Sold by: Weaviate
Deployed on AWS
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.
4.6
Overview
A SaaS solution built on the popular open-source low-latency vector database. Benefit from out-of-the-box support for multimodal media types (text, images, etc.) and seamlessly combine vector search with structured filtering. Leverage the fault tolerance of a cloud-native database accessible through a variety of client-side programming languages, enhancing your data capabilities effortlessly.
Please note that if you cancel your SaaS Marketplace Subscription, Weaviate will delete your clusters and your Weaviate organization.
Highlights
- End-to-end vector database for vector similarity search, hybrid search, and advanced filtered search.
- Optional integrations with SageMaker, Bedrock, OpenAI, Cohere, HuggingFace, and many others.
- Suited for vector search, retrieval augmented generation (RAG), and generative search.
Details
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Commit Amount | Contract commit amount to access Weaviate Vector Database | $10,000.00 |
Dimension | Description | Cost/unit |
|---|---|---|
Additional per 1M vector | Per 1M vector dimensions stored. | $0.285 |
Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law.
Custom pricing options
Request a private offer to receive a custom quote.
Legal
Vendor terms and conditions
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Please visit https://weaviate.io/partners/aws/contactus to request support and setting up support contract for critical response SLA, the setup of support channels (Slack, email, and phone), and to receive optional complementary training from experts at Weaviate.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Weaviate
By Qdrant
By Pinecone
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Vector Similarity Search
End-to-end vector database supporting vector similarity search, hybrid search, and advanced filtered search capabilities.
Multimodal Data Support
Out-of-the-box support for multimodal media types including text, images, and other data formats.
Structured Filtering
Ability to seamlessly combine vector search with structured filtering for refined query results.
Cloud-Native Architecture
Fault-tolerant cloud-native database architecture deployed within AWS tenant and VPC with low-latency performance.
Third-Party AI Model Integration
Optional integrations with SageMaker, Bedrock, OpenAI, Cohere, HuggingFace, and other AI/ML platforms.
Vector Search Engine
High-performance vector search engine for storing, searching, and managing vector embeddings with production-ready service capabilities
Advanced Filtering Support
Extended filtering capabilities on additional metadata fields that can be stored as payload along with vector embeddings
Flexible Storage Options
Multiple storage configuration options to support various deployment and scalability requirements
API Interface
Convenient API for storing, searching, and managing vectors with payload support
Unstructured Data Processing
Support for neural network encoders and embeddings to enable matching, searching, and recommendation applications on unstructured data
Hybrid Search Capabilities
Combines semantic and keyword search with integrated reranking to deliver relevant results across different query types.
Low-Latency Vector Retrieval
Achieves 20-100ms search latency on billion-vector datasets with real-time indexing and purpose-built Rust engine architecture.
Scalable Infrastructure Options
Supports elastic On-Demand scaling for variable traffic and Dedicated Read Nodes for provisioned read capacity with 99.9% uptime SLA.
Security and Compliance Certifications
SOC 2 Type II and HIPAA certified with security enforced at the data layer for enterprise deployments.
AWS Ecosystem Integration
Deep integration with Amazon Bedrock, SageMaker, and 50+ popular AI frameworks and data platforms through a unified API.
Standard contract
No
No
Customer reviews
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.