Weaviate Cloud Premium

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The documentation was excellent and provided a good fit for what we needed to do, including having a hosted service and cloud service with the possibility to have a hybrid search. These features combined with nice pricing were the reasons we chose to use Weaviate Enterprise Cloud .

The pricing is competitive and reasonable. The initial deployment was straightforward and fast. I previously used AWS  for deployment, which was more difficult, but comparing this with Weaviate Enterprise Cloud, it was much easier and faster to implement.

It would be beneficial to have a way to do an optimized comparison between the embeddings that I have and the embeddings that exist in the vector database.

I started using Weaviate Enterprise Cloud in January of the previous year and used it for around five months while I was at the company.

We did not experience any stability problems.

I cannot speak extensively about scalability because the product I was working with was not that large. However, for our needs, it was sufficient.

I tried other tools in this case, ChromaDB and pgvector. However, pgvector was not good to use because it consumed a lot of space and we would have needed to maintain it internally ourselves. ChromaDB did not have the hybrid search capability. This comparison led us to select Weaviate Enterprise Cloud for our needs.

The initial deployment was straightforward and fast. I previously used AWS  for deployment, which was more difficult, but comparing this with Weaviate Enterprise Cloud, it was much easier and faster to implement.

The setup took no more than three days. Since some time has passed, I do not remember the exact timeline, but it was certainly less than a week. I would estimate three days to fully make it work in the context we were operating in.

In our case, we did not need the data to persist for long periods. I implemented a cleanup schedule to keep the billing at its minimum. The maintenance we needed to perform was only a scheduled deletion for data that we no longer needed.

The documentation was excellent and provided a good fit for what we needed to do, including having a hosted service and cloud service with the possibility to have a hybrid search. These features combined with nice pricing were the reasons we chose to use Weaviate Enterprise Cloud.

The initial deployment was straightforward and fast. I previously used AWS for deployment, which was more difficult, but comparing this with Weaviate Enterprise Cloud, it was much easier and faster to implement.

The pricing is competitive and reasonable.

The setup took no more than three days. Since some time has passed, I do not remember the exact timeline, but it was certainly less than a week. I would estimate three days to fully make it work in the context we were operating in.

I would rate this review a ten out of ten.

Amazon Web Services (AWS)

What do you like best about the product?

I like Weaviate's ability to combine semantic vector search with traditional search capabilities in a scalable, developer-friendly platform. It makes building AI and retrieval-augmented applications much faster and more effective.

What do you dislike about the product?

The main drawback is the initial learning curve. Understanding vector search concepts, embeddings, and configuration can take time for new users, although it becomes easier with experience.

What problems is the product solving and how is that benefiting you?

Weaviate solves the problem of finding relevant information in large amounts of unstructured data by using semantic search instead of relying only on exact keyword matches. This helps retrieve more accurate and context-aware results. For me, the benefit is faster access to relevant information, improved search quality, and the ability to build AI-powered applications such as knowledge bases, chatbots, and retrieval-augmented generation (RAG) systems more efficiently.

What do you like best about the product?

i really like how quick it is to get going with weaviate. you don’t need to spend days messing around with configs or setups. just spin it up and start pushing data in, which makes it perfect when you’re prototyping or just testing ideas. i also like that it handles both vectors and metadata together, so you can try hybrid searches without building a whole extra system. overall, it feels beginner friendly but still powerful enough to run real demos fast

What do you dislike about the product?

the main issue is performance when you try to scale things up. it feels fine for small to medium datasets, but once the load grows the latency can get kinda unpredictable. sometimes queries just take longer than expected even with good hardware. for experiments it’s fine, but for production where speed really matters it can be frustrating. i’d say scaling is the weak point right now.

What problems is the product solving and how is that benefiting you?

weaviate is solving the problem of doing semantic search without needing to glue together 3 different tools. normally you’d need a database for structured data, a search engine for keywords, and some extra service for embeddings. with weaviate it’s all in one place, so you can store objects, vectors, and metadata together. the benefit for me is speed of building stuff. i don’t waste time wiring up multiple systems just to test an idea. i can push in text, run hybrid queries, and see results fast. it also makes building rag pipelines simpler since the vector storage and filtering logic already exists, so i just connect my llm to it. basically it cuts down setup pain and lets me focus on the actual application instead of infra headaches.

What do you like best about the product?

Weaviate makes it incredibly easy to implement semantic search and generative AI applications. The integration with Python and REST APIs is smooth, and the support for hybrid search (vector + keyword) is powerful for real-world use cases. Its modular design and integrations with tools like OpenAI, Cohere, and Hugging Face let you plug in embeddings quickly. The documentation is clear, and the community is active and responsive, which shortens the learning curve.

What do you dislike about the product?

The cloud pricing can scale up quickly if you’re handling large datasets, and the learning curve for more advanced features (like sharding or schema design) can be a bit steep for beginners. Some SDKs lag slightly behind the core feature set, so you occasionally need to rely on REST calls. More built-in visualization or monitoring features would make it easier to track cluster performance without third-party tools.

What problems is the product solving and how is that benefiting you?

Weaviate solves the challenge of building semantic and vector-based search at scale without requiring us to manage complex infrastructure. It allows us to unify structured data with embeddings, making it possible to deliver more accurate and context-aware search and recommendation systems.

What do you like best about the product?

Weaviate stores the data objects as vectors in multidimensional space, so you can search and find relationships between the data based on semantic meaning, resulting in great and stable accuracy.
Their customer support is impeccable, and there's a great community environment too in Slack.

What do you dislike about the product?

Could focus more on AI docs for direct API access.

What problems is the product solving and how is that benefiting you?

Weaviate is creating embeddings, storing them in a vector DB and retrieving them when performing a semantic search for generative augmentation, together as self-contained RAG in Weaviate.

I've also used their transformation agent and I was impressed about the quality of the answers, even though I made some mistakes in the setup at the time.

I subscribe to their cloud instance so that I don't have to deal with user data on my servers, and a great deal of RAG infra moving parts in general. It has reduced cost at scale, and it's easy to provision and configure.