Best PII Tokenization Software for LLMs in 2026: 6 Top Platforms for Protecting Prompts

Comparing the best PII tokenization software for LLMs of 2026: 1. Enigma Vault (NoPII), 2. Protecto, 3. Skyflow, 4. Limina (formerly Private AI), 5. Grepture, 6. Philterd.

About the author: Abhilash Kumar, Chief Growth Officer at Enigma Vault

I lead growth, positioning, and partnerships at Enigma Vault, after running marketing at Amnic, Element5, and KenSci. Much of my week goes to calls with developers and AI product teams who are wiring customer data into chatbots, RAG pipelines, and agents, so I see where LLM privacy work stalls: the token that changed between turns, the redacted answer nobody could use, the proxy that broke streaming. I wrote this guide with our product and security team, who run a PCI DSS Level 1 vault with SOC 2 Type II controls.

Disclosure: I work at Enigma Vault, which appears first in this list. Every platform here is judged on the same criteria, and the review ratings come from G2 and Capterra.

You’re building a support copilot, a booking assistant, or an agent that reads customer records, and the prompt has to include names, emails, account numbers, or claim details to be useful. The question for your team isn’t whether to protect that data; it’s how to do it without wrecking the feature.

That means the model provider should receive tokens instead of real values, the same customer should keep the same token from message one to message twenty, your users should see real values in the reply, and the whole thing shouldn’t add a week of plumbing or a noticeable pause before the first streamed word. This guide compares six platforms on exactly those points.

Top-Rated PII Tokenization Software for LLMs at a Glance

Here’s how the six platforms stack up for an LLM app team. Every rating, price, and feature was checked on October 16, 2026.

Want to see your own prompts tokenized and restored before you commit to anything? Try the NoPII demo

What Is PII Tokenization Software for LLMs?

PII tokenization software for LLMs swaps personal data in your app’s prompts for reversible tokens before it reaches the model provider, then restores the real values in the model’s answer.

Key Features to Look for in PII Tokenization Software for LLMs

In any demo or free-tier test, these are the things worth checking first:

• Round-trip restore: The platform should detokenize the model’s response for you, not just hand you a mapping table and wish you luck.
• Stable tokens across turns and documents: Deterministic tokens mean the same email address becomes the same token in every message, chunk, and agent step.
• Entity coverage you can test: Look for a published entity list, custom identifiers (policy numbers, member IDs), and a way to run your own sample prompts through detection.
• Low integration effort: A base-URL swap is a one-line change; an SDK wrap is a small refactor; a mask-call-unmask API is a feature you build and maintain.
• Streaming that still streams: If your chat UI renders tokens as they arrive, the platform has to restore values mid-stream without buffering the full answer.
• A deployment model your data rules allow: Hosted proxy, your VPC, or a container on your own servers, plus a clear statement of what is stored and for how long.
• Pricing that fits a feature, not a company: Per-token or per-request pricing with a free tier lets you price an LLM feature before procurement gets involved.

Related reading: how PII tokenization works for AI, PII masking for LLMs, and why your LLM doesn’t need your customers’ names.

How We Tested and Ranked PII Tokenization Software for LLMs

We scored each platform on six criteria weighted toward one question: can a small team ship a privacy-safe LLM feature with it this sprint?

Sources and method. We read each vendor’s product pages, developer documentation, GitHub repositories, security and pricing pages, plus G2 and Capterra listings, on October 16, 2026. We did not run hands-on benchmarks of competing products, so detection and latency claims are the vendors’ own. Compliance status is what each vendor states; ask for current reports before you sign.

The 6 Best PII Tokenization Software for LLMs for 2026

NoPII is Enigma Vault’s LLM privacy proxy. You point your existing OpenAI or Anthropic client at https://api.nopii.co, keep your own provider API key, and personal data in each prompt is replaced with deterministic tokens before it reaches the model provider, then restored in the response so your app receives real values.

It fronts nine providers, and Enigma Vault states time to protection is under five minutes. Test detection on your own prompts first, because entities the proxy doesn’t recognize can pass through.

• Ship protection with a one-line base-URL change, not a refactor.
• Keep the same customer on the same token from turn one to turn twenty.
• Prove it free on 1 million protected tokens a month.

