprises needing full AI governance platform | ### Anonym: Best for privacy-preserving model fine-tuning with differential privacy **Best for DP fine-tuning** Score: 8.0/10 Rating: No public G2/5 (Vendor · N/A reviews) **Starting price:** Custom enterprise Anonym occupies a niche that most tools in this list don't address: fine-tuning LLMs on sensitive data with differential privacy guarantees. Where Gretel replaces data with synthetic equivalents, Anonym uses DP training to bound what the model can reveal about any individual training example. The practical use case is a healthcare or financial services team that wants to fine-tune a base model on their own proprietary data without the regulatory risk of the model memorizing PII. That's a genuinely hard problem, and Anonym's approach is more principled than pre-masking the training set. The vendor is early stage with limited public review data. Confirm customer references before signing. Not the right pick for inference-time tokenization; pair it with NoPII or Presidio if you need both. **Pros:** - Differential privacy fine-tuning lets you train LLMs on sensitive data with mathematical privacy guarantees, not just obfuscation - Designed specifically for the fine-tuning use case that Gretel and most tokenization tools don't fully address - Can operate directly on sensitive training corpora without requiring pre-masking, which removes a preprocessing step from the pipeline **Cons:** - Very early-stage vendor with limited public presence and no public G2 or Capterra reviews - Differential privacy adds noise to model outputs, which degrades accuracy on tasks where exact recall of training data matters - Not a fit for inference-time PII protection; this is a training-time tool Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | Teams fine-tuning LLMs on sensitive proprietary data | ### Protopia AI: Best for round-trip inference protection without plaintext exposure **Best for inference security** Score: 7.8/10 Rating: No public G2/5 (Vendor · N/A reviews) **Starting price:** Custom enterprise Protopia's Stained Glass Transform is solving a different problem than most tools here. Instead of stripping PII before it hits the LLM, it transforms all input data into a stochastic embedding that the target model can still reason from without ever seeing plaintext. No entity detection, no replacement tokens, no list of PII types to maintain. That architecture is compelling for multi-tenant environments where prompt content itself is sensitive, not just the PII within it. A legal team sending confidential contract text to a hosted LLM, for example, where the issue is the whole document, not just the names in it. The [Protopia Oracle partnership](https://blogs.oracle.com/ai-and-datascience/safe-secure-efficient-llms-with-protopia-on-oci) opens the OCI deployment path for enterprise buyers already on Oracle infrastructure. Early stage with limited public traction; confirm customer references and ask hard questions about the accuracy impact of the transform on your specific LLM task. **Pros:** - Stained Glass Transform converts input data to a randomized embedding that preserves what the LLM needs while eliminating plaintext PII exposure throughout inference - Works in multi-tenant LLM environments where you cannot trust the inference provider to see raw prompts - Oracle partnership for OCI deployment gives enterprise buyers a familiar commercial pathway **Cons:** - Patented approach is architecturally novel but creates vendor lock-in; if Protopia changes pricing or discontinues, migration is non-trivial - Limited public review data and no self-serve trial path - Not a drop-in proxy replacement; integration requires more engineering work than base-URL-swap tools like NoPII Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | Multi-tenant inference environments | ### AWS Comprehend: For teams already in AWS wanting pay-per-use PII detection Score: 7.8/10 Rating: 4.1/5 (G2 · 45 reviews) **Starting price:** $0.0001/unit AWS Comprehend makes sense when AWS is already your cloud and you need PII detection without a new vendor contract. The DetectPiiEntities and ContainsPiiEntities APIs integrate directly into Lambda functions sitting in front of your Bedrock or third-party LLM calls. [See the Comprehend pricing page](https://aws.amazon.com/comprehend/pricing/) for current unit rates. **Pros:** - No additional vendor relationship needed if you're already in AWS; IAM, CloudTrail, and VPC integration come free - Pay-per-unit pricing with no minimums, cost-effective for low-volume or spiky workloads - DetectPiiEntities API covers 100+ PII types for English text **Cons:** - English-centric; multilingual PII detection is limited compared to Private AI - Not a