Sold by: Weaviate
Deployed on AWS
Weaviate Cloud Premium is a fully managed, AI-native database for vector
search, RAG and AI agents, running on shared or dedicated infrastructure with
enterprise SLAs. Premium is sold through a private offer. Contact
sales@weaviate.io before subscribing so we can scope your deployment and issue
an order form with your pricing.
4.4
Overview
Weaviate Cloud Premium is a fully managed AI-native database used to build search, RAG and agentic applications. It combines vector, keyword and hybrid search with structured filtering, built-in vectorization, native multi-tenancy, RBAC and replication.
Premium runs on shared or dedicated infrastructure with availability SLAs up to 99.95%, extended backup retention, SSO and SAML, and enterprise support with a severity 1 response in as little as one hour.
Query Agent turns natural language into precise queries against your data and returns answers with citations. Weaviate Embeddings, which generates vectors inside the platform, is available on Premium Shared and coming to Premium Dedicated.
Weaviate runs on AWS with native integrations for Amazon Bedrock and SageMaker, alongside OpenAI, Anthropic, Cohere, Mistral, Hugging Face and NVIDIA. It supports REST and gRPC APIs with Python, Go, JavaScript, TypeScript and Java clients, and works with LangChain, LlamaIndex, DSPy, Haystack and CrewAI.
Buying through AWS Marketplace draws down your existing AWS commit and runs through procurement you have already approved, so there is no new vendor security review and no changes to your sub-processor agreements.
Weaviate Cloud is SOC 2 Type II audited, with HIPAA available on dedicated deployments on AWS.
How to buy: Weaviate Cloud Premium is sold through a private offer. Contact sales@weaviate.io before subscribing so we can scope your deployment and issue an order form.
Highlights
- Fully managed AI-native database combining vector, keyword and hybrid search with structured filtering, native multi-tenancy, RBAC and replication.
- Query Agent answers natural language questions over your own data and returns citations. Native integrations with Amazon Bedrock and SageMaker.
- Enterprise SLAs up to 99.95%, with SSO, SAML, RBAC and replication. SOC 2 Type II audited, and HIPAA available on dedicated deployments on AWS.
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 |
Dimensions summary
This listing uses a prepaid contract structure with two linked dimensions. You pay a Commit Amount upfront to access the Weaviate Vector Database. Your usage draws down against that commitment. The second dimension, Additional per 1M vector, charges for vector dimensions stored beyond what your commit covers. It is billed per 1 million vector dimensions stored. Pricing scales with the volume of vector data you store, so larger datasets draw down the commitment faster and may trigger the additional per-unit charge. The commitment gives you predictable spend while usage flexes with your storage needs.
Top-of-mind questions for buyers
A vector dimension is one numeric value in a stored vector. Each object you store has a vector made of many dimensions. The total count is the number of objects multiplied by the dimensions per vector. You are billed per 1 million such stored dimensions, plus storage for objects and backups.
Compression does not change the number of vector dimensions stored, so the counted volume stays the same. It reduces the memory and compute needed for search. More aggressive compression yields discounted list rates for vector dimensions, so your per-unit cost can fall even though the stored count is unchanged.
You prepay the Commit Amount to access the database. Your vector-dimension usage draws down against that commitment first. The Additional per 1M vector charge applies to vector dimensions stored beyond what the commitment covers. Larger datasets draw down faster and are the main driver once your prepaid balance runs low.
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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
For support, setup help and enterprise SLAs, visit https://weaviate.io/partners/aws/contactus . To discuss a Premium deployment or pricing, contact sales@weaviate.io . If you cancel your AWS Marketplace subscription, your Weaviate clusters and organization will be deleted. Export any data you want to keep before cancelling.
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.
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Multi-Tenancy and Access Control
Supports native multi-tenancy with role-based access control (RBAC) and replication for enterprise deployments.
Natural Language Query Processing
Query Agent converts natural language questions into precise database queries and returns results with citations.
Enterprise Integration and API Support
Provides REST and gRPC APIs with client libraries for Python, Go, JavaScript, TypeScript, and Java, with native integrations for Amazon Bedrock, SageMaker, OpenAI, Anthropic, Cohere, Mistral, Hugging Face, and NVIDIA.
