From Regex Library to Real API: Building OpenRedaction's Developer Journey

You know that feeling when you build something small, open-source… then suddenly people star it, fork it, and ask: "How much for the API?"

That's where we found ourselves with OpenRedaction. What began as a deterministic regex-based redaction library — simple, local, dependable — has now become something bigger: a hosted AI-assist proxy, Stripe payments, API keys, and a real product behind it.

In this post I walk you through that journey: why we built each piece, what worked, what we learned — and how you can use the same blueprint for your own dev tools.

1. The beginning: an open-source library for privacy-first redaction

• Origins — OpenRedaction started as a personal tool: a regex-based engine to strip out names, emails, phone numbers, addresses, etc. from text. It was simple, deterministic, fast, and local.
• Why regex-first? Regex gives control: no external calls, no hidden AI, no data leaks. That's important for privacy, security, and compliance.
• Open-sourcing — I made it MIT, published on GitHub, added many patterns, tests, and documentation. People liked it: devs could trust its transparency and deterministic behaviour.

Value delivered: a lean, dependable redaction engine for anyone who needs PII-safe output — logs, disclosures, transcripts, and more.

2. The gap: real-world data isn't neat — regex wasn't enough

Real world isn't clean. Names are lowercase, uppercase, mixed case; people combine first/last names incorrectly; addresses vary; phone numbers have weird formats; blobs of unstructured text with noise.

Regex did a great job — but still missed messy, ambiguous, or unusual cases.

So I asked: What if we layer an AI-powered detection pass over regex?

But I also wanted to stay true to the original values: privacy, transparency, and optionality.

3. The hybrid solution: regex-first + optional AI-assist

We designed the architecture to be hybrid:

• Regex-first core — still default, local, open-source.
• Optional AI-assist via hosted proxy — when you need extra detection power.
• User decides — you can stay 100% local, or use hosted API.

That balance preserves trust while giving flexibility.

4. From library to product: building the API, proxy, billing

A few big steps:

• Built a hosted AI proxy — accepts text, passes it to a model provider, returns structured entity spans.
• Wrapped with rate-limiting, API keys, quota checks — using Upstash + KV storage.
• Integrated payment handling (via Stripe) → after checkout, generate API key + email to user.
• Updated docs + README + site messaging — made clear what's free, what's paid, and how to use.
• Added free tier + pro tier — free tier for experimentation; pro tier for real usage (e.g. 50,000 AI-assisted requests/month).

This turned OpenRedaction from a hobby-library to a real dev-tool product.

5. What we learned (the hard and the good)

Good

• Open source brings visibility and trust.
• Hybrid model satisfies both "privacy-first" and "power-when-needed" communities.
• Simple billing + API key logic is enough at early stage.
• Transparent docs + clear messaging convert interested devs quickly.
• Hosting under your own proxy lets you control quota, avoid vendor friction, and shield users from complexity.

Challenges & trade-offs

• You must explain clearly when AI-assist sends data externally — honesty builds trust.
• Edge cases: very long inputs, abuse, rate-limiting — had to harden API accordingly.
• Documentation & UX must stay rock-solid to avoid confusion.
• You lose the "fully local only" claim when users choose AI mode — needs clear communication.

6. The result: a tool devs can trust — with flexibility

OpenRedaction today:

• Is still free and open-source at its core.
• Lets you redact with pure regex quickly and privately.
• Offers optional AI-assist for messy or unstructured text.
• Provides a hosted API, billing, and key-based access — ideal for production.
• Gives community & enterprise users flexibility: local vs hosted; free vs paid; DIY vs plug-and-play.

It's become a full-featured redaction platform, but with developer values and transparency intact.

7. Advice for other dev-tool creators

If you're building a developer library and thinking of turning it into a product:

• Start with deterministic core functionality — something reliable, open-source, and trustable.
• Expose a clear switch — "core library only" vs "hosted service" — let users choose.
• Build incremental — library → self-hosted → hosted API → billing.
• Keep docs simple, honest, and upfront about trade-offs (privacy, cost, limits).
• Don't over-engineer early. A simple API key + rate limiting + small quota is enough to test demand.
• Use a hosted proxy rather than exposing vendor complexity — shield users from underlying dependencies.

Conclusion

OpenRedaction's journey — from small regex library to hosted API product — is a classic developer-tool growth arc. Because we stayed grounded in simplicity, transparency and dev-values, we haven't lost flexibility or trust — and unlocked real usage and revenue potential.

If you're building a tool, library or small SaaS: treat your users as developers, give them control, stay honest — and build slowly.

Want to see the live code or try it? Check out GitHub → OpenRedaction or visit openredaction.com.