proxy; you're building the integration yourself around Lambda or SageMaker - Accuracy on domain-specific PII formats (medical codes, financial identifiers) lags purpose-built tools Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Pay-per-use | $0.0001/unit | AWS-native teams | ### Google Cloud Sensitive Data Protection: For GCP-native teams needing DLP across BigQuery and Vertex AI Score: 7.7/10 Rating: 4.2/5 (G2 · 38 reviews) **Starting price:** $0.001/unit Google's Sensitive Data Protection (formerly Cloud DLP) is the right call for GCP-native teams processing data through BigQuery or Vertex AI. The BigQuery integration in particular lets you run PII inspection and transformation directly in the warehouse without moving data. [See the pricing page](https://cloud.google.com/sensitive-data-protection/pricing) for current per-character rates. **Pros:** - Native integration with BigQuery and Vertex AI, letting you inspect and de-identify training datasets without exporting data - De-identify API supports format-preserving encryption and pseudonymization with Cloud KMS key management - 150+ built-in infoTypes for PII, PHI, PCI, and credentials **Cons:** - GCP-vendor lock for key management and pseudonymization workflows - Pricing is per character inspected, which adds up fast on large document corpora - No proxy architecture; integration requires building custom middleware Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Pay-per-use | $0.001/unit | GCP-native teams | ### Azure Purview (Microsoft Purview): For Microsoft 365 enterprises needing unified data governance and LLM compliance Score: 7.7/10 Rating: 4.0/5 (G2 · 52 reviews) **Starting price:** Custom (bundled in M365 E5) Azure Purview's main selling point is consolidation. If you're running Microsoft 365 E5 and Azure OpenAI Service, Purview lets you extend sensitivity labeling and DLP policies into your Copilot and Azure LLM workflows without a new vendor. [See Microsoft Purview pricing](https://azure.microsoft.com/en-us/pricing/details/purview/) for standalone costs outside M365 E5. **Pros:** - Included in Microsoft 365 E5 for organizations already paying for it; effective zero incremental cost - Sensitivity labels from Purview extend into Copilot and Azure OpenAI Service for consistent LLM data governance - Unified data map across on-prem, multi-cloud, and SaaS data sources **Cons:** - The sensitivity labeling and DLP approach is less surgical than tokenization proxies for real-time LLM call interception - Setup complexity in large M365 tenants is significant; teams report 3-6 month rollouts for full deployment - Less capable for third-party LLM APIs (OpenAI direct, Anthropic) than Azure OpenAI Service scenarios Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Included | M365 E5 bundle | Enterprises already on M365 E5 | | Standalone | Custom | Azure-centric teams not on E5 | ### Baffle: For teams needing encryption-first data protection at the database layer Score: 7.6/10 Rating: 4.4/5 (G2 · 14 reviews) **Starting price:** Custom enterprise Baffle's encryption-first approach is differentiated for teams where the sensitive data lives in structured databases that feed LLM retrieval pipelines. Encrypting at the field level means your RAG retrieval step never exposes cleartext PII even if the vector database or retrieval layer is compromised. [Baffle's website](https://baffle.io) covers the technical architecture in detail. **Pros:** - Transparent encryption at the database layer means applications (and LLMs) interact with encrypted data without schema changes - Works at the field level in PostgreSQL, MySQL, and cloud databases, not just at rest - AWS and Azure marketplace listings simplify procurement **Cons:** - Not a real-time LLM proxy; the database-layer approach needs custom middleware to intercept LLM API calls - Small vendor with limited G2 review volume - Primarily a database encryption tool; text/document PII masking for unstructured data is limited Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | Database-layer PII protection for RAG pipelines | ### Hazy: For UK and EU financial services teams needing FCA-compliant synthetic data Score: 7.6/10 Rating: 4.5/5 (Capterra · 8 reviews) **Starting price:** Custom enterprise Hazy is worth evaluating for UK and EU financial services teams building LLM systems on tabular financial data. The regulatory track record in that market segment is the differentiator over Gretel or Mostly AI. US-based teams with no FCA compliance requirements should start with Gretel instead, which has clearer published pricing and stronger US infrastructure. **Pros:** - Strong UK and EU financial services focus with GDPR and FCA compliance track record - Synthetic tabular data generation preserves