Weaviate vs Rockset
Compare and contrast Weaviate and Rockset by architecture, ingestion, queries, performance, and scalability.
Weaviate vs Rockset Ingestion
Weaviate supports batch insertion of vectors and updates and in-place updates for vectors and metadata. Weaviate supports searches across high dimensional vector embeddings.
Rockset is built for streaming data and is a mutable database, supporting in-place updates for vectors and metadata. As a real-time search and analytics database, Rockset supports searches across large-scale data, including vector embeddings and metadata.
Weaviate vs Rockset Indexing
Weaviate supports KNN and ANN search using HNSW indexing algorithms. Weaviate provides inverted indexes and vector search indexes and uses vectorization to speed up query execution. Users are responsible for index maintenance.
Rockset supports KNN and ANN search using FAISS indexing algorithms. Rockset consolidates search, vector search, columnar and row indexes into a Converged Index to support a wide range of query patterns out of the box. Vectorization is used to speed up query execution
Weaviate vs Rockset Querying
Weaviate uses a strict schema system with all of the fields and their type specified before the data is indexed.
Weaviate pre-filters data before an approximate kNN search. Weaviate supports a GraphQL API for search.
Rockset supports pre-filtering and applying a filter during an approximate kNN search. Rockset supports SQL and REST APIs. Rockset applies a filter during an approximate kNN search.
Weaviate vs Rockset Ecosystem
Weaviate vs Rockset Architecture
Weaviate is built for on-prem with a tightly coupled architecture. Scaling Weaviate requires data and infrastructure expertise and management.
Rockset is built for the cloud and separates compute-storage and compute-compute. The compute used for ingestion and indexing of vector embeddings is isolates from the compute used for query serving. Rockset is used by enterprises including Allianz, JetBlue and Whatnot.