The minimum monthly amount covers the baseline cluster cost and includes charges for both Vector Dimensions and Storage. These are the primary usage dimensions that count toward your minimum spend. Backups are charged on top of the minimum amount based on data volume and retention.
Weaviate
Open-source vector database that stores and indexes data objects and vector embeddings for semantic search, RAG, and agent-driven workflows in AI applications.
Open-source vector database that stores and indexes data objects and vector embeddings for semantic search, RAG, and agent-driven workflows in AI applications. Built for teams that need faster decision-making.
- Pricing
- Paid
- Best for
- AI engineers
Open-source vector database that stores and indexes data objects and vector embeddings for semantic search, RAG, and agent-driven workflows in AI applications. Built for teams that need faster decision-making.
About Weaviate
Weaviate is an open-source vector database designed to store and index both data objects and their vector embeddings, enabling advanced semantic and hybrid search capabilities for AI applications. It supports retrieval augmented generation (RAG) workflows for context-aware responses and agent-driven workflows for intelligent applications. Key features include billion-scale architecture and enterprise-ready deployment options like hybrid search, vector compression, multi-tenancy, and high availability.
FAQ
Currently, the ability to move tenants between hot, warm, and cold storage is only available if you are using Weaviate Dedicated Cloud or self-hosting Weaviate.
A vector dimension refers to one element in a vector representing an embedding for an object. The total number of these elements is the vector's dimensionality. A vector index holds all the vector dimensions for every object in a collection.
The total count of vector dimensions is a good indicator of the resources required. This is a simple number that a user can calculate rather than trying to understand abstract concepts like Read/Write units, CPU cycles or other measurements of infrastructure usage.
The number of vector dimensions is calculated by multiplying the number of objects by the vector dimensionality of each index. This value is further multiplied by the replication factor to account for data replicas.
Uncompressed vectors provide the highest accuracy for search results. However, this comes at the cost of requiring significant amounts of memory at scale. A compression technique called quantization allows the size of the vectors to be reduced.
Compression does not alter the number of vector dimensions stored, but it reduces the computational and memory resources required for search. The more aggressive the compression method, the greater the efficiency gains, which are reflected in discounted list prices.
List prices for all pricing vector dimensions vary by Cloud provider and region to reflect regional variations.
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