statistical distributions needed for model training - Established customers in UK banking and insurance sectors **Cons:** - UK-centric vendor with less US market penetration and support infrastructure than Gretel or Tonic - Limited to tabular data; less useful for unstructured text or LLM prompt protection - Pricing and review data is sparse for independent verification Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | UK/EU financial services | ### Statice: For privacy-conscious ML teams needing statistical utility guarantees Score: 7.5/10 Rating: No public G2/5 (Vendor · N/A reviews) **Starting price:** Custom enterprise Statice's strength is in privacy guarantees you can report to a DPA or compliance auditor: formal epsilon-DP metrics and identifiability risk scores that quantify rather than assert privacy. That's the differentiator for EU-domiciled enterprises under GDPR Article 89 research exemptions. The acquisition by Anonos adds platform depth; verify the roadmap with the current team before signing. **Pros:** - Formal privacy guarantee metrics (epsilon-DP, identifiability risk scores) quantify privacy-utility trade-offs for compliance reporting - European HQ with strong GDPR data residency story for EU-domiciled enterprises - Part of Anonos, which adds a broader privacy engineering portfolio **Cons:** - No public pricing, limited public reviews; evaluate carefully for vendor longevity - Less US market presence than Gretel or Mostly AI - Tabular data focus; unstructured text LLM masking is outside core capability Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | GDPR-regulated EU enterprises | ### Mostly AI: For self-serve synthetic data generation with a freemium entry point Score: 7.5/10 Rating: 4.4/5 (G2 · 20 reviews) **Starting price:** Free up to 100K rows Mostly AI wins on self-serve accessibility. The free tier at 100K rows and the no-code UI mean a data analyst can generate synthetic training data for an LLM evaluation set without waiting for an ML engineer. [Mostly AI's pricing page](https://mostly.ai/pricing) is more transparent than most in this category. **Pros:** - Free tier up to 100K synthetic rows is the most accessible self-serve entry point in the synthetic data segment - Strong user interface for non-ML-engineer data teams who need synthetic data without Python code - Good multi-table support for relational dataset synthesis **Cons:** - Not an LLM proxy; synthetic data generation is a batch process, not real-time inference protection - Limited unstructured text synthesis compared to Gretel - Smaller engineering team and less commercial backing than NVIDIA-owned Gretel Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Free | $0 | Up to 100K synthetic rows | | Pro | Custom | Production volumes | | Enterprise | Custom | Large deployments | ### Synthetic Users: For product teams generating synthetic user personas for LLM UX testing Score: 7.5/10 Rating: 4.3/5 (Product Hunt · 35 upvotes reviews) **Starting price:** $59/mo Synthetic Users is on this list for product and UX teams who need to run LLM-based user research without touching real user data. The tool generates believable synthetic personas you can interview via LLM without ever collecting or exposing actual PII. Worth considering if your PII problem is 'we want to do user research without GDPR risk' rather than 'we need to mask production data going to GPT-4.' **Pros:** - Generates synthetic user personas for LLM-based user research without collecting or processing real user PII - Lower-stakes entry point for product teams vs. full enterprise PII platforms - Accessible pricing starting at $59/mo **Cons:** - Not a PII tokenization tool in the traditional sense; generates fake persona data for research rather than protecting real PII in pipelines - Limited compliance coverage compared to enterprise platforms - Niche use case; wrong category if your need is real-time LLM prompt protection Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Starter | $59/mo | Product teams | | Pro | Custom | Enterprise research teams | ### ARX Data Anonymization: For academics and data scientists needing open-source k-anonymity and l-diversity Score: 7.5/10 Rating: OSS/5 (GitHub · 736 stars reviews) **Starting price:** Free (open source) ARX is the right tool for academic researchers and data scientists who need to apply formal privacy models (k-anonymity, l-diversity) to tabular datasets before using them in LLM experiments. The open-source nature and peer-reviewed methodology are its strengths for research contexts where you need to cite and justify your anonymization approach. Not suited for production inference pipelines. **Pros:** - Implements k-anonymity, l-diversity, t-closeness, and differential privacy; the