G2

Subhashree S.

Developer

Computer Software

Enterprise (> 1000 emp.)

8/15/2026

"Weaviate Makes Semantic Search and RAG Fast, Flexible, and Easy to Build"

4.5/5

What do you like best about Weaviate?

What I like most about Weaviate is how easy it is to build semantic search and RAG applications around it. The vector search is fast and flexible, and I like that it supports structured metadata along with embeddings, so I can narrow down results without making the retrieval logic overly complicated. Review collected by and hosted on G2.com.

What do you dislike about Weaviate?

The main thing I find challenging with Weaviate is that there can be a learning curve when setting up more advanced configurations, especially around schemas, indexing, and tuning retrieval. It also takes some time to understand how to get the best results from the vector search instead of relying on the default setup. For smaller projects, it can sometimes feel like more infrastructure than I actually need. Review collected by and hosted on G2.com.

What problems is Weaviate solving and how is that benefiting you?

Weaviate mainly helps with the search and retrieval side of AI applications. I use it to store and search vectorized data based on semantic similarity, which is much more useful than relying only on keyword matching. It makes things like RAG and knowledge-base search easier to build, and helps return more relevant context to the application without having to manually manage the retrieval layer. Review collected by and hosted on G2.com.