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The Weaviate SaaS Platform offers hassle-free deployment, hosting the vector database cluster within your AWS tenant and VPC. This end-to-end deployment includes the Weaviate Enterprise Terms (support) and Enterprise Service License Agreement, ensuring a comprehensive and supported SaaS experience for your organization.
Reviews (32)
John Venpin
Rapid prototyping has transformed our city intelligence search into reliable production services
Reviewed on Jul 07, 2026
Review provided by PeerSpot
What is our primary use case?
Weaviate Enterprise Cloud serves multiple uses, but predominantly it functions as a vector database that we use to store a database for semantic search retrieval. We have four products that are driven by it: Inteligencity CMS, Inteligencity Signal, Inteligencity Honeybadger, and Inteligencity Deploy Studio, and they all use Weaviate Enterprise Cloud.
A specific example of how Inteligencity Signal uses Weaviate Enterprise Cloud is as follows: we work on city intelligence, so people utilize that product to find events and people within a city, predominantly London. People can find events, all the data is stored in Weaviate, and then semantic search can be conducted, which is very fast and accurate. It provides agents that can be called to get excellent semantic search results.
What is most valuable?
Weaviate Enterprise Cloud helps us achieve those results in Inteligencity Signal with an excellent SDK. The SDK allows developers to integrate with the cloud solution so that the search is very accurate, but also relatively simple to set up. There is not only speed and accuracy, but ease of setup, provided you are a developer. The SDK is the primary reason why we are using Weaviate Enterprise Cloud.
Weaviate Enterprise Cloud is a good all-round product. The support is excellent. I have spoken to people there directly when we have had issues early on, but they were rectified. They also innovate, constantly creating updates and providing new features. They do not stand still.
The best features Weaviate Enterprise Cloud offers include the good SDK, speed, and scaling. It is easy to scale as you can go from zero to massive scaling without encountering issues.
Regarding Weaviate Enterprise Cloud's governance and security, it is good and pretty standard, with nothing that stands out security-wise. The standard approach makes things easy to integrate with what we are doing. We have not had any security issues, and we are not anticipating any.
Regarding Weaviate Enterprise Cloud's accuracy and reliability of output, it is excellent. The reliability of output is very good, and it allows considerable tweaking as well, so we can be very specific in our requirements. The SDK is really excellent, so we can tweak responses and our use of it.
What needs improvement?
The agentic side of things that Weaviate Enterprise Cloud has started is really good. I am actually struggling to think of a way it can be improved. Nothing really comes to mind regarding needed improvements. What tends to happen is that they are actually ahead of when we think of an improvement—they have already done it. There is a lot of innovation going on there.
For how long have I used the solution?
I have been using Weaviate Enterprise Cloud for two to two and a half years.
What do I think about the stability of the solution?
Weaviate Enterprise Cloud is stable, and we have never had any downtime.
What do I think about the scalability of the solution?
Scalability is excellent, really excellent.
How are customer service and support?
The support is excellent. I have spoken to people there directly when we have had issues early on, but they were rectified.
We have excellent customer support. I have spoken to the people in the past on their support, and when we identified a few problems, they identified the solutions, and that was fine.
Which solution did I use previously and why did I switch?
We have experimented with a few other solutions including Pinecone and theoretically Superbase, but we have done some experimentation, and Weaviate has become the default, so we have backed off the experimentation.
Before choosing Weaviate Enterprise Cloud, I evaluated other options including Pinecone and Superbase.
How was the initial setup?
Weaviate offers an initial free trial, which is useful. It lasts for fourteen days, which is nice because it helps with prototypes. When you want to start scaling, the pricing is very competitive. You really have to have huge scaling to incur large costs, so it is fair.
What about the implementation team?
Weaviate Enterprise Cloud impacts our organization positively by allowing us to rapidly prototype products, which is really helpful. We can build a product with the knowledge that all the capabilities and features are there, and then we can rapidly do that. We can turn around initial prototypes in a few hours. Once we prove the prototype, we can eventually scale that as well.
During a team weekend hackathon where we were building a product, the first call was because we needed a vector database, and we went straight to Weaviate Enterprise Cloud. We were able to build that within the weekend, and eventually, we have developed that into a full production product. Because of the rapid prototyping we can do, we effectively work faster as a team.
What was our ROI?
We use Weaviate Enterprise Cloud as a backbone of what we are doing. The only comparison we can make is if we started to build that type of database ourselves, which would be really expensive. Having it out of the box means that we are paying dollars rather than having to develop a whole platform. So that makes it very cost-effective. In terms of metrics, it is difficult because we really have not thought about that, but it is very beneficial. It is probably an amazing return on investment because having to develop the platform from scratch is something we are capable of, but we obviously do not want to do that.
What other advice do I have?
My advice to others looking into using Weaviate Enterprise Cloud is to try the trial. See if you like it and make a limited subset of what you are doing so you can try all the features out. Then see where it takes you. I would rate this product a ten out of ten.
Lucas Pires
Hybrid search in the cloud has accelerated deployment and simplified our data review workflows
Reviewed on Jun 29, 2026
Review from a verified AWS customer
What is our primary use case?
We are a review website for enterprise IT. We publish reviews for other people to read, either publicly or anonymously. We are also working directly with Weaviate Enterprise Cloud to help them better understand what people appreciate, what people dislike, and how they can use the product.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
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.
What do I think about the stability of the solution?
We did not experience any stability problems.
What do I think about the scalability of the solution?
