VectorLiteDB – a vector DB for local dev, like SQLite but for vectors

Hacker News

I built [VectorLiteDB ( https://github.com/vectorlitedb/vectorlitedb ) — a simple, embedded vector database that stores everything in a single file, just like SQLite. The problem: If you’re a developer building AI apps, you usually have two choices for vector search - Set up a server (e.g. Chroma, Weaviate) - Use a cloud service (e.g. Pinecone) That works for production, but it’s overkill when you just want to: - Quickly prototype with embeddings - Run offline without cloud dependencies - Keep your data portable in a single file The inspiration was *SQLite* during development — simple, local, and reliable. The solution: So I built VectorLiteDB - Single-file, embedded, no server - Stores vectors + metadata, persists to disk - Supports cosine / L2 / dot similarity - Works offline, ~100ms for 10K vectors - Perfect for local RAG, prototyping or personal AI memory Feedback on both the tool and the approach would be really helpful. - Is this something that would be useful - Use cases you’d try this for

Visit product View on Hacker NewsSep 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, single · Missing: mac, agents, macos

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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