Elasticsearch vs Pinecone
Compare and contrast Elasticsearch and Pinecone by architecture, ingestion, queries, performance, and scalability.
Elasticsearch vs Pinecone Ingestion
Elasticsearch supports both streaming and bulk ingestion. It recommends using fewer Lucene segments and avoiding updates and reindexing to save on compute costs. Elasticsearch 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.
Elasticsearch vs Pinecone Indexing
Elasticsearch supports KNN and ANN search using HNSW indexing algorithms. Elasticsearch provides inverted indexes and vector search indexes and uses vectorization to speed up query execution. Users are responsible for index maintenance.
Pinecone supports KNN and ANN search. Pinecone supports sparse-dense vectors for hybrid search. Pinecone handles all index management.
Elasticsearch 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.
Elasticsearch supports REST APIs.
Pinecone applies a filter during an approximate kNN search. Pinecone supports REST APIs.
Elasticsearch vs Pinecone Ecosystem
Elasticsearch vs Pinecone Architecture
Elasticsearch is built for on-prem with a tightly coupled architecture. Scaling Elasticsearch requires data and infrastructure expertise and management. Elasticsearch is used by enterprises including Booking.com and Cisco.
Pinecone is a cloud-service with a tightly-coupled architecture.