Sold by: Weaviate
Deployed on AWS
Weaviate Cloud Flex Shared is a fully managed vector database for building AI applications with hybrid search, RAG, and generative AI on AWS.
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Overview
Weaviate Cloud Flex Shared - Fully Managed AI Vector Database on AWS
Weaviate Cloud Flex Shared is a fully managed SaaS vector database built on the popular open-source Weaviate engine. It enables developers and teams to store data objects alongside their vector embeddings, powering AI applications such as semantic search, retrieval-augmented generation (RAG), recommendation systems, and AI agents - all without managing infrastructure.
Key Capabilities
- Vector and hybrid search - Combine semantic vector similarity with keyword matching and structured metadata filters to retrieve the most relevant results across large-scale datasets
- Multimodal data support - Store and search across text, images, and other media types with out-of-the-box support
- Built-in vectorization - Use pluggable embedding and LLM providers including AWS Bedrock, OpenAI, Anthropic, Cohere, Google, and Hugging Face, or leverage embedding models hosted directly in Weaviate Cloud
- Weaviate Query Agent - Convert natural-language questions into precise database queries and receive answers with source citations
- Native multi-tenancy - Isolate data across tenants with built-in multi-tenancy support for applications serving multiple customers or workloads
- Broad client library support - Connect through a variety of client-side programming languages for seamless integration into your existing stack
Built for AI Workloads on AWS
Weaviate Cloud Flex Shared integrates with AWS services. Whether you are building document Q&A systems, product recommendation engines, knowledge retrieval for customer support, or semantic search across enterprise content, Weaviate provides the low-latency vector storage and retrieval layer your AI applications need.
Cloud-Native Reliability
Benefit from the fault tolerance and scalability of a cloud-native architecture with global coverage on AWS. Weaviate Cloud handles provisioning, scaling, backups, and maintenance so your team can focus on building AI-powered features rather than managing database infrastructure.
Security and Compliance
Weaviate holds SOC 2 Type II certification and offers HIPAA compliance for regulated workloads. The platform includes baseline and enterprise security features, along with native multi-tenancy with data isolation and role-based access control (RBAC) to help organizations meet their security and governance requirements.
Flexible Deployment Options
Weaviate offers multiple deployment tiers to match your needs. The Shared tier available through this AWS Marketplace listing provides a fully managed experience. Weaviate also offers Dedicated clusters for production workloads requiring high availability.
Important Information
Please note that if you cancel your SaaS Marketplace subscription, Weaviate will delete your clusters and your Weaviate organization.
Highlights
- Hybrid search combining vector similarity, keyword matching, and structured metadata filtering in a single query. Weaviate supports pluggable embedding and LLM providers - including AWS Bedrock, AWS SageMaker, OpenAI, Cohere, Anthropic, and Hugging Face - so you can swap models without re-architecting your application. Built on an open-source foundation (BSD-3-Clause license) for transparency and portability.
- Purpose-built for AI workloads including retrieval augmented generation (RAG), generative search, recommendation systems, and AI agents. The Query Agent converts natural-language questions into database queries and returns answers with source citations. Engram provides persistent, personalized memory for AI agents, enabling context-aware interactions across sessions.
- Fully managed cloud-native deployment with native multi-tenancy and data isolation, role-based access control (RBAC), and SOC 2 Type II certification with HIPAA compliance available for regulated workloads. Weaviate Cloud includes a free tier with no credit card required and no expiration, making it easy to evaluate before scaling to production.
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 actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
Weaviate Cloud Shared | Weaviate Cloud Shared | $0.01 |
Not available | Not available - please do not select | $0.285 |
Not available | Not available - please do not select | $0.095 |
Dimensions summary
You buy this listing through one active dimension: Weaviate Cloud Shared, billed by usage. Your cost scales with what you consume rather than a fixed seat or server count. The main usage drivers are the number of vector dimensions stored and your storage volume, with backup storage billed on top. Vector dimension rates vary by index type, compression method, and region. Query Agent and Embedding services run inside the database and bill by usage. The two remaining dimensions are marked "Not available" and should not be selected; they carry no purchasable pricing.
Top-of-mind questions for buyers
A vector dimension is one numeric value in an embedding. Your total is the object count multiplied by dimensions per vector. This number is easy to calculate and reflects the memory and compute your data needs, unlike abstract read/write units or CPU cycles.
Yes. Compression does not reduce the dimension count stored, but it lowers memory and compute for search, which is reflected in discounted dimension rates. List prices also vary by cloud provider and region to reflect regional cost differences.
All three charges apply together. You pay for vector dimensions plus persistent storage of objects and embeddings, with backup storage billed on top based on data volume and retention. Query Agent and Embedding services also meter by usage and add to the same invoice.
weaviate.io
Helpful?
