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.
4.5
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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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
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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
Muhammed A.
Scalable, Easy-to-Build Vector Search with Seamless RAG Integrations
Reviewed on Jul 30, 2026
Review provided by G2
What do you like best about the product?
Weaviate stands out for making vector search and retrieval applications easy to build while remaining highly scalable. The setup process is straightforward, and the documentation provides clear guidance for getting started. Integration with popular AI frameworks like LangChain and LlamaIndex is seamless, making it simple to build RAG applications. I also appreciate the combination of semantic search, hybrid search, metadata filtering, and fast query performance, which consistently delivers relevant results even when working with large datasets.
What do you dislike about the product?
Managing and optimizing a Weaviate deployment can become challenging as projects grow in size and complexity. Some advanced configuration options, such as clustering, indexing strategies, and performance tuning, require a solid understanding of vector databases to get the best results. While the documentation is comprehensive, it can feel overwhelming for new users exploring advanced features. In addition, resource usage may increase significantly with large datasets, making infrastructure planning important for production environments.
What problems is the product solving and how is that benefiting you?
Weaviate solves the challenge of finding relevant information across large volumes of unstructured data by enabling semantic and hybrid search instead of relying solely on keyword matching. This has made it much easier to build AI-powered search and retrieval workflows that return contextually relevant results, improving the quality of RAG applications and AI assistants. The platform also reduces development time through its integrations with popular embedding models and AI frameworks, allowing projects to move from prototype to production more efficiently while maintaining fast search performance as datasets grow.
Nikita J.
Fast, Intuitive Vector + Semantic Search with a Strong Developer Experience
Reviewed on Jul 30, 2026
Review provided by G2
What do you like best about the product?
What I like most about Weaviate is how it combines vector search with AI-powered semantic search in a way that’s easy to integrate into modern applications. The API feels well designed, so it’s straightforward to build intelligent search and retrieval features without spending a lot of time dealing with complex infrastructure.
The user interface is clean and intuitive, which makes it simple to manage collections, inspect data, and experiment with different search queries. Performance has been consistently fast, even when working with large datasets, and the results are highly relevant because they rely on semantic understanding rather than basic keyword matching.
Another major strength is the integration ecosystem. Connecting Weaviate with embedding models, LLMs, and popular development frameworks is smooth, and it helps speed up AI application development. That flexibility also made it easier for me to prototype and deploy retrieval-augmented generation (RAG) workflows.
From a business perspective, Weaviate has reduced development time by offering built-in capabilities for vector indexing, hybrid search, and AI-powered retrieval, instead of forcing me to stitch together multiple separate tools. The documentation and onboarding experience are well structured, so new users can become productive quickly. When I needed guidance, both the documentation and community resources were genuinely helpful.
Overall, Weaviate delivers strong performance, a great developer experience, and powerful AI capabilities that make building intelligent search applications faster and more efficient.
The user interface is clean and intuitive, which makes it simple to manage collections, inspect data, and experiment with different search queries. Performance has been consistently fast, even when working with large datasets, and the results are highly relevant because they rely on semantic understanding rather than basic keyword matching.
Another major strength is the integration ecosystem. Connecting Weaviate with embedding models, LLMs, and popular development frameworks is smooth, and it helps speed up AI application development. That flexibility also made it easier for me to prototype and deploy retrieval-augmented generation (RAG) workflows.
From a business perspective, Weaviate has reduced development time by offering built-in capabilities for vector indexing, hybrid search, and AI-powered retrieval, instead of forcing me to stitch together multiple separate tools. The documentation and onboarding experience are well structured, so new users can become productive quickly. When I needed guidance, both the documentation and community resources were genuinely helpful.
Overall, Weaviate delivers strong performance, a great developer experience, and powerful AI capabilities that make building intelligent search applications faster and more efficient.
What do you dislike about the product?
My overall experience with Weaviate has been positive, but there are a few areas where it could be stronger. The user interface is functional, yet it would benefit from better visibility into index health, query performance, and cluster status—ideally through more detailed dashboards and monitoring tools. In addition, some advanced configuration options still require frequent trips to the documentation, which can slow down newer users.