most thorough open-source anonymization toolkit for tabular data across formal privacy models - Free, no licensing cost or usage limits - Academic provenance with peer-reviewed methodology documentation **Cons:** - Java desktop application, not a cloud API or LLM proxy; integration into modern ML pipelines requires custom wrapping - No streaming or real-time capability; batch-only processing - Actively maintained but not designed for the LLM use case; better suited to traditional data anonymization workflows Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Open Source | $0 | Academic research | ### DataFleets: For privacy-preserving federated analytics on distributed sensitive data Score: 7.5/10 Rating: No public G2/5 (Vendor · N/A reviews) **Starting price:** Custom enterprise DataFleets addresses a specific gap: training LLMs on sensitive data distributed across multiple organizations or jurisdictions without centralizing it. Where tokenization protects data going to an existing LLM API, DataFleets enables training new models without any data leaving its source environment. Evaluate if your LLM use case involves multi-party data collaboration across organizational or regulatory boundaries. **Pros:** - Federated learning architecture means sensitive data never leaves the originating environment; the model trains locally and only aggregates learned parameters - Relevant for organizations sharing LLM training data across jurisdictions where centralization would create GDPR or data sovereignty issues - Addresses the multi-party data collaboration use case that tokenization proxies and synthetic data tools cannot solve **Cons:** - Very limited public review data; early-stage vendor with unverified commercial track record - Federated learning is computationally expensive and complex to orchestrate across multiple data environments - Not suited for real-time LLM inference protection; training-time use case only Pricing breakdown: | Plan | Price | Best for | |---|---|---| | Enterprise | Custom | Multi-party federated LLM training | ## More ## The PII-protection landscape in 20 tools The category splits into four architectures, and picking the wrong one costs engineering weeks. **Proxy tools** (NoPII, Skyflow LLM gateway) sit between your application and the LLM API. Change a base URL, get PII stripped from every prompt automatically. Zero code changes in most cases. The right default for inference-time protection. **SDK-based detection** (Private AI, Presidio, Nightfall) processes text through a library or API call your code invokes explicitly. More control over what gets masked and when. More integration work to wire into existing pipelines. **Synthetic data platforms** (Gretel, Tonic, Mostly AI, Hazy, Statice) replace real data with statistically equivalent fake data. The right approach for fine-tuning and training datasets, not for real-time inference protection. You don't tokenize a fine-tuning dataset; you replace it. **Governance and vault platforms** (Securiti, Azure Purview, Skyflow vault) treat PII protection as one capability inside a broader data governance stack. They cost more and take longer to deploy but solve adjacent problems (consent management, subject rights, data lineage) alongside tokenization. A team building a RAG chatbot on top of OpenAI needs a proxy or SDK approach. A team fine-tuning a model on customer support records needs a synthetic data platform. A CDO building an enterprise AI governance program needs a vault or governance platform. The tools are genuinely different in design, not just marketing copy. ## What's different in 2026 **Proxy architecture became the default for LLM PII protection.** Two years ago most teams were rolling their own Presidio wrappers. In 2026, purpose-built LLM proxies like NoPII and Skyflow's gateway handle the interception automatically. The proxy pattern is now table stakes; teams that built custom wrappers are migrating to them. **Synthetic data took on a new role after training data regulation.** Several US state AI laws now require documented provenance for training data containing personal information. Synthetic data platforms shifted from "nice to have for staging" to "compliance requirement for fine-tuning." Gretel's NVIDIA acquisition in 2025 accelerated this. **Differential privacy moved from academic to commercial.** Two years ago DP fine-tuning was a research paper. In 2026 it's an enterprise sales pitch from Anonym and others. Mathematical privacy guarantees are appearing in procurement RFPs from healthcare and financial services companies. The math is real; the vendor commercial maturity is still early. **Multi-tenant inference created a new attack surface.