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
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Weaviate provides support for Weaviate Cloud Flex Shared customers through the following channels:
Email Support: Reach the Weaviate support team at support@weaviate.io for assistance with configuration, troubleshooting, and general product questions.
AWS Partner Contact Form: Visit https://weaviate.io/partners/aws/contactus to submit inquiries specific to your AWS Marketplace subscription, including onboarding assistance and account-related requests.
Documentation: Comprehensive product documentation is available at https://weaviate.io/developers/weaviate , covering setup guides, API references, client library usage, and best practices for vector search, RAG, and hybrid search configurations.
For refund requests or billing inquiries related to your AWS Marketplace subscription, please contact support@weaviate.com with your AWS account details and subscription information.
Weaviate Cloud is built on an open-source foundation with an active developer community. Additional resources, tutorials, and community discussions are available through the Weaviate website to help you get started and optimize your deployment.
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.
Similar products
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.
Enterprise Vector Database Platform and Assurance Support Services sold as a Weaviate UNITS SKU that customer can flexibly use
Code Creator Weaviate Vector AI Server deploys Weaviate Community Edition on Ubuntu as a ready-to-launch self-hosted vector database for semantic search, RAG, embeddings storage, and AI application backends. It exposes REST on port 8080 and gRPC on port 50051, and supports secure API based access through official client libraries and standard Weaviate interfaces. Note: this AMI provides the server backend only, so customers should plan to connect using their own API calls, client libraries, or external applications. This is a repackaged open source software product wherein additional charges apply for Code Creator secure first boot configuration, Docker based deployment, persistent storage configuration, REST and gRPC service setup, and AWS Marketplace AMI engineering. Code Creator Weaviate Vector AI Server provides a ready to launch self hosted vector database for semantic search, RAG, embeddings storage, and AI application backends.
Only for accepting private offers. Pinecone is a serverless vector database built to power production AI on AWS. It delivers fast, accurate retrieval with hybrid search, reranking, filtering, and real-time indexing - no infrastructure or tuning required. Purpose-built for scale, Pinecone handles billions of vectors with low latency and high reliability. Teams use Pinecone to power agents, semantic search, recommendations, and RAG pipelines without managing infrastructure or stitching together open-source tooling. With fully managed operations and predictable performance, developers can focus on building intelligent applications instead of operating vector infrastructure.
Customer reviews
Tuli D.
Weaviate Makes Vector Search and Agent Workflows Easy in One Place
Reviewed on Sep 15, 2026
Review provided by G2
What do you like best about the product?
What stands out most about Weaviate is how well it handles both vector search and agent-based workflows in one place. The Collections feature makes organizing and querying data intuitive, and the Agents functionality adds a powerful layer for building AI-driven applications. For personal projects, it strikes a great balance between capability and ease of use, you get enterprise-grade vector database features without needing a complex setup
What do you dislike about the product?
The interface has a bit of a learning curve when you first get started, navigation can feel slightly confusing until you understand how the different components fit together. However, once you get past that initial adjustment period, it becomes intuitive and genuinely impressive to work with.
What problems is the product solving and how is that benefiting you?
Weaviate solves the challenge of storing and retrieving data in a way that goes beyond traditional keyword search, making it possible to build smarter, context-aware applications using vector embeddings. For personal AI projects, it removes the complexity of setting up a vector database from scratch and pairs it with agent capabilities, so you can prototype and test intelligent workflows quickly. It's essentially an all-in-one backend for anyone building with AI.
Vikash K.
Solving exact-match RAG issue for our AI Pipeline.
Reviewed on Sep 12, 2026
Review provided by G2
What do you like best about the product?
I really loved Weaviate's native hybrid search feature. It's perfect for handling complex queries in our insurance claim documents, where adjusters often need to use alphanumeric policy codes with natural language questions. This feature combines BM25 keyword scoring with HNSW vector similarity out of the box, which is fantastic. Another aspect I like is the recent upgrade to the Python client (V4 API), especially the type hinting, which integrates seamlessly with our Python codebase. This has made development much smoother and easier for us. I also appreciate Weaviate's ability to manage both data residency and security constraints effectively and the option to tune infrastructure to control storage costs.
What do you dislike about the product?
In our project, dialing in the hybrid search requires quite a bit of effort and manual tuning, particularly when adjusting the alpha parameter (keyword vs vector) and going through trial and error to get the fusion ranking correct. Additionally, while Weaviate Cloud offers ease of use, managing the open-source version locally via Docker presents noticeable operational complexity compared to a fully managed serverless database.
What problems is the product solving and how is that benefiting you?
In our insurance claims project, Weaviate solves our need for precise document retrieval by using native hybrid search, combining semantic vector and keyword searches, particularly useful in handling specific insurance terms. This improves retrieval accuracy for our claims adjusters.
Anson D.
Weaviate Makes Vector Search Straightforward for AI Experiments
Reviewed on Aug 30, 2026
Review provided by G2
What do you like best about the product?
