redskyluan

  Maintainer of Milvus here. A few thoughts from someone who lives this every day:

  1. The free user problem is real, and AI makes it worse. We serve a massive community of free Milvus users — and we're grateful for them, they make the project what it is. But we also feel the tension MinIO is describing. You invest serious engineering effort into stability and bug fixes, and most users will never become paying customers. In the AI era this ratio only gets harder — copy with AI becomes easier than ever

  2. We need better object storage options. As a heavy consumer of object storage, Milvus needs a reliable, performant, and truly open foundation. RustFS is a solid candidate — we've been evaluating it seriously. But we'd love to see more good options emerge. If the ecosystem can't meet our needs long-term, we may have to invest in building our own.

  3. Open source licensing deserves a serious conversation. The Apache 2.0 / Hadoop-era model served us well, but cracks are showing. Cloud vendors and AI companies consume enormous amounts of open-source infrastructure, and the incentives to contribute back are weaker than ever. I don't think the answer is closing the source — but I also don't think "hope enterprises pay for support" scales forever. We need the community to have an honest conversation about what sustainable open source looks like in the AI era. MinIO's move is a symptom worth paying attention to.

nothing is better than sqlite as a library and don't use high perforamnce as your value for a python product

Yes, I’m the founder and maintainer of the Milvus project, and also a big fan of many AWS projects, including S3, Lambda, and Aurora. Personally, I don’t consider S3Vector to be among the best products in the S3 ecosystem, though I was impressed by its excellent latency control. It’s not particularly fast, nor is it feature-rich, but it seems to embody S3’s design philosophy: being “good enough” for certain scenarios.

In contrast, the products I’ve built usually push for extreme scalability and high performance. Beyond Milvus, I’ve also been deeply involved in the development of HBase and Oracle products. I hope more people will dive into the underlying implementation of S3Vector—this kind of discussion could greatly benefit both the search and storage communities and accelerate their growth.

Full codebase indexing means richer context and better code generation.

100% open-source.

For startups, Postgres is a fantastic first choice. But plan ahead: as your workload grows, you’ll likely need to migrate or augment your stack.

That’s not how things work in production.

We built VectorDBBench to benchmark vector databases under real-world conditions. It’s open source and just hit v1.0.0.

What’s new in v1.0.0:

Label Filtering + vector search tests

Concurrent read/write under pressure

Customer dataset

Better webUI

It supports several popular vector DBs out of the box (Milvus, Weaviate, Qdrant, Chroma, etc.) and is easy to extend.

If you’re building anything with RAG, embeddings, or search infra, we’d love feedback.

If you’ve had the chance to manage one—especially while handling concurrent insert and search operations—you’ve probably felt the pain firsthand and understand just how critical scalability really is.

Insert into PGvector is super slow when data accumulate and the index build will take forever

Prepare for scalability with a non-sharded architecture from the start.

"Woodpecker" is a good metaphorical name for a Write-Ahead Log (WAL) service for several reasons of Persistent and Reliable. Woodpeckers repeatedly and persistently peck trees, symbolizing the reliability and consistent logging of events or data.

Search is not a problem . What to search is!

Using reasoning model, it is much easier to split task and focus on what to search

$0.318/hour ($228.96/month)

Which means you can store 20M 768 dimension data in 228$ per month