XTrace – Encrypted vector DB (search embeddings without exposing them)

Hacker News

Hey everyone! This is XTrace. Wanted to share what we’ve been working on for the past year. We built a private vector database from the ground up that performs similarity search on encrypted vectors. The server never sees your plaintext embeddings or documents. The problem we’re trying to solve: every vector DB today requires plaintext on the server. If you're doing RAG over sensitive data (medical, legal, financial), your embeddings — which researchers have shown can be inverted to recover original text — sit exposed on someone else's infrastructure. XTrace encrypts everything on your machine first. Vectors get Paillier homomorphic encryption, text gets AES-256. The server stores and searches only ciphertexts. Your keys never leave your environment. We just open-sourced the SDK (Apache 2.0). You can run the encryption verification tests offline without even creating an account. Trade-offs we're upfront about: there's latency overhead from the encryption operations. We're actively optimizing this. The free tier is rate-limited but fully functional. Happy to answer questions about the crypto approach, architecture decisions, or anything else.

Visit product View on Hacker NewsApr 22, 2026

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, open, plain · Missing: agents, macos, agent

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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