I cannot speak extensively about scalability because the product I was working with was not that large. However, for our needs, it was sufficient.
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
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.
What about the implementation team?
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.
What other advice do I have?
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.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Nanthakumar M.
Weaviate Makes Semantic + Traditional Search Fast, Scalable, and Developer-Friendly
Reviewed on Jun 24, 2026
Review provided by G2
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.
Apoorv D.
easy to start but needs work at scale
Reviewed on Oct 03, 2025
Review provided by G2
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.
Satvik K.
Fast, flexible, and developer-friendly vector database.
Reviewed on Sep 29, 2025
Review provided by G2
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.
Carlos F.
Outstanding RAG and support for customer & community
Reviewed on Jun 10, 2025
Review provided by G2
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.
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.
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.
Computer Software
Clean Interface and Straightforward Setup Make Vector Database Implementation Simple
Reviewed on May 28, 2025
Review provided by G2
What do you like best about the product?
The interface is impressively clean and intuitive, making it easy to navigate even as a newcomer to vector databases. The setup and testing process is refreshingly straightforward - you can get up and running quickly without wrestling through complex configuration steps. What really stands out is their commitment to continuous improvement; they're consistently rolling out new products and features that genuinely make the developer experience easier.
Their free office hours, workshops, and events are incredibly valuable for newcomers - having direct access to experts who can answer questions and provide guidance makes the learning curve much more manageable. The integration process feels well-thought-out, and the documentation guides you through implementation without unnecessary complexity.
Their free office hours, workshops, and events are incredibly valuable for newcomers - having direct access to experts who can answer questions and provide guidance makes the learning curve much more manageable. The integration process feels well-thought-out, and the documentation guides you through implementation without unnecessary complexity.
What do you dislike about the product?
As someone just getting started, it's hard to identify major pain points yet. The learning curve for vector database concepts and who to use them themselves can be steep if you're new to the space, though that's more about the technology category than Weaviate specifically.
What problems is the product solving and how is that benefiting you?
As someone just beginning to explore vector databases, I'm still in the early stages of understanding how Weaviate will fit into my application architecture. From what I've learned so far, Weaviate appears to solve the challenge of efficiently storing and retrieving high-dimensional data for AI applications - particularly for semantic search, recommendation systems, and RAG (Retrieval Augmented Generation) implementations.
While I haven't yet implemented a full production use case, the benefit I'm already seeing is how Weaviate makes vector database concepts more accessible to developers like me who are new to this space. Their clean interface and educational resources (office hours, workshops) are helping me understand not just how to use their product, but how vector databases can enhance applications with more intelligent search and data retrieval capabilities.
I'm exploring use cases around improving search functionality in my applications and potentially implementing AI-powered features, but I'm still in the learning phase of understanding where vector databases provide the most value compared to traditional databases.
While I haven't yet implemented a full production use case, the benefit I'm already seeing is how Weaviate makes vector database concepts more accessible to developers like me who are new to this space. Their clean interface and educational resources (office hours, workshops) are helping me understand not just how to use their product, but how vector databases can enhance applications with more intelligent search and data retrieval capabilities.
I'm exploring use cases around improving search functionality in my applications and potentially implementing AI-powered features, but I'm still in the learning phase of understanding where vector databases provide the most value compared to traditional databases.
Tina Jaykumar C.
Great tool when it works — but sometimes I wish the setup was smoother
Reviewed on May 28, 2025
Review provided by G2
What do you like best about the product?
The most helpful about Weaviate is that you can store, vectorize, and search data all within one system — no need to juggle multiple tools and no need to precompute embeddings it has built in vectorization. Also a good community as in it is actively maintained.
What do you dislike about the product?
If you're new, it can feel like you're piecing things together from scattered sources.
Also, it is heavy to run locally. I used it in my windows laptop and my machine used to groan a bit.
Also, it is heavy to run locally. I used it in my windows laptop and my machine used to groan a bit.
What problems is the product solving and how is that benefiting you?
In my solo hackathon project on women’s safety, Weaviate made it super easy to build a fast, intelligent search over incident reports without worrying about vector storage or custom search logic. It saved me hours I would’ve spent wiring up embeddings and let me focus on actually building something useful.
Keith S.
great product, even better tech support
Reviewed on May 15, 2025
Review provided by G2
What do you like best about the product?
The tech support is fantastic: ticket ownership, fast turn-around times, professional, personable, and proactively willing share product knowledge with the end user to better help them understand the Weaviate product. Thank you.
What do you dislike about the product?
Nothing. We had one issue with our serverless cloud and Weaviate support assigned four engineers to quickly resolve the issue.
What problems is the product solving and how is that benefiting you?
Vector database tied to our AI workloads.
Katerina T.
Easy to use and amazing customer support
Reviewed on Apr 01, 2025
Review provided by G2
What do you like best about the product?
Weaviate was so easy to integrate and use. The documentation is easy to follow, the Weaviate AI is super helpful for navigating common problems, and their customer support is next level! Facing a challenge is somehow a pleasant experience - you get a swift response and an expert perspective on your problem.
What do you dislike about the product?
It would've been great to have PHP instructions in the docs, or just simple HTTP requests.
What problems is the product solving and how is that benefiting you?
We're fully replacing our keyword searches to find relevant data for given criteria, with a smart semantic search. Weaviate returns the closest matches and you can further tune them using their RAG functionality by passing the results through Generative AI. It reduces hours of manual work and improves our internal processes immensely.