Looking for a Microsoft Presidio alternative? Start with what Presidio does well

A familiar story: an engineer wires Presidio into a FastAPI service on a Friday afternoon, runs a few prompts through it, and watches Ada Lovelace and ada@example.com turn into <PERSON> and <EMAIL_ADDRESS>. It works. Three months later the same team is maintaining provider-specific JSON parsers, a Redis-backed token map, a custom recognizer for Swiss AHV numbers and a pager rotation for a service nobody planned to own. That's usually when someone searches for a Presidio alternative.

This page is written by Maskflare, so read it with that in mind. We've tried to be specific about where Presidio is the better choice, because for a fair number of teams it is.

What is Presidio?

Presidio is an open-source, MIT-licensed toolkit for PII detection and anonymization, originally built at Microsoft. As of October 2026 its GitHub README says the project has moved to a new home under the Data Privacy Stack organization, and its documentation describes a transition to community ownership. The code and license are unchanged.

It has four parts. The Analyzer finds entities using regular expressions, checksums, context words and a named-entity recognition (NER) model; spaCy is the default, with Stanza, Hugging Face transformers and others as options. The Anonymizer replaces, redacts, hashes, masks or encrypts what was found. There's an image redactor that uses OCR, and a structured module for tabular and semi-structured data. You run it as a Python library, in PySpark, or as Docker containers, and you can write your own recognizers. Several AI tools use it under the hood, including LiteLLM's PII masking guardrail.

When is Presidio the right choice?

• You need it free and fully self-run, and you have engineers who are happy to own it long term.
• Your data is mostly English prose with names in it. An NER model is good at spotting names in running text, and that's a real strength. Maskflare also runs a local named-entity layer for names, places and other free-text details on top of its deterministic rules, so test both on your own documents.
• You're doing batch anonymization of datasets in Spark or a notebook, not live traffic to an AI provider.
• You need text redaction inside images. Presidio has an image redactor. Maskflare's image scanning uses classic OCR in English, German, French and Italian for images in API requests and browser uploads, and refuses handwriting it can't read.
• You want to plug in your own ML model. Presidio is designed to host external NER models (Flair, GLiNER, transformers). Maskflare ships its own local named-entity layer and doesn't load arbitrary third-party models.

Where do teams hit the limits?

None of these are flaws in Presidio. They're the parts it was never meant to cover.

1. It's a library, not a gateway. To protect calls to OpenAI or Anthropic you write the middleware: intercept the request, find the text fields in each provider's JSON format (messages, tool calls, system prompts, content parts), call the analyzer, rebuild the body, then deal with the response. Every provider and every API change is yours to maintain.
2. Getting the real values back is DIY. Presidio can encrypt values and decrypt them later with the same key. A production restore also needs a token store, expiry, per-tenant keys, and a way to restore values inside a streaming response where one token may be split across two server-sent events.
3. Country coverage is uneven. As of October 2026 Presidio's supported entities list global types plus recognizers for 18 countries, with good depth for the US, UK, Germany, Italy and India. There's nothing built in for Switzerland, the Netherlands, France, Austria or Belgium. If you handle AHV numbers or BSNs, you write and test those recognizers yourself. Maskflare ships 47 country packs, including all EU-27, with checksum validation.
4. Secrets are a separate problem. API keys, tokens and connection strings aren't Presidio's focus. Maskflare includes 232 credential and secret formats.
5. Results can shift. NER output can change with the model version and surrounding context. That's fine for analytics and harder to defend to an auditor who asks why a value was or wasn't caught. Maskflare's detection is deterministic, and the console's explain view shows which rule matched.
6. There's no policy layer. Environments, maker-checker approval of rule changes, per-team rules, audit exports and an admin UI aren't part of Presidio.
7. People and agents aren't covered. Presidio protects code you write. It can't do anything about an employee pasting a customer list into ChatGPT in a browser, or an agent's MCP tool calls.

Presidio vs Maskflare, feature by feature

How do you move from Presidio to Maskflare?

If Presidio sits inside your own middleware, the simplest switch is to delete the middleware. Point your SDK's base URL at the gateway and keep the rest of your code:

from openai import OpenAI

client = OpenAI(
    base_url="https://<your-gateway>/v1/openai",
    api_key="mf_live_...",
)

reply = client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Draft a reply to Ada Lovelace (ada@example.com) about her CHF 2,400 refund."}],
)
# The provider saw tokens; your app gets the real name and email back.

If you only want detection, the SDKs expose mask and unmask directly (pip install maskflare or npm install @maskflare/sdk). And if you run LiteLLM with the Presidio guardrail today, the maskflare[litellm] extra ships a guardrail class you can swap in, so you can compare both engines on the same traffic. The LiteLLM masking guide walks through it.

The honest objections

“Presidio is free.” True, and for a prototype that settles it. The cost shows up later: the middleware, the token vault, the country recognizers, the streaming edge cases and the on-call rotation. Estimate the engineering time for your own team and compare it with a managed tool before deciding.

“We want everything inside our network.” Fair, and it's no longer a reason to build it yourself. Maskflare is EU-hosted by default, and on-premises deployment is available for enterprise customers, so prompts never leave your infrastructure. If you need open source specifically, so you can read and patch every line, Presidio is still the answer.

“We already use LiteLLM with Presidio.” Then you already have a proxy. Keep it, swap the detection layer for the Maskflare guardrail, and compare. One thing worth checking either way: restoring values on non-OpenAI paths and in streams is where DIY setups tend to break.

“NER catches things rules can't.” Sometimes, yes, especially names and addresses in prose. Rules catch things NER struggles with: a 13-digit Swiss AHV number with a valid check digit, an API secret key pasted from a config file, a card number like 4532 7781 0021 9047 with zero-width characters hidden between the digits. Many teams care more about the second group, because those are the values that cause incidents.

What Maskflare doesn't do

• It isn't open source, and you can't swap in your own NER model.
• Person names are found by word lists, context and the local named-entity layer. Test unusual names from your own data before you rely on it, and add a dictionary or Exact Data Match for known customers.
• Image OCR is English-only and limited to API requests. Audio in realtime voice sessions isn't masked; text events are.
• SOC 2 Type II and ISO 27001 audits are in progress.