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Choosing Between pgvector and Qdrant for Large-Scale Vector Database on Azure – What Do You Recommend?
在 Azure 上选择 pgvector 和 Qdrant 作为大规模向量数据库之间的比较——您有什么建议？
Hey everyone! I’m currently evaluating options for a vector database and am looking for insights from anyone with experience using pgvector or Qdrant (or any other vector databases that might fit the bill).
大家好！我目前正在评估向量数据库的选项，并希望从任何使用过 pgvector 或 Qdrant（或任何其他可能符合条件的向量数据库）的人那里获得见解。
Here's my situation:
这是我的情况：
Cloud provider: I’m tied to Azure for infrastructure. Scale: This project will likely need to scale considerably in the future, so I'm looking for a solution that’s cost-effective, efficient, and scalable. Priorities: I’m most concerned with long-term costs, performance, and scalability. Has anyone worked with pgvector or Qdrant on Azure and could share their experiences? Is there a clear winner in terms of price/performance at scale? Or maybe there’s another vector DB provider I should consider that offers a good balance of quality and price?
云服务提供商：我的基础设施受限于 Azure。规模：这个项目未来可能需要大幅扩展，因此我正在寻找一个具有成本效益、高效和可扩展性的解决方案。优先级：我最关心的是长期成本、性能和可扩展性。有没有人在 Azure 上使用过 pgvector 或 Qdrant，并可以分享他们的经验？在规模方面，价格/性能是否有明显的赢家？或者也许我应该考虑另一个向量数据库提供商，它提供了良好的品质和价格平衡？
Any recommendations or advice would be much appreciated! Thanks!
任何建议或意见都将不胜感激！谢谢！
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I worked extensively with both.
我深入使用过这两种。
pgVector:
Pros: Amazing productivity, easily maintainable, and you get all the stability and upside of Postgres.
优点：极高的生产力，易于维护，并且能获得 Postgres 的所有稳定性和发展潜力。
Cons: Performance tend to be slightly behind Qdrant. At certain scale, becomes hard to work with.
缺点：性能通常略逊于 Qdrant。在特定规模下，使用起来会变得困难。
Qdrant:
Pros: Best in class performance, flexible scaling.
优点：性能一流，扩展灵活。
Cons: You will need 2 DBs, and this is a lot of engineering hours to get right.
缺点：需要两个数据库，并且正确配置需要大量的工程时间。
Overall, I like pgVector and I have a large open-source RAG API built on it. But, Qdrant is really good, and it feels like the pgVector community is always catching up to something Qdrant have or does.
总的来说，我喜欢 pgVector，并且基于它构建了一个大型开源 RAG API。但是，Qdrant 真的很出色，感觉 pgVector 社区总是在追赶 Qdrant 已经拥有或正在做的事情。
and how do you use to deploy your instances? in terms of pricing what do you think?
你通常如何部署你的实例？在价格方面，你有什么看法？
I like a beefy RDS for pgVector, $300/mon and it will be good for a loooong time.
我喜欢为 pgVector 使用一个强大的 RDS，每月 300 美元，这将长期有效。
For Qdrant, my experience was for multi-tenant b2b, and we gave each customer their own Qdrant. We self-hosted, so basically ~$25/mon a month for a good VPS. (It took their application, and qdrant, and an sqlite3).
对于 Qdrant，我的经验是用于多租户 B2B，我们为每个客户分配了各自的 Qdrant。我们自行托管，基本上一个良好的 VPS 每月约 25 美元。（它包含了他们的应用程序、qdrant 和 sqlite3）。
My impression is that Qdrant is much cheaper, or is it because self-hosting and you don't have to pay some of the costs?
我的印象是 Qdrant 要便宜得多，或者是因为自行托管，所以你不必支付一些成本吗？
Mind sharing your github?
能分享一下你的 github 吗？
https://github.com/tjmlabs/ColiVara
Here is the project w/ pgvector
这是使用 pgvector 的项目
I am the main maintainer, so u can go from there
我是主要维护者，你可以从这里开始
I think PGVector would be good enough for your case. Once you would run into performance issues it is not difficult to switch between Vector DB.
我认为 PGVector 对你的情况来说足够好了。一旦你遇到性能问题，切换向量数据库并不难。
What about elastic search?
那弹性搜索呢？
Pgvector is great. Pgvector 很棒。