---
title: "Philter vs Skyflow (Redaction vs Data Privacy Vault)"
description: "Philter and Skyflow solve different problems: Philter redacts PII in your pipeline, Skyflow is a vault that tokenizes it. How they differ and when each fits."
url: "https://philterd.ai/compare/philter-vs-skyflow/"
---

[← All comparisons](https://philterd.ai/compare/)

Comparison

# Philter vs Skyflow (Redaction vs Data Privacy Vault)

Philter and Skyflow come up in the same conversations, but they are different categories. [Philter](https://philterd.ai/philter/) is self-hosted software that redacts and de-identifies PII in text as it flows through your pipeline. Skyflow is a managed data privacy vault: it stores sensitive values, hands back tokens, and governs who can detokenize them. One removes sensitive data from a stream; the other isolates and retains it behind an access boundary. Picking correctly starts with which problem you actually have.

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### Choose Philter when

- You want sensitive data removed or transformed before it reaches analytics, logs, search, or an LLM, not stored for later retrieval.
- You need the original values to never leave your perimeter: a self-hosted engine, not a managed vendor.
- You want open source and an auditable detection path.
- You are de-identifying unstructured text at volume.

### Choose Skyflow when

- You must retain the original sensitive values and retrieve them later (for example, to charge a card or ship an order), just not store them in your own systems.
- You want a managed vault with fine-grained, policy-based detokenization access control.
- Your data is primarily structured records (customer profiles, payment data) rather than free text.
- A managed vendor relationship fits your posture.

## Side by side

Philter and Skyflow are adjacent, not interchangeable. The table compares them on the axes that actually separate a redaction engine from a data privacy vault.

|  | Philter | Skyflow |
| --- | --- | --- |
| Category | Redaction and de-identification engine | Data privacy vault (tokenization and storage) |
| What happens to the original value | Removed or transformed in the stream | Stored in the vault, returned as a token |
| Reversibility | Optional · format-preserving encryption (key holder) or one-way redaction | Core feature · detokenization with access control |
| License | Apache 2.0 · open source | Commercial (proprietary) |
| Deployment | Self-hosted in your VPC, on-prem, or air-gapped | Managed SaaS: multi-tenant, single-tenant, or bring-your-own-cloud |
| Who runs it | You | Skyflow (in their cloud or a managed VPC in yours) |
| Primary data shape | Unstructured text (and PDF) | Structured records and fields |
| Integration model | REST API, [LLM proxy](https://philterd.ai/philter-ai-proxy/), SDKs, embeddable [library](https://philterd.ai/phileas/) | Vault SDK and API; data is routed through the vault |
| Policy authoring | Full engine: dictionaries, regex, conditions, per-entity strategies, FPE | Vault schema and tokenization / access policies |
| Audit posture | Read the source; measure accuracy with [Philter Scope](https://philterd.ai/philter-scope/) | Vault access logs and governance controls |

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](https://philterd.ai/compare/philter-vs-skyflow/#) and we will correct it.

## They solve different problems

The most useful thing this page can do is keep you from comparing the wrong attributes. Skyflow is a [data privacy vault](https://www.skyflow.com/data-privacy-vault). Its job is to take a sensitive value (a card number, a Social Security number, a customer record), store it in an isolated, governed vault, and hand your systems an opaque token in its place. When you genuinely need the original value back, an authorized service detokenizes it under policy. The point is to keep the real data out of your application databases while still being able to retrieve it.

Philter does the opposite of “retain and retrieve.” It [redacts](https://philterd.ai/philter/) and de-identifies PII in text as the text moves through your pipeline, so the sensitive content is gone (or encrypted, or replaced with a synthetic value) before it lands in a log, a warehouse, a search index, or a prompt to an LLM. There is no vault to retrieve from because, for most Philter use cases, you never wanted to keep the original in the first place.

So the question is not “which is better.” It is “do I need to retain this value and retrieve it later, or do I need it gone from this data flow?”

## When Skyflow is the right tool

If your requirement is to **hold** sensitive data, just not in your own systems, a vault is the right shape. Classic cases: you must keep a card on file to bill it next month, retain a government ID to satisfy a KYC obligation, or store a customer profile that downstream services occasionally need in the clear under tight access control. Skyflow’s detokenization with fine-grained, policy-based access control is built precisely for that, and it is mostly about structured records rather than free text. If that describes your problem, Philter is not a substitute.

## When Philter is the right tool

If your requirement is to **remove** sensitive data from a flow, a redactor is the right shape. Classic cases: scrub PII out of application logs before they reach your observability stack, de-identify free-text notes before they enter an analytics warehouse, strip identifiers from prompts before they reach a third-party LLM (the [Philter AI Proxy](https://philterd.ai/philter-ai-proxy/) does exactly this), or de-identify a training-data set so the original identifiers are never memorized. These are unstructured-text problems where the goal is that the sensitive value does not survive the pipeline. A vault does not solve them, because routing free text through a vault still leaves the surrounding text full of identifiers.

Two further differences matter for regulated buyers:

- **Self-hosted and open source.** Philter runs entirely inside your perimeter and is [open source](https://philterd.ai/open-source-software/) under Apache 2.0, so the original values never reach a vendor and your team can read the detection logic. Skyflow is managed: even in a bring-your-own-cloud deployment, it is Skyflow’s software operating the vault.
- **Reversibility without a vault.** When you do need a value to be recoverable, Philter offers [format-preserving encryption](https://philterd.ai/blog/what-is-format-preserving-encryption/): the value is encrypted (and decryptable by the key holder) while still looking like the original format, with no separate store to govern. It is a different reversibility model than tokenization, and for many use cases a simpler one.

## Many teams use both

These tools are not mutually exclusive, and a mature data-privacy architecture often has a place for each: a vault for the handful of fields you are legally required to retain and retrieve, and a redaction engine for the much larger volume of text and telemetry you simply want cleaned before it spreads. The mistake is using one where you needed the other: storing-and-tokenizing data you only ever wanted gone, or trying to redact your way out of a “we must retrieve this later” requirement.

## What to do next

Decide which problem you have first. If you need to retain and retrieve specific structured values under access control, evaluate Skyflow (or a vault generally). If you need to remove or de-identify PII as it flows through logs, analytics, search, training data, or LLM traffic, evaluate [Philter](https://philterd.ai/philter/). If you are currently using Skyflow for something that is really a redaction problem, the [migration guide](https://philterd.ai/migrate/from-skyflow/) walks through the architectural translation.

## Further reading

- [Migrate from Skyflow to Philter](https://philterd.ai/migrate/from-skyflow/): How to translate a vault-and-tokenize architecture into a redaction pipeline (this is a re-architecture, not an API swap).
- [Why API-based redaction is a security antipattern](https://philterd.ai/blog/why-api-based-redaction-is-a-security-antipattern/): The architectural argument for keeping sensitive data inside your perimeter rather than routing it through a managed service.
- [What is format-preserving encryption?](https://philterd.ai/blog/what-is-format-preserving-encryption/): Philter's answer to reversibility: encrypt a value so it still looks like the original, without a separate vault to store it.

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