When should we choose Philter over Private AI?

Open source and auditable is a hard requirement: your security or compliance team needs to read the detection logic, not just trust a datasheet. You want a full policy engine: dictionaries, custom regex, conditional rules, per-entity replacement strategies, and format-preserving encryption. You want the surrounding toolkit: an LLM proxy, discovery, drift monitoring, and policy benchmarking, not just a redaction API. You want predictable per-instance pricing and the option to embed the engine as a library.

When should we choose Private AI over Philter?

You need broad out-of-the-box coverage across many languages without assembling it yourself. You need multi-modal redaction (PDFs, images, and audio) from a single vendor API. A commercial, closed-source license is acceptable, and you would rather buy breadth than build depth. You want one managed vendor relationship and are comfortable with usage-based commercial pricing.

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Comparison

Philter vs Private AI (PII Redaction)

Private AI is a commercial, closed-source PII detection and redaction API with strong multilingual and multi-modal coverage, deployable as a container or used in their cloud. Philter is open source under Apache 2.0, always self-hosted, and sits inside a broader privacy toolkit. If you use Private AI's cloud your text leaves your boundary to be scanned; if you run their container it does not, so the decision turns on data egress, auditability, policy depth, pricing posture, and breadth versus depth. Here is the honest comparison.

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Choose Philter when

Open source and auditable is a hard requirement: your security or compliance team needs to read the detection logic, not just trust a datasheet.
You want a full policy engine: dictionaries, custom regex, conditional rules, per-entity replacement strategies, and format-preserving encryption.
You want the surrounding toolkit: an LLM proxy, discovery, drift monitoring, and policy benchmarking, not just a redaction API.
You want predictable per-instance pricing and the option to embed the engine as a library.

Choose Private AI when

You need broad out-of-the-box coverage across many languages without assembling it yourself.
You need multi-modal redaction (PDFs, images, and audio) from a single vendor API.
A commercial, closed-source license is acceptable, and you would rather buy breadth than build depth.
You want one managed vendor relationship and are comfortable with usage-based commercial pricing.

Side by side

How Philter and Private AI differ on the dimensions that drive procurement and architecture decisions. Private AI can run as a self-hosted container or in its cloud; Philter is always self-hosted, so start with where your text goes, then weigh licensing, depth, breadth, and pricing.

	Philter	Private AI
License	Apache 2.0 · open source	Commercial (closed source)
Source auditability	Full source on GitHub; read every detection rule	Closed-source container; behavior is documented, not inspectable
Deployment	Self-hosted in your VPC, on-prem, or air-gapped	Self-hosted container or Private AI cloud
Data residency	Stays in your environment	Stays in your environment (container) or sent to Private AI cloud
Language coverage	General and healthcare lenses; additional languages via PhEye lenses	Broad multilingual coverage out of the box
Modality	Text (NLP via PhEye) and PDF	Text, PDF, images, and audio
Entity coverage	30+ built-in types plus a custom policy engine	Large built-in entity set
Policy authoring	Full engine: dictionaries, regex, custom identifiers, conditions, per-entity strategies	Configurable entity selection and replacement
Consistent pseudonymization	Yes · context and document scope	Yes · de-identify and re-identify
Format-preserving encryption	Yes	Synthetic replacement and re-identification
Pricing posture	Open source · per-instance-hour on the marketplaces ($0.49/hr)	Commercial license, usage-based (contact sales)
Integration surface	REST API, LLM proxy, SDKs, and embeddable Phileas library	REST API (container or cloud)
Surrounding toolkit	Discovery, drift monitoring, benchmarking, differential privacy	Focused on detection and redaction

We want these comparisons to be accurate and fair. Technology moves fast: vendor capabilities, pricing, and product names change frequently, so this reflects publicly documented behavior at the time of writing and may have changed since. Always verify against current vendor documentation before deciding, and if you spot anything inaccurate or out of date, please let us know and we will correct it.

