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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, self-hosted, and sits inside a broader privacy toolkit. Both can run in your own environment, so the real decision is about auditability, policy depth, pricing posture, and breadth versus depth. Here is the honest comparison.

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Side by side

How Philter and Private AI differ on the dimensions that drive procurement and architecture decisions. Both can be self-hosted, so the differences are about licensing, depth, breadth, and pricing model rather than where the data runs.

PhilterPrivate AI
LicenseApache 2.0 · open sourceCommercial (closed source)
Source auditabilityFull source on GitHub; read every detection ruleClosed-source container; behavior is documented, not inspectable
DeploymentSelf-hosted in your VPC, on-prem, or air-gappedSelf-hosted container or Private AI cloud
Data residencyStays in your environmentStays in your environment (container) or sent to Private AI cloud
Language coverageGeneral and healthcare lenses; additional languages via PhEye lensesBroad multilingual coverage out of the box
ModalityText (NLP via PhEye) and PDFText, PDF, images, and audio
Entity coverage30+ built-in types plus a custom policy engineLarge built-in entity set
Policy authoringFull engine: dictionaries, regex, custom identifiers, conditions, per-entity strategiesConfigurable entity selection and replacement
Consistent pseudonymizationYes · context and document scopeYes · de-identify and re-identify
Format-preserving encryptionYesSynthetic replacement and re-identification
Pricing postureOpen source · per-instance-hour on the marketplaces ($0.49/hr)Commercial license, usage-based (contact sales)
Integration surfaceREST API, LLM proxy, SDKs, and embeddable Phileas libraryREST API (container or cloud)
Surrounding toolkitDiscovery, drift monitoring, benchmarking, differential privacyFocused 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.

Both can self-host, so start with the license

The first thing to get straight is that this is not a self-hosted-versus-cloud comparison. Private AI offers a container you can run in your own infrastructure, just as Philter does, so for teams that deploy the container, sensitive data can stay inside the perimeter in both cases. That is a point in Private AI’s favor relative to pure-SaaS competitors, and worth saying plainly.

The real fork is the license and what it buys you. Philter is open source under the Apache 2.0 license, and every detection rule, model, and policy behavior is in source you can read on GitHub. 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 Show me the code path.

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 redacting audio transcriptions).
  • 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.”
  • 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 Go 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.

Further reading

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