Vector Database reviews from Reddit
Summary
We analyzed 157 Reddit reviews across 14 subreddits and 31 posts to rank the best Vector Database brands recommended by redditors, including communities like r/LangChain, r/vectordatabase, r/Rag, r/learnmachinelearning, r/ChatGPT. Top-rated brands include Qdrant (4.4/5), Milvus (4.3/5), Pinecone (3.8/5).
Stats
Reviews157
Subreddits14
Posts31
Brands54
Products24
157 reviews from
and
By Brand
/By Product
#1
Qdrant
4.4
(16)
"Quadrant, it's a vector DB designed specifically for RAG/AI workflows."
"Qdrant hands down. Its the most performative and flexible."
"Switched to Qdrant and performance boost was incredible, night and day."
"I really encourage you to use Qdrant. It's by far one the best."
"Qdrant has the best performance for big data use cases, significantly outperforming Elastic."
"Satisfied"
"If you end up choosing Qdrant"
"Very happy with Qdrant"
"It offers both local and cloud, and recently we've introduced an in-memory mode, so you can use it for testing even without spinning a container."
"For Qdrant, my experience was for multi-tenant b2b."
#2
Milvus
4.3
(15)
"Milvus is great at storing a large amount of data and can provide ScaNN if you want to optimize your cost further."
"Good documentation and good support so far"
"Looks interesting"
"Easiest to work with"
"Clear winner"
"The only vector database I’ve seen working in production on billion scale vectors with sub 100ms latency and thousands of RPS is Milvus."
"Milvus, Qdrant, and ApertureDB."
"Vespa, Qdrant, Milvus are quite good."
"Check out the pip install-able ones like Milv"
"I currently use Milvus, it is quite good"
#3
Pinecone
3.8
(13)
"I still would recommend Pinecone. First, they have a very generous free tier that covers most of the personal projects unless it's really big."
"Pinecone really works well for large datasets. Performance wise, it's top-notch."
"If you end up choosing Pinecone"
"Easy way to get started and they have a generous free tier"
"Pinecone has been awesome for me so far."
"Some common ones are (not in any order, I'm not affiliated with any): - Pinecone: private, cloud based, very popular"
"Pinecode cause of its documentation."
"Can i use pinecone and create 23 indexes"
"A managed service that provides benefits similar to open-source vector databases."
"Pine cone I’ve heard is good"
#4
Weaviate
4.4
(9)
"If you end up choosing Weaviate"
"Very cool comparison"
"Graph like properties"
"Weaviate is open source and offers many ways to use it."
"We are using weaviate and are happy with it."
"Weaviate is my choice"
"Weaviate and tenants are optimized for lots of collections."
"We hope it can help people trying to pick a vector database."
"Me like weaviate."
#5
pgvector
3.9
(10)
"Pgvector is great."
"I am using pgvector (Self hosted);"
"Solve most of your problems with a few lines of docker-compose."
"I think PGVector would be good enough for your case."
"Pgvector (azure flexible server)"
"The plugin based like pgvector and elasticsearch’s can just do fine."
"PgVector works fine for us"
"We use pgVector, we didn't love Pinecone."
"Pgvector ? CrateDB ?"
"Complaints about pgvector's performance"
#6
PostgreSQL
4.9
(7)
"And yes Postgres plus pgvector is the way to go."
"PostgreSQL + pgvector is all you need"
"Postgres is all you need"
"Sqlite or postgres with pgvector"
"Get a cheap server from hetzner or DO too."
"Scalability, performance and cost"
"You could use the pgvector extension for it and not need to manage another database infrastructure"
#7
lanceDB
4.8
(6)
"I used LanceDB and was quite impressed with the results."
"Check it out for sure."
"LanceDB or DuckDB sounds perfect."
"Lancedb"
"Dead simple set up, on disk, can have thousands of collections and its quick"
"Hosting lancedb (it is really small and has good speed) in the dockerized AWS lambda"
#8
Faiss
4.5
(6)
"We love FAISS"
"FAISS for efficient searches"
"Use FAISS"
"FAISS?"
"I would use FAISS with OpenAI Embeddings."
"If speed is your priority, you might want to consider vector library instead - Faiss and run it on GPU"
#9
Chroma
4.0
(5)
"Consider using Chroma - Chroma is build to make it very cheap and easy to have a large number of individual collections/indexes."
"If you end up choosing Chroma"
"Chroma's docker-compose implementation uses Clickhouse under the hood."
"I am using chroma for now."
"Chroma, Milvus, whatever, just don’t waste money and time on the managed ones like pinecone"
#10
DataStax
4.3
(4)
"AstraDB is free to sign-up and use at low volume, cloud-based, and easy to use with a great Python library."
"Astra DB offers superior performance over Pinecone, with a solid free tier and a JSON API."
"Outperforms Pinecone in many metrics"
"Datastax was too hard to use"
#11
OpenSearch
4.0
(4)
"I recommend OpenSearch a lot because it’s open source, has strong support for compliance standards like FIPS and provides the most flexibility for vectorization"
"If you want a good ecosystem of plugin support, authentication and authorization options and a very flexible vector engine OpenSearch works great."
"OpenSearch is a good alternative for scaling."
"Don’t forget OpenSearch."
#12
ObjectBox
5.0
(3)
"The vector extension for ObjectBox DB is a great solution for local AI on various devices."
"We just released the very first on-device vector database for Mobile: ObjectBox - it supports Flutter / Dart and Android."
"ObjectBox is a powerful on-device vector database that supports Android and Dart/Flutter."