I like that Weaviate makes it fairly straightforward to work with vector data and search through it. The documentation is useful when setting things up, and the dashboard makes it easy to keep track of the projects and collections. It also works well for experimenting with AI and search-related use cases.
What do you dislike about the product?
It can take some time to understand the different concepts if you're new to vector databases. Some of the configuration options can also feel a little overwhelming at first.
What problems is the product solving and how is that benefiting you?
Weaviate helps with storing and searching vector data, which is useful for AI-based search and retrieval use cases. It gives a convenient way to experiment with semantic search without having to build the whole infrastructure from scratch.
Professional Training & Coaching
All-in-One Open-Source Vector Search Platform for Production-Ready AI
Reviewed on Aug 29, 2026
Review provided by G2
What do you like best about the product?
Weaviate is an all-in-one platform for building vector search, RAG, and agent memory management for us. With it, we design, build, and ship the entire AI stack, from local development through to our AWS production environment. Above all, it’s open source, which helps eliminate vendor lock-in concerns for our organization and also provides the flexibility to customize, improving overall performance.
With its vector database, we store, index, and retrieve different types of media information for our products, which supports scaling AI agentic systems. It also includes an Explorer that runs semantic, keyword, and hybrid search with aggregation, without needing to write GraphQL that saves time for our engineers.
Weaviate embeddings also help deliver efficient, faster models like Snowflake designed for enterprise-level retrieval operations.
With its vector database, we store, index, and retrieve different types of media information for our products, which supports scaling AI agentic systems. It also includes an Explorer that runs semantic, keyword, and hybrid search with aggregation, without needing to write GraphQL that saves time for our engineers.
Weaviate embeddings also help deliver efficient, faster models like Snowflake designed for enterprise-level retrieval operations.
What do you dislike about the product?
Although it has decent features, the UI feels slightly outdated based on my experience. When it comes to integrations with third-party platforms outside of the machine learning ecosystem, it’s not quite there yet. That said, because of the open-source community on GitHub, I expect the number of integrations to grow over time for other tech stacks that we use daily.
It does offer a 7-day free trial, but after that, calculating the overall monthly cost is complicated. It charges different services at different rates, which makes it hard to understand the final price per month. We have to use a calculator to add up all the final costs.
It does offer a 7-day free trial, but after that, calculating the overall monthly cost is complicated. It charges different services at different rates, which makes it hard to understand the final price per month. We have to use a calculator to add up all the final costs.
What problems is the product solving and how is that benefiting you?
Its native data query agent turns natural language–based questions into database queries and operations, which eliminates the time we used to spend writing SQL. This AI-based query agent delivers good results with dynamic filters, cross-collection routing, and source citation for our conversational AI apps, including customer-support chatbots. We can upload or add new databases from our knowledge base into collections, where we’re able to review detailed metadata and properties to evaluate the dataset.
It also includes a fully managed memory for AI agents called Engram, which remembers personalized preferences and decisions across all of our agent sessions. This helps shrink the context window and sends relevant, structured memories to our production agents during customer interactions.
We noticed our database costs dropped by 11% thanks to more efficient resource consumption and an optimized memory footprint. On top of that, it offloads tenant details to cold storage, which further reduces storage usage and cost.
It also secures and isolates our customer data, which is an important safety net for us. The open-source community has created support documentation that’s handy when we need to troubleshoot and fix issues.
It also includes a fully managed memory for AI agents called Engram, which remembers personalized preferences and decisions across all of our agent sessions. This helps shrink the context window and sends relevant, structured memories to our production agents during customer interactions.
We noticed our database costs dropped by 11% thanks to more efficient resource consumption and an optimized memory footprint. On top of that, it offloads tenant details to cold storage, which further reduces storage usage and cost.
It also secures and isolates our customer data, which is an important safety net for us. The open-source community has created support documentation that’s handy when we need to troubleshoot and fix issues.
Anonymous
Efficient Vector Retrieval, Complex Self-Hosting
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
I use Weaviate for efficiently storing vector data, which is crucial for embeddings and the retrieval phase of a RAG implementation. I appreciate its open-source nature and the efficient vector retrieval, which is necessary for ensuring the retrieval phase is accurate and fast. Additionally, AWS support is great, and the initial setup was straightforward.
What do you dislike about the product?
I think finetuning the self-hosting option is a bit complex. The RAM usage can be high depending on the dataset, so if self-hosting, several decisions regarding this have to be made. You have to scale horizontally in some cases if you want to keep it running smoothly. The initial setup was great, but finetuning is not as easy.
What problems is the product solving and how is that benefiting you?
I use Weaviate for efficient vector data storage in a RAG pipeline. It enhances the retrieval phase by being fast and accurate, which is essential as the pipeline is large and can't afford extra latency.