Key Features

• Base-URL proxy: Point your OpenAI or Anthropic SDK at https://api.nopii.co and keep your own provider key, with no SDK or middleware rewrite.
• Deterministic round-trip tokens: The same value always gets the same token, and the response is detokenized automatically before it reaches your app.
• Streaming restore: SSE responses are detokenized in flight, so streamed chat keeps streaming.
• Refusal-safe labels: Data-type labels are neutralized (a “Social Security number” becomes an “ID number”) so the model doesn’t refuse a harmless request.
• Credential detection: Secrets across 14 pattern families, such as cloud keys, database connection strings, and private keys, are tokenized in the same pass as PII.
• Fail-closed with audit log: If the tokenization service can’t be reached, the request is blocked instead of sent raw, and each request is logged.

Pricing tiers

Pros

• Integration is a one-line base-URL change, so you can protect an existing feature without touching your prompt or retrieval code.
• Deterministic tokens and automatic detokenization keep multi-turn context intact, so your assistant still answers about the right customer.
• The free plan covers 1 million protected tokens a month, so you can prove the approach in staging before you spend anything.

Cons

• It runs as a managed service only, so teams that must keep every byte on their own servers will need a self-hosted engine instead.
• The Free plan keeps token mappings for one day, so tokens that must persist across longer sessions need Pro, where retention is configurable.
• There’s no published entity list or accuracy benchmark, so you need to test coverage on your own traffic before launch.

What developers say in reviews: Very positive. Enigma Vault holds 4.4/5 from 6 reviews on G2 and 4.8/5 from 4 on Capterra. Reviewers mostly discuss the company’s vault products and like the simple, easy-to-follow tokenization API, the affordable pricing, and quick responses to feature requests. One reviewer wants deeper reporting dashboards and another found pricing a little high; NoPII doesn’t yet have its own review listing.

Swap one base URL and watch your next prompt reach the model with tokens instead of customer details. Start NoPII free

Protecto calls itself an AI data control plane that masks sensitive values before they reach LLMs, agents, RAG pipelines, or MCP tools. It claims 200+ data types and offers SaaS, VPC, and air-gapped on-premises deployment, but you write the mask, call, and unmask steps yourself through its APIs.

Key Features

• 200+ data types: Detection spans PII, PHI, and business-confidential data, with custom entities and multilingual support.
• Namespace-consistent tokens: The same input always produces the same token within a namespace, and tokens can be format-preserving.
• Role-based unmasking: Unmasking is governed by permissions, so a support agent and a supervisor can see different detail from the same answer.
• Framework coverage: Protecto names OpenAI, Gemini, Claude, DeepSeek, Cohere, and Grok, plus LangChain, LlamaIndex, Semantic Kernel, Haystack, Pinecone, and Weaviate.
• Flexible deployment: Run it as SaaS, in your own VPC, or on-premises in air-gapped mode with zero data egress.

Pricing tiers

Pros

• The 200+ entity catalog covers PHI and business data too, so you write fewer custom detectors for unusual identifiers.
• Role-based unmasking lets different users see different levels of detail, so one pipeline can serve several teams.
• VPC and air-gapped options exist, so security reviews that rule out shared SaaS don’t end the project.

Cons

• Per-call pricing climbs fast; $250 a month buys 5,000 calls, which a busy chatbot can use in a day or two.
• The free trial masks only 1,000 values, which is thin for testing detection on realistic traffic.
• There’s no documented drop-in base-URL proxy, so you maintain the mask and unmask code around every model call.

What developers say in reviews: Protecto has no G2 or Capterra reviews yet, so ask for customer references. Its G2 and Capterra pages exist with zero ratings, and on G2 make sure you don’t confuse it with Protect AI, a different company.

Comparing per-call and per-token costs for your traffic? See NoPII pricing

Skyflow’s LLM Privacy Vault extends its data privacy vault to generative AI, protecting sensitive data across collection, training, fine-tuning, RAG, and inference. It lists ISO 27001:2022, SOC 2 Type II, and PCI DSS Level 1 service provider status, but pricing is by quote and there’s no self-serve trial.

