Sold by: Weaviate
The easiest way to build and scale AI applications
4.4
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 AWS SageMaker, AWS Bedrock, OpenAI, Cohere, Anthropic, 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 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 |
Weaviate Cloud Shared (HA, Standard SLA) per 1M Dimensions Stored-min $75 pm | Weaviate Cloud Shared (HA, Standard SLA) per 1M Dimensions Stored (min $75 pm) | $0.285 |
Weaviate Cloud Shared (non-HA, Standard SLA) per 1M Dimensions Stored-min $25 pm | Weaviate Cloud Shared (non-HA, Standard SLA) per 1M Dimensions Stored (min $25 pm) | $0.095 |
Dimensions summary
You pay based on usage, measured per 1 million vector dimensions stored per minute. Two configurations set the rate. The HA (high availability) option carries a higher per-unit rate and a monthly minimum charge. The non-HA option carries a lower per-unit rate and a lower monthly minimum. Both apply the Standard SLA. Your cost scales with how many dimensions you store and how long you store them. The monthly minimum applies when usage falls below that floor. Choose HA for redundancy or non-HA to reduce the minimum spend.
Top-of-mind questions for buyers
You are billed per 1 million vector dimensions stored, metered by the minute. A dimension is a value in a stored vector. The count reflects how many dimensions you keep in the database and how long they remain there. Storing more vectors, or larger vectors, raises the dimension count and the charge.
Both meter the same unit: dimensions stored per minute under the Standard SLA. The HA option adds redundancy and carries a higher per-unit rate with a higher monthly minimum. The non-HA option carries a lower per-unit rate and a lower monthly minimum. You choose based on redundancy needs versus minimum spend.
A monthly minimum applies to each option. If your metered dimension usage falls below that floor, you pay the minimum charge for that configuration. The HA option has a higher minimum; the non-HA option has a lower one. Usage above the floor is billed per unit stored.
docs.weaviate.io
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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) .
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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 or reach out to support@weaviate.com to request additional support.
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
Enterprise Vector Database Platform and Assurance Support Services sold as a Weaviate UNITS SKU that customer can flexibly use
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.
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 product has charges associated with it for the provision and deployment of the application and AMI support.
This product has charges associated with it for seller support. Weaviate is an open-source vector database that enables developers to store, search, and manage data using AI powered vector embeddings for semantic search, recommendation, and generative AI applications.
Customer reviews
anish k.
Seamless Hybrid Search That Speeds Up Production-Grade RAG
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
Its seamless hybrid search combining BM25 and vector search and native module integrations. It makes setting up, scaling, and retrieving data for production-grade RAG applications remarkably fast and easy.
What do you dislike about the product?
The initial setup and GraphQL/API query structure have a steep learning curve for new teams. While the documentation is improving, debugging complex filter queries and schema errors can still be time-consuming.
What problems is the product solving and how is that benefiting you?
Traditional SQL and keyword-based databases often fall short when queries include synonyms, misspellings, or more conceptual matches. Weaviate, on the other hand, focuses on intent and context instead of relying on exact string matching.
Muhammad O.
Weaviate Makes Vector Search and Embeddings Simple
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
What I like most about Weaviate is how straightforward it makes working with vector search and embeddings. The setup feels intuitive, and it’s easy to store, search, and retrieve relevant data for AI applications without adding unnecessary complexity to my workflow.
What do you dislike about the product?
What I dislike about Weaviate is that some of its advanced features and configuration options can feel a bit overwhelming at first. It can take a while to understand the different settings and to get everything configured exactly the way you want.
What problems is the product solving and how is that benefiting you?
Weaviate simplifies how we store and search vector data for AI applications. It makes it easier to retrieve relevant information quickly, which cuts down on manual searching and keeps our AI workflows more efficient, organized, and easier to manage.
Nirmal K.
Gold-Standard Hybrid Search for Highly Accurate RAG Retrieval
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
It natively supports hybrid search, allowing developers to combine dense vector search (for semantic meaning) with traditional keyword search (BM25). This is widely considered the gold standard for retrieving highly accurate context in RAG applications.
What do you dislike about the product?
Instead of using standard SQL, Weaviate's primary query language is a custom implementation of GraphQL. While powerful for complex graph relationships, developers accustomed to traditional relational databases often report a steep learning curve.
What problems is the product solving and how is that benefiting you?
Beyond just storing isolated vectors, Weaviate allows you to define cross-references and relationships between data objects (similar to a graph database), making it easier to represent highly complex, interconnected enterprise data.
Harshul S.
Easy, Scalable Vector Search with Solid Performance
Reviewed on Aug 11, 2026
Review provided by G2
What do you like best about the product?
What I like best about Weaviate is how easy it makes working with vector search at scale. The setup is straightforward, the performance is solid, and it handles embeddings without forcing you into complicated configurations. It feels like a tool built to get real semantic search running quickly.
What do you dislike about the product?
The only downside is that some of the more advanced configuration options feel a bit scattered. When you’re trying to fine‑tune performance or adjust hybrid search behavior, it takes a bit of digging through docs and settings. It’s powerful, but not always as straightforward as the basics.
What problems is the product solving and how is that benefiting you?
Weaviate solves the problem of building fast, reliable semantic search without having to manage a lot of custom infrastructure. Instead of stitching together your own vector store, index logic, and retrieval pipeline, it handles all of that cleanly. The benefit is quicker development, better search accuracy, and less time wasted maintaining your own search stack.
Computer Software
Weaviate’s Hybrid Search Makes Semantic Video Discovery Effortless
Reviewed on Aug 10, 2026
Review provided by G2
What do you like best about the product?
What stands out most about weaviate is its native hybrid search and multi-tenancy capabilities, which make combining BM25 keyword matching with dense vector search effortless. It allows us to deliver ultra-fast semantic video discovery and personalized viewer recommendations across huge OTT metadata catalogs.
What do you dislike about the product?
Setting up self hosted clusters and tuning HNSW index memory parmeters for large scale video catalogs requires significant infrastructure overhead. Additionally, breaking SDK changes between major version updates can require unexpected maintenance for our automated OTT metadata ingestion pipelines.
What problems is the product solving and how is that benefiting you?
Weaviate solves the challenge of organizing and searching millions of unstructured video transcripts, viewer logs, and show metadata in real time. It benefits our OTT platform my powering instant, highly accurate semantic search and personalized content recommendations, which keeps subscribers engaged longer.