SQLite’s perfect if you’ve got rows and tables. Valori’s for when you’ve got embeddings and chaos.

I really enjoyed this article. I have a lot of appreciation for PG, but some articles tend to exaggerate its capabilities, especially when it comes to PG vectors, which can be off-putting."

check https://github.com/zilliztech/VectorDBBench

Author of this article.

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.

By the way, if you’re not fully satisfied with S3Vector’s write, query, or recall performance, I’d encourage you to take a look at what we’ve built with Zilliz Cloud. It may not always be the lowest-cost option, but it will definitely meet your expectations when it comes to latency and recall.

While your technical analysis is excellent, making judgements about workload suitability based on a Preview release is premature. Preview services have historically had significantly lower performance quotas than GA releases. Lambda for example was limited to 50 concurrent executions during Preview, raised to 100 at GA, and now the default limit is 1,000.

Thanks for writing a balanced article - much easier to take your arguments seriously! And a sign of expertise.

Curious—why the shift from a Milvus-compatible API to a Chroma-compatible one? And of course, something in Python… because that’s obviously the fastest way to conquer the world.

Code Context is an MCP plugin that brings semantic code search to Claude Code, Gemini CLI, or any AI coding agent.

Full codebase indexing means richer context and better code generation.

100% open-source.

Same as cursor, We update every 5 minutes. You can pick a local storage or using cloud services for sure

Postgres users often hit scaling issues — whether it's with LISTEN/NOTIFY, PGVector, or even basic relational queries.

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.

Also let us know your ideas! How do we make code search easier

Most vector database benchmarks today focus on toy use cases: static data, pure ANN search, no filters, no writes.

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.