Diffyn VS SOWA Privacy

Diffyn's answer

Addresses workflow and change management on LLM prompts, provide teams with traceability and visualization of tests across multiple models, provide deeper understading into efficiency of these prompts.

SOWA Privacy's answer:

SOWA Privacy is the only AI privacy proxy that runs entirely on-premise - inside the browser, with zero cloud infrastructure. While most privacy tools operate at the network or policy level, SOWA intercepts personally identifiable information before a prompt is ever sent.

Three detection layers work in sequence on every prompt: regex pattern matching for structured data, Named Entity Recognition for names and entities in natural language, and a local AI model for context-aware edge cases. All three run on the user's own device. Nothing ever leaves the browser. No cloud migration, no infrastructure changes, no workflow disruption. Open source, fully auditable, and GDPR-conscious by architecture - not by promise.

Diffyn's answer

Diffyn is the platform that specializes on both change management and multi-model analysis.

SOWA Privacy's answer:

Most alternatives ask you to trust their server, their policy, or their terms of service. SOWA asks you to trust nothing but the code - which you can read, fork, and audit yourself on GitHub. Zero-Trust is the architecture, not a marketing claim.

For individuals, it is a one-click Chrome Extension - free to download, no account required, active immediately. For organizations, it deploys org-wide via Master Policy with a single configuration, covering every machine without touching existing workflows. 33 detection patterns across 4 languages, 635 automated tests, and SLA-backed enterprise support for regulated sectors. No competitor combines on-premise architecture, open-source transparency, and enterprise-grade deployment in a single browser extension.

Diffyn's answer

React, Next.js, POSTGRESQL

SOWA Privacy's answer:

SOWA Privacy is built as a Manifest V3 Chrome Extension.

The detection pipeline runs three layers entirely on-device: regex pattern matching for structured PII formats, a Named Entity Recognition model for natural language entity detection, and a local large language model for context-aware anonymization. All inference runs locally - no external API calls, no cloud dependencies.

The codebase is fully open source on GitHub with 635 automated tests covering the detection pipeline, anonymization logic, and extension behavior. The architecture is modular, designed to be extended with additional detection patterns, languages, and enterprise policy layers. Currently supporting four languages: English, German, French, and Spanish.

Diffyn's answer

Professionals incorporating LLMs or AI tools in their workflow and wants to keep track of changes and test their prompts.

SOWA Privacy's answer:

SOWA serves two parallel audiences that reinforce each other.

The first is privacy-conscious individuals - developers, journalists, NGO workers, and activists - who use AI tools daily but will not hand a cloud platform their data, their clients' information, or their source code. For them, SOWA is a free, open-source, zero-friction layer of protection.

The second is compliance-driven organizations - particularly in healthcare, finance, legal, and public administration - where a single mishandled AI prompt can become a GDPR incident. Decision-makers include Data Protection Officers, IT Security Leads, CTOs, and compliance managers in mid-to-large organizations, primarily across the DACH region and the broader EU. These teams need a solution that deploys at scale, produces audit logs, and integrates with existing infrastructure without rearchitecting it.

Diffyn's answer

I started working on Diffyn when I notice that prompting has become an essential part of work across many industries. While there are version control platofrms like github, they are not designed for just prompt management are can be overkill such applications, it is also not integrated natively with various LLMs and relevant tools for users to validate ideas and visualise results properly.

SOWA Privacy's answer:

SOWA Privacy was built out of a problem that kept coming up in every organization we spoke to: teams were already using ChatGPT, Claude, and Copilot - and nobody could tell us what data was actually leaving the browser. Names, case numbers, client details, source code. All of it sent to servers outside EU jurisdiction, with no real opt-out and no visibility.

The existing answers were either too blunt (block AI tools entirely) or too vague (trust our privacy policy). Neither worked for teams that needed AI to do their jobs and needed compliance to keep their licenses.

So we built the fix. An on-premise proxy that sits at the point of input, not the point of policy. The owl - SOWA's symbol - sees everything before it leaves. That is the product philosophy: intercept early, anonymize locally, leave no trace. Built in the open, owned by no one but the user.

SOWA Privacy's answer:

SOWA Privacy is in its public launch phase and does not yet have publicly referenceable enterprise customers. The product is actively used by privacy-conscious individuals, developers, and early-adopter organizations across the DACH region.

We offer a 2 to 4 week proof-of-concept deployment for organizations in regulated sectors - healthcare, legal, finance, and public administration - looking to validate SOWA before committing to an enterprise license. If you are evaluating SOWA for your organization, we welcome direct contact via sowaprivacy.ai.