Key Features

• Full AI lifecycle scope: Sensitive data is protected during collection, training, fine-tuning, RAG, and inference, for “any LLM or AI apps.”
• Tokenization and polymorphic encryption: Detected values are redacted or tokenized, then re-identified for authorized use under vault policies.
• Kong gateway plugin: The Skyflow De-identify plugin for Kong Gateway fails closed, so a gateway team can add protection without app code changes.
• Data stack integrations: Skyflow names AWS, Google Cloud, Snowflake, Databricks, Glean, ServiceNow, and HubSpot.
• Data residency product: A separate residency offering helps meet regional rules such as GDPR, the EU AI Act, and DPDP.

Pricing tiers

Pros

• The certification list is the strongest in this group, so your vendor review moves faster.
• One vault can serve LLM prompts and your warehouse, so tokens match across analytics and AI.
• The Kong plugin fails closed, so a gateway team can add protection without app code changes.

Cons

• There’s no self-serve trial or public price, so you can’t test it in a spare afternoon.
• Rollout is a project; GoodRx, quoted on the product page, deployed in under three weeks, not minutes.
• LLM entity coverage and the full provider list aren’t published, so you’ll need a scoped proof of concept.

What developers say in reviews: Too few to judge. Skyflow holds 5/5 from 1 review on its G2 Payments Data Privacy Vault listing, its Skyflow for GenAI listing has no reviews yet, and there’s no Capterra listing, so ask for customer references that run LLM workloads.

Need LLM protection running before a vault project is scoped? Try NoPII in minutes

Limina, which rebranded from Private AI, is a detection engine that runs as a container in your VPC or on-premises. It covers 50+ entity types in 52 languages, and its LLM guide describes a de-identify, send, re-identify loop that you build yourself.

Key Features

• Context-aware detection: 50+ entity types across PII, PHI, and PCI, in 52 languages including code-switched text.
• Published accuracy test: In Limina’s 45,000-word internal test, general-purpose services missed 13.8% to 46.5% of entities against Limina’s 0.2% to 7%.
• Re-identification mappings: The redaction call returns entity mappings you use to restore the model’s answer.
• Self-hosted container: Runs in your VPC or on-premises with no outbound calls, and is available on AWS, Azure, and Snowflake marketplaces.
• Stateless processing: Text isn’t stored by the engine, which simplifies data-retention reviews.

Pricing tiers

Pros

• Published accuracy figures give you a baseline, so you know what to measure against in your own tests.
• Coverage of 52 languages means one engine handles a global support queue instead of one model per region.
• Running the container in your VPC keeps raw text inside your network, so data-residency reviews are simpler.

Cons

• You build the proxy and restore loop yourself, so a working LLM feature takes real engineering time.
• The free tier stops at 75 calls a day, which is enough to evaluate but not to run a pilot.
• The LLM integration guide is dated and skips multi-turn consistency, streaming, and latency.

What developers say in reviews: Very positive. Limina holds 4.6/5 from 22 reviews on G2 under its former Private AI name and isn’t listed on Capterra. Reviewers praise detection accuracy and quick setup and call it simple and elegant. Complaints center on the interface and on enterprise-style pricing and setup that feel heavy for small teams.

Want detection and restore handled in one hop? See how NoPII works

Grepture is a young, privacy-focused AI gateway hosted in Frankfurt and Nuremberg that masks values on the way out and restores them in the response. Pro costs $49 a month billed annually, but there’s no SOC 2 or comparable audit report, only “GDPR and EU AI Act ready” claims.

Key Features

• Mask and restore: Reversible redaction with a token time to live keeps downstream systems working.
• 80+ detection patterns: Pattern rules run alongside AI-powered entity detection.
• Prompt-injection blocking: The gateway detects and blocks injection attempts in the same hop.
• Built-in observability: Multi-step traces, LLM-as-judge evals, and per-model cost tracking come with the gateway.
• Zero-data mode: Content is processed in flight and never stored; only operational metadata is logged.
• Ten named providers: OpenAI, Anthropic, Google AI, Azure OpenAI, Cohere, Mistral, AWS Bedrock, Hugging Face, Groq, and Replicate.

Pricing tiers

Pros

• Paid plans start under $50 a month, so a side project or early startup can afford a gateway.
• Evals, traces, and cost tracking ride along, so you may not need a separate observability product.
• EU hosting suits teams whose customers expect data to stay in Europe.

Cons

• No SOC 2 or comparable audit report is published, which stalls enterprise security reviews.
• Token consistency across requests and streaming aren’t documented, so multi-turn behavior needs testing.
• The free plan allows just 25 AI detections, which isn’t enough to judge detection quality.