#13
Vespa
4.3
(3)
"If you’re looking to just do vectors and doing it the massive scale you should use Vespa."
"Vespa, Qdrant, Milvus are quite good."
"If you are more of a ML shop or need bleeding edge relevancy features then Vespa is probably the better option."
#14
MyScale
4.0
(3)
"Try MyScale (https://myscale.com/), which offers a free tier capable of hosting up to 5 million vectors."
"How about trying MyScale, an SQL vector database?"
"MyScale claims that it offers an expanded range of vector indexes (IVF/HNSW) on top"
#15
Redis
4.0
(3)
"Been very happy with it"
"The graph query api inside Redis is great and based on http://www.opencypher.org/; free."
"Requires a lot of overhead"
#16
ClickHouse
3.7
(3)
"Depending on the use case, you may be able to use ClickHouse directly"
"You may be able to use ClickHouse directly - this vector search w/ ClickHouse guide might help."
"ClickHouse has integrated ANNOY and can be used as a vector database."
#17
OpenAI
5.0
(2)
"Qdrant (written in Rust) as an embedding vector database"
"Qdrant (written in Rust) as an embedding vector database"
#18
Marqo
5.0
(2)
"Marqo provides end-to-end vector search with advanced features like embedding generation and intelligent chunking."
"Pretty slick"
#19
Astra
5.0
(2)
"It's free to sign-up and use at low volume, it's cloud based and it can store both your app data (scored, games, etc) and vector data. The JSON API is easy to use and it has a great Python library."
"Superior performance over pinecone"
#20
Elasticsearch
4.5
(2)
"Support for vectors"
"Elasticsearch scales well for our needs."
#21
Llmware
4.5
(2)
"Auto embedding your PDF or Office docs with native parsing, auto text chunking and embedding into Milvus (free), FAISS (free) or Pinecone"
"Check out LLMWare."
#22
PostgreSQL with PGVector
4.5
(2)
"Works like a champ"
"I have heard postgres with PGvector is great!"
#23
ChromaDB
2.3
(3)
"ChromaDB is amazing."
"Any reason why ChromaDB is barely suggested in these questions? Not prod-ready?"
"Not ChromaDB. It basically stores everything in memory via sqlite. Stay away."
#24
SQLite
3.5
(2)
"SQLite"
"There is extension for sqlite for vector search"
#25
Datastax
5.0
(1)
"Cassandra/AstraDB is great for scale and is the only DB that won't fall over if your project hyperscales."
#26
Microsoft
5.0
(1)
"Offers solid speed, capacity, and scalability."
#27
Spotify
5.0
(1)
"Annoy is fast, open source, and can be self-hosted."
#28
txtai
5.0
(1)
"The default mode combines Faiss and SQLite."
#29
MyScale DB
5.0
(1)
"Generous free tier and excellent performance for both structured and vectorized data."
#30
SuperDuperDB
5.0
(1)
"None"
#31
AstraDB
5.0
(1)
"It's free to sign-up and use at low volume, it's cloud based and it can store both your app data (scored, games, etc) and vector data. The JSON API is easy to use and it has a great Python library."
#32
Supermicro
5.0
(1)
"32 dimm slots"
#33
Apache
5.0
(1)
"Store regular data together with embeddings in the same table"
#34
Hetzner
5.0
(1)
"Get a Hetzner server for 70 bucks a month"
#35
DuckDB
5.0
(1)
"DuckDB sounds perfect."
#36
Zilliz
5.0
(1)
"The hosted cloud version of milvus, Zilliz, inherits and enhances the powerful performance of milvus"
#37
Neo4j
5.0
(1)
"I'm using neo4j as my vector store."
#38
Superlinked
5.0
(1)
"Check out https://vdbs.superlinked.com for a full comparison of all features across ~40 DBs that have vector search functionality."
#39
Oracle
5.0
(1)
"Puoi provare con la versione 23ai di Oracle, che include un vector DB e può importare modelli di embedding open source."
#40
Postgres
5.0
(1)
"Weaviate could be a good option, and pgvector is super powerful."
#41
Vectara
5.0
(1)
"More than just a vector database - it’s RAG as a service and integrated with Langchain too. Scales super well to large sizes with very low latency."
#42
Infiniflow
5.0
(1)
"Functions, performance, and ease of use are top-notch. This project might be the fastest vector search database."
#43
Vectra
5.0
(1)
"I recently started using Vectra as an in-memory vector DB for some of my Node.js projects."
#44
SemaDB
5.0
(1)
"Open-source Go Lang offers better source code documentation, making it easy to run and integrate."
#45
VectorX
5.0
(1)
"VectorX DB is an encrypted vector database that performs superfast ANN searches without the need for decryption."
#46
4.0
(1)
"I use faiss and it works OK for me."
#47
GreptimeDB
4.0
(1)
"Found greptimedb is quite specialized in vector."
#48
MariaDB
4.0
(1)
"MariaDB has vectors and SQL - especially useful if you already have your data in MariaDB"
#49
Aerospike
4.0
(1)
"Surprised Aerospike hasn't been mentioned yet."
#50
API
4.0
(1)
"Run embeddings on it in a vector database."
#51
Airbyte
4.0
(1)
"You could use Airbyte to connect ClickHouse with Weaviate."
#52
CloseVector
4.0
(1)
"I was looking at CloseVector plus Langchain"
#53
Meilisearch
4.0
(1)
"I would recommend Meilisearch"
#54
FastCodeAI
3.0
(1)
"It’s the first that I encounter and will give it a shot"
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