Rockset vs Pinecone
Compare and contrast Rockset and Pinecone by architecture, ingestion, queries, performance, and scalability.
Rockset vs Pinecone Ingestion
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.
Pinecone supports batch insertion of vectors and updates and in-place updates for vectors and metadata. Pinecone supports searches across high dimensional vector embeddings.
Rockset vs Pinecone Indexing
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
Pinecone supports KNN and ANN search. Pinecone supports sparse-dense vectors for hybrid search. Pinecone handles all index management.
Rockset vs Pinecone Querying
Pinecone supports a limited number of metadata field types. It recommends avoiding indexing high-cardinality metadata as that will consume significantly more memory. The maximum results a query will return with metadata filtering is 1,000.
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.
Pinecone applies a filter during an approximate kNN search. Pinecone supports REST APIs.
Rockset vs Pinecone Ecosystem
Rockset vs Pinecone Architecture
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.
Pinecone is a cloud-service with a tightly-coupled architecture.