What developers say in reviews: Grepture has no G2 or Capterra reviews yet, so ask for customer references. Its open source proxy repository on GitHub is small and recent, so there’s little public feedback to go on.

Need SOC 2 Type II behind your gateway? Talk to Enigma Vault

Philterd’s Philter AI Proxy is an Apache-2.0 proxy that strips PII before a request reaches OpenAI, Claude, Gemini, Bedrock, Ollama, or an OpenAI-compatible server like vLLM. It’s here as a contrast, because it redacts rather than tokenizes, so the model sees “REDACTED” and nothing is restored.

Key Features

• Cloud and local providers: OpenAI, Azure OpenAI, Claude, Gemini, Vertex AI, Amazon Bedrock, and Ollama, plus OpenAI-compatible servers such as vLLM.
• Philter-backed redaction: Each request is sent to a running Philter engine, which redacts sensitive values before the text goes to the model.
• Opt-in response scanning: Per route, responses can be redacted, blocked, or passed through with a warning log.
• Flexible self-hosting: Deploy as a binary, a Docker Compose stack, or a Helm chart with autoscaling and mTLS.
• Long-running streams: Transport timeouts don’t bound body reads, so streamed responses run as long as the upstream sends.

Pricing tiers

Pros

• Everything is self-hosted and open source, so you control the code and the data path.
• Support for Ollama and vLLM means you can protect local models as well as cloud APIs.
• Marketplace pricing by the hour keeps infrastructure costs predictable.

Cons

• Redaction can’t be reversed, so the model loses who is who and replies can’t be re-personalized.
• Multipart uploads (files, audio, image edits) are rejected, which rules out many multimodal features.
• It’s a small project with a handful of GitHub stars, so community support is limited.

What developers say in reviews: Philterd has no G2 or Capterra reviews yet, so ask for customer references. Its GitHub repositories are small, and public discussion is limited to a few open issues.

Need the real values back in your answers? Compare with NoPII

Key Features Compared Side by Side

Only Enigma Vault and Grepture document a hosted base-URL swap with automatic restore and a published plan under $60 a month, and only Philterd is redaction-only. Each cell comes from the vendor’s own pages, checked October 16, 2026.

Packaging changes often, so confirm the cells that matter most to you in the vendor’s quote.

How to Pick the Right PII Tokenization Software for Your LLM App

In my experience, two questions narrow the list fast: do your users need real values back in the answer, and is your team allowed to send data to a hosted service at all?

Recommended PII Tokenization Software for Each Use Case

Which PII Tokenization Software Fits Your Stage

Common Mistakes Developers Make When Buying PII Tokenization Software

• Testing on clean demo prompts: Real chat logs have typos, nicknames, and IDs in odd formats; run a sample of your own traffic before you trust any detection claim.
• Ignoring multi-turn behavior: A platform that issues a new token every request will confuse your assistant by turn three, so test a ten-message conversation.
• Forgetting the response path: Masking the prompt is half the job; if nothing restores tokens, your users see placeholders and your team writes the fix.
• Measuring latency on total time only: For chat, time to first token is what users feel, so compare it with and without the proxy while streaming.
• Pricing per call when you think in tokens: Convert each vendor’s unit (calls, requests, words, tokens) into your monthly prompt volume before comparing plans.

Smart Questions to Ask a PII Tokenization Software Vendor Before You Sign

• Can I change only my base URL, or do I need your SDK or my own mask and unmask calls?
• Does the same value get the same token across turns, sessions, and documents, and can I scope that?
• Which providers support streaming with restore today, and what happens to time to first token?
• Can you show me your entity list, and how do I add a custom identifier like a booking reference?
• Where is the token mapping stored, how long does it live, and who can call detokenize?
• If your service is down, does my request fail closed or go to the provider unprotected?

Ready to Try Enigma Vault’s NoPII?

If your LLM feature needs customer details to be useful, NoPII lets you keep them: detected values are tokenized before they reach the model provider, the same customer keeps the same token across the conversation, and real values come back in the response. Read how it works on the NoPII product page, check the pricing (the Free plan covers 1 million protected tokens a month), or run your own prompt through the live demo and see exactly which entities it catches.