** Teams running LLM workloads on shared GPU infrastructure (not Azure OpenAI or AWS Bedrock dedicated instances) discovered that prompt confidentiality is as important as PII removal. Protopia's stochastic embedding approach addresses this; most tokenization tools do not. **HIPAA enforcement guidance on LLMs landed.** OCR issued informal guidance in late 2025 that LLM API calls with PHI in the prompt likely constitute a disclosure requiring BAA coverage. That pushed HIPAA-covered entities to either get BAAs with OpenAI/Anthropic or add a PII-stripping layer before API calls. The BAA path is often the faster one, but the PII layer is cheaper if you're already sending data to multiple providers. ## What I check in every PII-protection demo **One, detection coverage on your actual data, not their sample data.** Every vendor demo uses clean English-language test cases. Ask to run their tool on a 500-row sample of your own data (anonymized first if needed) before the demo ends. False positives on legitimate technical terms and false negatives on non-English names are the two failure modes that show up immediately on real data. **Two, latency overhead on a streaming LLM call.** For chatbot or real-time assistant use cases, every millisecond of proxy latency compounds. Ask for p99 latency numbers on a 2,000-token prompt during their demo. Anything over 200ms added latency starts affecting user experience. **Three, de-tokenization accuracy on LLM responses.** Tokenizing the prompt is half the problem. The other half is correctly identifying and replacing tokens that the LLM echoed back in its response. Ask the vendor to demo a prompt where the LLM refers to a tokenized entity by its token, and show you what the de-tokenized response looks like. **Four, custom entity type configuration.** Your data probably contains entity types that aren't in the vendor's standard set: product IDs, customer codes, internal account numbers, clinical trial identifiers. Ask how long it takes to add a custom recognizer or regex pattern and whether that requires a support ticket or self-serve configuration. **Five, the audit log.** For compliance purposes, you need to know which PII was detected and masked in which API call. Ask to see the audit log schema and confirm it captures enough detail to answer a GDPR Article 15 subject access request. Some tools mask PII but don't log what they masked. **Six, the failure mode.** What happens when the PII detection confidence is low? Does the tool block the call, pass it through, or flag it for review? The default behavior on uncertain cases tells you a lot about the security posture of the product. ## Narrowing the PII tool shortlist ### 1. Inference vs training use case This is the first decision. Real-time inference (every user prompt going to an LLM API) needs a proxy or detection SDK. Batch training data masking needs a synthetic data or anonymization platform. The tools don't cross over well. NoPII is fast at inference; it's not the right tool for a 10TB training dataset. ### 2. Team engineering capacity Presidio is free and customizable. It's also 40-80 hours of engineering work to stand up production-ready. If your team has a dedicated data engineering resource and a specific entity type the commercial tools miss, Presidio is the right foundation. If your team is three backend engineers and none of them want to own a PII infra system, NoPII or Private AI's cloud API is the faster path. ### 3. Compliance regime HIPAA-covered entities need a BAA. Private AI, Skyflow, AWS Comprehend, and Google Cloud Sensitive Data Protection all offer them. PCI DSS Level 1 is table stakes for fintech; NoPII and Skyflow both hold it. GDPR Article 44 data residency requirements push toward self-hosted or EU-region deployments. Work out your compliance requirements before you shortlist. ### 4. Language and entity type coverage English-only workloads are straightforward. Private AI at 50+ languages is the clear choice for global deployments. Domain-specific entity types (clinical codes, financial identifiers) that no vendor covers out of the box push toward Presidio with custom recognizers or NoPII's custom recognizer support. ### 5. Vendor scale and longevity Skyflow, Nightfall, and Securiti have raised meaningful venture capital and have enterprise customer bases. NoPII is a focused product from Enigma Vault, which has PCI DSS Level 1 and SOC 2 credentials but a smaller public profile. Anonym and Protopia are genuinely early-stage. If your compliance team requires a 3-year vendor viability assessment, the enterprise platforms will pass more easily. ## Compliance lockdown **SOC 2 Type II is the minimum for any enterprise LLM infrastructure.