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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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.
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.
Multi-Language Client Support
Accessible through a variety of client-side programming languages for flexible integration.
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
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.