Pinecone vs Elasticsearch
Compare and contrast Pinecone and Elasticsearch by architecture, ingestion, queries, performance, and scalability.
Pinecone vs Elasticsearch Ingestion
Ingestion
Pinecone
Elasticsearch
Streaming and bulk ingestion
Pinecone supports inserting vectors in batches of 100 vectors or fewer with a maximum size per upsert request of 2MB. Pinecone cannot perform reads and writes in parallel and so writing in large batches can impact query latency and vice versa.
Elasticsearch supports updates and bulk ingestion. It recommends bulk ingesting into larger, fewer segments as each segment has an HNSW graph that needs to be searched. This results in higher latency.
Index updates
Pinecone offers two methods for updating vectors: full and partial updates. Full updates modify the entire dataset and a partial update will update specific fields using a unique identifier.
Index updates happen through expensive merge operations and reindexing. It's recommended to avoid heavy indexing during approximate kNN search and to reindex the new documents into a separate index rather than update them in-place.
Embedding generation
Pinecone supports API calls to OpenAI, Cohere and HuggingFace to insert and index embeddings.
Elasticsearch introduced its own Elastic Sparse Encoder model that can be used for embedding generation. To use a third party model with Elasticsearch, you must import and deploy the model, and then create an ingest pipeline with an inference processor to perform data transformation.
Size of vectors and metadata
Pinecone supports 40kb of metadata per vector and a maximum vector dimensionality is 20,000. Pod sizes, resource confidurations, are storage bound.
Elasticsearch supports vectors up to 2048 dimensions. With approximate KNN search, all vector data must fit in the node's page cache for it to be efficient.
Versioning
There does not appear to be a way to version in Pinecone.
Elasticsearch uses aliases to reindex data with no downtime.
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 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 vs Elasticsearch Indexing
Indexing
Pinecone
Elasticsearch
KNN and ANN
Pinecone supports KNN and ANN search. The algorithms leveraged by Pinecone are not documented.
Elasticsearch supports KNN and ANN search. ANN search uses the HNSW algorithm. HNSW is a graph-based alorithm which only works efficiently when most vector data is held in memory.
Additional indexes
Pinecone supports the creating a single sparse-dense vector for hybrid search. The sparse vector is used for text search and includes support for BM25 algorithms. Because this is a single vector there's no ability to independently weight the sparsity or density of the coordinates of the vector.
Elasticsearch includes inverted index for text search, BKD trees for geolocation search and ANN indexes.
Vectorization
There is no documentation on vectorization in Pinecone.
Elasticsearch added vectorization to its 8.9.0 version to speed up query execution.
Index management
Pinecone handles all index management.
Elasticsearch users are responsible for index maintenance including the number of index segments and the reindexing of data.
Pinecone supports KNN and ANN search. Pinecone supports sparse-dense vectors for hybrid search. Pinecone handles all index management.
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 vs Elasticsearch Querying
Querying
Pinecone
Elasticsearch
Metadata filtering
Pinecone supports metadata filtering and hybrid search. Pinecone filters are applied during the approximate kNN search.
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.
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 metadata filtering and hybrid search. Elasticsearch filters are applied during the approximate kNN search.
Multi-modal models
There is no documentation on multi-modal models in Pinecone.
Elasticsearch enables searches across multiple kNN fields to support multi-modal models.
Pinecone applies a filter during an approximate kNN search. Pinecone supports REST APIs.
Elasticsearch supports REST APIs.
Pinecone vs Elasticsearch Ecosystem
Ecosystem
Pinecone
Elasticsearch
Integrations (Huggingface, Langchain, etc.)
Pinecone supports API calls to OpenAI, Cohere and HuggingFace to insert and index embeddings. Pinecone has an integration to Langchain and LlamaIndex.
To use a third party model with Elasticsearch like Huggingface, you must import and deploy the model, and then create an ingest pipeline with an inference processor to perform data transformation. Elasticsearch has an integration with Langchain.
Pinecone vs Elasticsearch Architecture
Architecture
Pinecone
Elasticsearch
Cloud architecture
Pinecone is a cloud-based service deployed partly on Kubernetes with a tightly coupled architecture. Each pod, configuration of resources, has one or more replicas and provides the RAM, CPU and SSD required.
Built for on-prem. Indexing and search are run on the same instances which has the potential to cause compute contention. Users responsible for clusters, shards and indexes.
Scalability
Pinecone offers a number of pods, or resource configurations, that can be picked depending on the performance requirements of the vector search. Pods are storage bound so once you cross a threshold you will scale your pod size up (1x, 2x, 4x and 8x) without downtime. It is not possible to scale a pod size down. You can also horizontally scale by adding more pods but this will pause new inserts and index creation. You can also add replicas to increase QPS.
Elasticsearch requires deep expertise around servers, clusters, nodes, indexes and shards to operate at scale. For vector search on Elasticsearch, users may face scaling challenges given that indexing and search are run on the same instance, all vector data must fit into the page cache and each index segment has an HNSW graph that needs to be searched which constributes to latency.
Enterprise readiness
Pinecone does not have case studies of enterprises using their product in production. It reported a partial database outage on March 1st, 2023.
Elasticsearch is used by enterprises at scale including Booking.com and Cisco.
Pinecone is a cloud-service with a tightly-coupled 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.