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That's because this product isn't for you then. My team has been evaluating vector databases for years and everything on the VectorChord page resonated with me. We run one of the world's largest vector databases and we'll likely benchmark vectorchord to see if it lives up to its promises here.


Hi, we’re here to help if you need assistance (via GitHub issue, Discord, or email). Could you let us know the scale of your vectors—are they 1B or 10B?


10B, feel free to email me luke@ the domain in my profile.


Do you have a blog per chance? Or any recommended reading on pre processing / data chunking strategies to improve results?


I recently came across a project that looks promising: [WordLlama](https://github.com/dleemiller/WordLlama?tab=readme-ov-file#s...). It appears to be well-suited for semantic chunking, though I haven’t had a chance to try it out yet.


Maybe check this https://zilliz.com/pricing

Yan easily store 1B data into Zilliz Serverless and the cost is incredible cheap


You cannot compare Serverless with dedicated instance, this is unfair. A large number of writes and queries will eat up your wallet.


That's true. Now we can offer the new dedicated Cost-optimized CU，pricing is

$0.318/hour ($228.96/month)

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


Zilliz gave us a near 6-figure a month quote for our database.




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