Pinecone vs Weaviate
Compare and contrast Pinecone and Weaviate by architecture, ingestion, queries, performance, and scalability.
Pinecone vs Weaviate Ingestion
Pinecone supports batch insertion of vectors and updates and in-place updates for vectors and metadata. Pinecone supports searches across high dimensional vector embeddings.
Weaviate supports batch insertion of vectors and updates and in-place updates for vectors and metadata. Weaviate supports searches across high dimensional vector embeddings.
Pinecone vs Weaviate Indexing
Pinecone supports KNN and ANN search. Pinecone supports sparse-dense vectors for hybrid search. Pinecone handles all index management.
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.
Pinecone vs Weaviate 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.
Weaviate uses a strict schema system with all of the fields and their type specified before the data is indexed.
Pinecone applies a filter during an approximate kNN search. Pinecone supports REST APIs.
Weaviate pre-filters data before an approximate kNN search. Weaviate supports a GraphQL API for search.
Pinecone vs Weaviate Ecosystem
Pinecone vs Weaviate Architecture
Pinecone is a cloud-service with a tightly-coupled architecture.
Weaviate is built for on-prem with a tightly coupled architecture. Scaling Weaviate requires data and infrastructure expertise and management.