Deployment and data egress come first

Private AI ships two ways: a container you run in your own infrastructure, and a hosted Private AI cloud. That fork decides where your text goes, so settle it first. If you use the Private AI cloud, the raw pre-redaction text leaves your boundary to be scanned by a third-party service, the same data-egress exposure as any SaaS PII API, and the first thing a security review will flag. If you run the container, sensitive data can stay inside your perimeter. That self-hosted option is a genuine point in Private AI’s favor relative to cloud-only competitors, and it is worth saying plainly.

Philter has a single mode: self-hosted. It runs as a container in your VPC, on-prem, or air-gapped, so the text never leaves your boundary to be redacted and the data-egress question never has to be raised. If you would choose the Private AI container specifically to keep data in-house, Philter gives you that residency posture by default rather than as one of two options.

Then the license: open source vs. closed

Beyond where the data runs, the real fork is the license and what it buys you. Philter’s code is open source under the Apache 2.0 license , so every detection rule and policy behavior is in source you can read on GitHub , and the trained models are published openly on Hugging Face for inspection. Private AI is a commercial, closed-source product: you can run the container, but you cannot read the logic that decided a given token was or was not PII. For a buyer who has to defend a redaction decision to an auditor or regulator, that difference is the whole game, which is the argument we make in Open source vs black box .

Where Private AI is genuinely strong

It is worth being honest about Private AI’s strengths, because they are real and they matter for some workloads:

Multi-modal redaction. Private AI redacts not just text but PDFs, images, and audio through one API. If your pipeline needs to scrub identifiers out of scanned documents or call recordings out of the box, that breadth is a genuine convenience. Philter focuses on text and PDF; for audio you would pair it with a speech-to-text step (see the philter-transcriptions demo ).
Broad language coverage. Private AI ships wide multilingual support without configuration. Philter covers general and healthcare English strongly and extends to other languages through swappable PhEye lenses , which is flexible but is not the same as dozens of languages enabled by default.

If your primary need is “one vendor API that handles many file types in many languages,” Private AI’s breadth is a legitimate reason to choose it.

Where Philter pulls ahead

Philter’s advantages cluster around depth, auditability, and the surrounding toolkit:

Policy depth. Philter exposes a full policy engine: dictionaries, custom regex, identifier patterns, conditional rules (redact a ZIP code only when its population is below a threshold, redact an age only when over a value), per-entity replacement strategies, and format-preserving encryption. That control is the difference between “redact the built-in entity types” and “encode exactly the privacy behavior your downstream systems need.”
Checksum-validated national IDs. A custom identifier can apply a checksum or structural validator, so a pattern keeps only genuine values and rejects format-valid look-alikes. Ready-made, validated policies cover national and financial identifiers such as the Canadian SIN, Brazilian CPF and CNPJ, Spanish DNI, French NIR, IBAN, and SWIFT/BIC. This is precision on specific identifiers, not the broad multilingual entity coverage where Private AI is strong.
The toolkit, not just an API. Redaction is one job. Philter sits next to Phinder for discovery, Phield for PII drift monitoring, Philter Scope for measuring redaction quality, the Philter AI Proxy for guarding LLM traffic, and Philter Diffuse for differentially private analytics. Private AI is focused on the detection-and-redaction step.
Embeddable. Beyond the API, the Phileas library lets you compile redaction directly into a JVM, Python, or .NET application with no service to call.
Auditable accuracy. You do not have to take an accuracy claim on faith. You can measure precision and recall against your own gold-standard set with Philter Scope and put the number in the audit file.

Pricing posture

Private AI uses commercial, usage-based pricing negotiated with sales; the closed-source license is part of what you are paying for. Philter is free and open source , with paid, predictable per-instance-hour deployment on the AWS, GCP, and Azure marketplaces ($0.49/hr) and optional commercial support. For high-volume workloads, per-instance pricing flattens out in a way usage-based pricing does not, and there is no per-call license cost on the open source engine itself.

What to do next

If broad multilingual and multi-modal coverage from a single commercial vendor is the priority, Private AI is a reasonable choice. If open source and auditability are requirements, if you want policy depth and format-preserving encryption, or if you want the surrounding discovery, monitoring, benchmarking, and LLM-proxy tooling rather than a redaction API alone, start the evaluation on Philter. The migration guide covers how the concepts map if you are moving off Private AI.

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