** NoPII, Skyflow, Private AI, Nightfall, Gretel, Tonic, and Securiti all hold SOC 2 Type II. Presidio is open source and self-attestation only. For AWS Comprehend and Google Cloud DLP, your cloud provider's SOC 2 covers the service. **HIPAA BAA availability is the key gate for healthcare.** Not all tools offer BAAs. Private AI, Skyflow, Nightfall, AWS Comprehend, and Google Cloud Sensitive Data Protection do. NoPII's PCI DSS Level 1 cert is strong, but confirm their HIPAA BAA status with the vendor before signing for a covered entity. **PCI DSS scope reduction is the main value for fintech.** NoPII (PCI DSS Level 1) and Skyflow (PCI DSS) can tokenize card data and other payment PII before it reaches an LLM, keeping those values out of scope for your annual PCI audit. That's a meaningful compliance cost reduction for teams processing payment data through AI workflows. **GDPR data residency under Article 44 matters for EU deployments.** Self-hosted options (Private AI, Presidio, Tonic, Gretel Enterprise) are the clean solution. Statice and Hazy are EU-headquartered vendors with GDPR residency commitments. US-only cloud APIs require either Standard Contractual Clauses or confirmation that processing stays in an EU region. ## The pick by stage **Pre-seed to Series A (under 20 engineers):** Enigma Vault NoPII for inference-time protection, Microsoft Presidio if you need custom entity types and have the engineering capacity. Both have free tiers. **Series B to Series C (20-100 engineers):** NoPII for inference pipelines, Gretel Team tier ($295/mo) for training data, Nightfall if you also need SaaS DLP coverage. Start building toward SOC 2 Type II on your side, and pick vendors that already have it. **Regulated startup (fintech, healthtech, any stage):** Skyflow for vault-grade PII architecture, Private AI if you need self-hosted deployment for data residency. Budget for enterprise pricing from day one; the free-tier tools won't pass your compliance review. **Enterprise (100+ engineers, existing compliance program):** Securiti for governance-first organizations adding AI capabilities to an existing data program. Azure Purview if you're a Microsoft 365 E5 shop and Copilot is your primary LLM. Skyflow for any team building a purpose-built AI data layer. **ML research team (academia or R&D):** Microsoft Presidio for open-source text anonymization. ARX for tabular k-anonymity and formal privacy models. Mostly AI's free tier for synthetic data generation without budget approval. **Fine-tuning a model on sensitive data:** Gretel with differential privacy if statistical utility matters. Tonic.ai if the data lives in relational databases with referential integrity requirements. Anonym if mathematical differential privacy guarantees are required by your compliance team. Corrections and pricing updates can be sent to corrections@topickz.com. This guide is refreshed quarterly; pricing data was last verified October 1, 2026. ## FAQs ### What is PII tokenization for LLMs? Replacing sensitive values (SSNs, names, health data) with reversible tokens before they reach an LLM API, then restoring originals in the response. ### Does Enigma Vault NoPII work with Claude and Gemini? NoPII proxies any OpenAI-compatible API. Anthropic (Claude) is supported natively. Gemini requires the OpenAI-compatible endpoint. ### Is Microsoft Presidio accurate enough for production? Yes for English text with standard PII types. Non-English or domain-specific entities (medical codes, internal IDs) need custom recognizer work. ### How much does Skyflow cost? Skyflow does not publish tiers. Vendr transaction data puts average enterprise deals around $195K/yr. Requires sales engagement. ### What is the difference between tokenization and redaction? Redaction removes PII permanently. Tokenization replaces it with a reversible token, so the LLM response can be de-tokenized back to the original value. ### Can I use AWS Comprehend as a real-time LLM proxy? Not natively. You build a Lambda function that calls DetectPiiEntities, masks values, then forwards to your LLM. NoPII or Presidio are simpler proxy options. ### What is differential privacy fine-tuning? Training an LLM with mathematical noise guarantees that bound what the model can reveal about any individual training record. Anonym specializes in this. ### Does Gretel.ai work for unstructured text? Yes but tabular data is its strongest suit. Private AI and Presidio outperform Gretel on freeform text PII detection accuracy. ### Which tools have HIPAA BAAs available? Private AI, Skyflow, Nightfall, AWS Comprehend, and Google Cloud Sensitive Data Protection all offer HIPAA BAA agreements. ### What is the fastest integration for a startup hitting OpenAI? Enigma Vault NoPII. Change one environment variable (base URL), get PII tokenization with no code changes. Free tier at 1M tokens/mo.