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Which vector database should I use? A comparison cheatsheet

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Re: Which vector database should I use? A comparison cheatsheet

#31

Post by andre-z »

echelon wrote: post #9
navidres wrote: post #2

I have prepared this comparison table to help me choose a vector database. I am sharing it here, hoping it may assist you in your projects as well. Main comparison points: cost at scale, compliance, and queries per second (QPS).

I was looking at Pinecone, but if I'm reading this correctly, several open source vector DBs can pull off the same or better QPS and are open source. I really hope Pinecone doesn't become the defacto vector DB. They're getting all the attention, but they're closed and crazily venture funded. That's going to turn into an Oracle situation fast. I understand wanting to keep Amazon out of your business, but licences exis…

Likely there are open-source alternatives, also with managed cloud offerings https://github.com/qdrant/qdrant Disclaimer: I’m from the team.

Re: Which vector database should I use? A comparison cheatsheet

#33

Post by simonw »

anthlax wrote: post #30

Coming at this from a diffeeent angle, does anyone have any links to tutorials for use-cases? I’d love to see what vectorDB hype is about but as a regular engineer I’m unable to even grasp how to use a vectorDB

I wrote one here: https://simonwillison.net/2023/Jan/13/semantic-search-answer...

Re: Which vector database should I use? A comparison cheatsheet

#34

Post by simonw »

andre-z wrote: post #29
vinni2 wrote: post #3

Earlier quoted context omitted.

Wondering why you didn’t include Elasticsearch [0] in your comparison. Also having some benchmark to compare performance would help. [0] https://www.elastic.co/guide/en/elasticsearch/reference/curr...

I guess because ES/OS are text search engines and not vector databases. Some benchmarks: https://qdrant.tech/benchmarks/

Follow that link - Elastic had vector features now.

I find vector search more convincing as a feature of an existing database than as justification to design an entirely new database - it's basically a new type of index.

Re: Which vector database should I use? A comparison cheatsheet

#35

Post by ZephyrBlu »

anthlax wrote: post #30

Coming at this from a diffeeent angle, does anyone have any links to tutorials for use-cases? I’d love to see what vectorDB hype is about but as a regular engineer I’m unable to even grasp how to use a vectorDB

Not a tutorial, but TLDR vector DBs are specialized DBs that store embeddings. Embeddings are vector representations of data (E.g. text or images), which means you can compare them in a quantifiable way.

This enables use cases like semantic search and Retrieval-Augmented Generation (RAG) as mentioned in the article.

Semantic search is: I search for "royal" and I get results that mention "king" or "queen" because they are semantically similar.

RAG is: I make a query asking, "tell me about the English royal family", semantically similar information is fetched using semantic search and provided as context to an LLM to generate an answer.

Re: Which vector database should I use? A comparison cheatsheet

#36

Post by shon »

* Shameless plug and a free ticket:

Etienne Dilocker, The Co-founder/CTO of Weaviate and Ram Sriharsha, the VP of R&D at Pinecone are both presenting at The AI Conference.

Lots of other smart people are presenting including Nazneen from Hugging Face, Harrison from Langchain, Jerry from Llamaindex, Ben the co-founder of Anthropic and many more.

A hackathon is happening in the evening at the event as well.

If you can't make the event, we'll put up all the talks on YouTube post-event.

More info at https://aiconference.com

Here are 5 free tickets to the event: www.eventbrite.com/e/487289986467/?discount=hack4free

Please only take one ticket each. They are first come, first served.

*This is my event -- Shameless plug *

Happy Monday!

Re: Which vector database should I use? A comparison cheatsheet

#38

Post by readyplayeremma »

vinni2 wrote: post #3
navidres wrote: post #2

I have prepared this comparison table to help me choose a vector database. I am sharing it here, hoping it may assist you in your projects as well. Main comparison points: cost at scale, compliance, and queries per second (QPS).

Wondering why you didn’t include Elasticsearch [0] in your comparison. Also having some benchmark to compare performance would help. [0] https://www.elastic.co/guide/en/elasticsearch/reference/curr...

I can also add one more data-point in favor of Elastic / OpenSearch. They benefit from a long history of providing search-specific features. Including the ability to write custom re-ranking functions to combine the benefits of traditional TF/IDF style search with the modern benefit of vector search techniques. And you can easily use OpenSearch with state of the art open embedding models like SGPT that use 2048-dimensional vectors. Plus, it is designed to be highly scalable and distributed.

Given how well OpenSearch works and scales, I would find it hard to justify a specialized vector-specific database unless it brought A LOT of new benefits to the table. And I am not currently aware how any of them would actually do that.

Also, OpenSearch provides all of that out-of-the-box. You just configure a vector field mapping and start inserting your data. No need for an add-on plugin/extension. It just works.

Re: Which vector database should I use? A comparison cheatsheet

#39

Post by gk1 »

echelon wrote: post #9
navidres wrote: post #2

I have prepared this comparison table to help me choose a vector database. I am sharing it here, hoping it may assist you in your projects as well. Main comparison points: cost at scale, compliance, and queries per second (QPS).

I was looking at Pinecone, but if I'm reading this correctly, several open source vector DBs can pull off the same or better QPS and are open source. I really hope Pinecone doesn't become the defacto vector DB. They're getting all the attention, but they're closed and crazily venture funded. That's going to turn into an Oracle situation fast. I understand wanting to keep Amazon out of your business, but licences exis…

Hey, I'm from Pinecone. What we've found in the past two years is people need a lot more from a vector database than raw speed on a single-node index.

In practice, as long as search latency meets requirements (like, say, 100ms p95), the deciding factors tend to be things like cost for a given scale, amount of engineering overhead required or saved, reliability, features that affect search quality such as filtering and hybrid search, and so on.

Everyone has different workloads and different need. For example, I wouldn't recommend Pinecone to someone who just needs a pure ANN index like Faiss or HNSW on a single machine. Try out a few options and see what works for you... We make Pinecone easy + free to try for exactly this purpose, so you don't have to rely on a barebones "comparison" table from a third party.

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