Security and Compliance Standards
SOC 2 Type II audited with support for SSO, SAML authentication, and HIPAA compliance available on dedicated deployments.
Vector Search Engine
High-performance vector search engine capable of 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
Neural Network Integration
Support for embedding and neural network encoders 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 a purpose-built Rust engine.
Scalable Architecture
Supports scaling from thousands to billions of vectors using On-Demand elastic scaling or Dedicated Read Nodes for provisioned capacity.
Security and Compliance
SOC 2 Type II and HIPAA certified with security enforced at the data layer for enterprise-grade protection.
Ecosystem Integration
Provides 50+ integrations with popular AI frameworks and data platforms, including deep support for Amazon Bedrock and SageMaker.
Standard contract
No
No
Customer reviews
Yogesh K.
Weaviate Makes Vector Search and AI Apps Feel Straightforward
Reviewed on Oct 15, 2026
Review provided by G2
What do you like best about the product?
What I like most about Weaviate is how it makes working with vector search and AI-powered applications feel much more straightforward. It provides a flexible way to store data and search it by meaning, not just exact keywords, which I find especially helpful when building AI solutions. I also appreciate its developer-friendly approach and the way it can slot into different application architectures without making the overall workflow feel unnecessarily complicated.
What do you dislike about the product?
One area that could be improved is the initial learning curve, especially for developers who are new to vector databases and semantic search. It can take a while to understand the various configuration options and to choose the right setup for a specific use case. While the documentation is helpful, adding more practical, end-to-end examples for real-world applications would make it easier to get started and to troubleshoot issues more quickly.
What problems is the product solving and how is that benefiting you?
Weaviate helps solve the challenge of retrieving relevant information from large amounts of data when exact keyword searches are not enough. Its semantic search capabilities make it easier to find information based on the meaning and context of a query, which is particularly useful when building AI-powered applications and retrieval-augmented generation (RAG) workflows. This can reduce the effort required to build a reliable search layer and help developers connect relevant information to AI models more efficiently. I also appreciate the flexibility it offers for integrating search into existing applications as requirements evolve.
Pankaj P.
A Powerful and Flexible Vector Database for AI Applications
Reviewed on Oct 13, 2026
Review provided by G2
What do you like best about the product?
I like Weaviate because it makes vector search straightforward and helps developers build AI-powered applications more efficiently. Its semantic search capabilities, along with how easily it integrates with AI tools, make it especially useful for development work.
What do you dislike about the product?
Weaviate can take a little time to learn at first, especially if you’re new to it. I also found that the documentation could be clearer and more straightforward in a few areas, which would make the initial setup process and troubleshooting much easier.
What problems is the product solving and how is that benefiting you?
Weaviate helps solve the challenge of searching through large volumes of data and finding relevant information based on meaning, not just keywords. It makes it easier to build AI applications by improving search accuracy and supporting faster information retrieval, which ultimately saves development time and reduces effort.
Bart K.
Powerful Vector Search with Real Tuning Knobs and Smooth AI Stack Integrations
Reviewed on Oct 07, 2026
Review provided by G2
What do you like best about the product?
I like being able to keep vectors and application data living together in one place. Generative Search is especially interesting to me when retrieval is embedded directly inside an AI workflow. Once the index and query patterns are tuned properly, performance stays predictable and consistent. The integrations also fit nicely around the AI application stack I’m working with. I appreciate that the vector index configuration gives me real engineering knobs to turn, rather than feeling like a black box. Multi-tenancy aligns well with the SaaS architecture I work with, and filtering combined with vector retrieval feels like a natural, practical pairing.
What do you dislike about the product?
Bad schema decisions tend to stick around. Debugging vectors is a different beast than debugging SQL, and it takes a different mindset and tooling. Pricing also needs to be grounded in workload math, not based on a generic ROI assumption.
What problems is the product solving and how is that benefiting you?
Hybrid search feels like the obvious route. In practice, though, the architecture keeps accumulating separate retrieval components over time, and Weaviate consolidates a big part of that stack. The AI answer often sounds right on the surface, but the supporting context can be weak. When that happens, I trace the retrieval layer first instead of immediately blaming the model.
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.