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Everyone’s talking about vector databases. They’re missing the real opportunity. The shift is: vector search is moving inside your app.
Alibaba just open-sourced Zvec, basically “SQLite for vector search.”
No server.
No daemon.
Just a library you ship with your product.
That matters because most RAG projects don’t fail on embeddings.
They fail on ops.
Latency.
Cost.
Privacy.
And the pain of running yet another service.
Zvec is built on Alibaba’s production engine, Proxima, and released under Apache 2.0.
What it enables is the part teams actually want:
• CRUD so your knowledge base can change daily.
• Hybrid scalar + vector filters so results stay relevant.
• Multi-vector search for richer queries.
• Built-in reranking so you get better top results.
The eye-popping claim is 8,000+ QPS on a 10M dataset.
Even if your mileage varies, the trend is clear.
On-device RAG is getting real.
↓ If you’re building a RAG feature, here’s a simple way to think about it.
↳ If you need maximum control and privacy → embedded vector search.
↳ If you need multi-team scale and shared indexing → managed service.
↳ If you’re prototyping → start embedded, prove value, then decide.
The teams that win will ship faster with fewer moving parts.
What’s stopping your product from going “serverless” for search?
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