Weaviate integrates well with many AI models and frameworks, but setting up more advanced integrations or migrating between embedding models can take extra effort. More built-in templates, guided configuration, and integration wizards would make the setup process smoother and reduce friction.
Performance is generally excellent; however, large-scale indexing or complex hybrid search workloads may require careful resource tuning to get the best results. More automatic optimisation, along with clearer scaling recommendations, would help reduce operational overhead.
On the pricing side, costs can rise as datasets and infrastructure needs grow. Additional cost-management tools and better usage insights would help organisations forecast and optimise spending more effectively.
The documentation is comprehensive, but beginners may still find some advanced topics difficult to navigate. More step-by-step tutorials, end-to-end implementation examples, and practical troubleshooting guides would make onboarding easier.
Finally, while the AI capabilities are powerful, more built-in evaluation tools, explainability features for search results, and simpler model management would make it easier to optimise AI applications and understand retrieval quality. Overall, these improvements would further strengthen an already capable platform.
Weaviate integrates well with many AI models and frameworks, but setting up more advanced integrations or migrating between embedding models can take extra effort. More built-in templates, guided configuration, and integration wizards would make the setup process smoother and reduce friction.
Performance is generally excellent; however, large-scale indexing or complex hybrid search workloads may require careful resource tuning to get the best results. More automatic optimisation, along with clearer scaling recommendations, would help reduce operational overhead.
On the pricing side, costs can rise as datasets and infrastructure needs grow. Additional cost-management tools and better usage insights would help organisations forecast and optimise spending more effectively.
The documentation is comprehensive, but beginners may still find some advanced topics difficult to navigate. More step-by-step tutorials, end-to-end implementation examples, and practical troubleshooting guides would make onboarding easier.
Finally, while the AI capabilities are powerful, more built-in evaluation tools, explainability features for search results, and simpler model management would make it easier to optimise AI applications and understand retrieval quality. Overall, these improvements would further strengthen an already capable platform.
What problems is the product solving and how is that benefiting you?
Weaviate addresses the challenge of efficiently storing, indexing, and searching unstructured data with vector embeddings, which makes it much easier to build AI-powered applications. Rather than relying on traditional keyword-based search, it supports semantic search that surfaces more relevant results based on meaning and context.
In my work, this has cut down the time needed to develop intelligent search and retrieval features. It has also streamlined retrieval-augmented generation (RAG) workflows by combining vector search with large language models, which improves the accuracy and relevance of AI-generated responses. On top of that, its scalable architecture and fast query performance have helped keep the user experience responsive as datasets grow.
Overall, Weaviate has boosted my development productivity, reduced the complexity of managing AI search infrastructure, and made it easier to deliver accurate, context-aware applications with less engineering effort.
In my work, this has cut down the time needed to develop intelligent search and retrieval features. It has also streamlined retrieval-augmented generation (RAG) workflows by combining vector search with large language models, which improves the accuracy and relevance of AI-generated responses. On top of that, its scalable architecture and fast query performance have helped keep the user experience responsive as datasets grow.
Overall, Weaviate has boosted my development productivity, reduced the complexity of managing AI search infrastructure, and made it easier to deliver accurate, context-aware applications with less engineering effort.
Tayyab N.
Efficient Vector Searches with Easy Integration
Reviewed on Jul 22, 2026
Review provided by G2
What do you like best about the product?
I really like Weaviate for its fast and efficient vector search capabilities. The ease of use and ease of data import and querying are impressive, thanks to its extensive Python SDK, which is crucial for my application integrations. It's great for the custom Python applications I build, especially when using FastAPI.
What do you dislike about the product?
I find the initial learning curve a bit steep, but it's worth the effort.
What problems is the product solving and how is that benefiting you?
I use Weaviate for fast and efficient vector search, and its ease of use, including data import and querying, is a big plus. The extensive Python SDK is crucial for integrating Weaviate functionalities into my custom applications.
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)