Pinecone vs OpenSearch

Pinecone vs. OpenSearch

Compare Pinecone vs. OpenSearch by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.

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Pinecone vs. OpenSearch on Scalability

PineconeOpenSearch

Separation of storage and compute

Yes, for the Serverless tier.

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Separation of query and insertions

Yes, for the Serverless tier.

Yes.

Multi-replication

❌

Dynamic segment placement vs. static data sharding

Static sharding

Both

Cloud-native

❌

Billion-scale vector support

❌

Pinecone

Pinecone supports the separation of compute and storage with their Serveless Tier.

For its POD-based clusters, Pinecone employs static sharding, which requires users to manually reshard data when scaling out the cluster.

OpenSearch

OpenSearch supports horizontal scaling, cluster management optimizations, and efficient shard allocation, making it suitable for handling large datasets and high query loads effectively.

Pinecone vs. OpenSearch on Functionality

PineconeOpenSearch

Role-based Access Control (RBAC)

Yes, with limited roles (only Org Owner & members are supported)

❌

Disk Index support

Available with the Pinecone S1 solution only

❌

Hybrid Search (ie Scalar filtering)

Yes. Sparse & Dense Vectors and Scalar filtering.

yes, vector search & keyword search & scalar filtered search

Partitions/namespaces/logical groups

Yes. Users cans organizes data into namespaces and should aware that there are a limited number of namespaces available. Please consult with Pinecone on the limitations.

❌

Index type supported

Closed source Index (proprietary)

ANN

Multiple in-memory indexes support

❌

Pinecone

RBAC is not enough for large organizations. Storage optimized (S1 ) has some performance challenges and can only get 10-50 QPS. The number of namespaces is limited and users should be careful when using metadata filtering as a way around this limitation as it will have a big impact on performance. Furthermore, data isolation is not available with this approach.

OpenSearch

OpenSearch supports:

Vectors with up to 16,000 dimensions.
Vector generation through external libraries or directly within OpenSearch.
Both binary and dense vectors.
Cosine Similarity, Inner Product, and L2 Distance (Euclidean).
Integration with multiple engines, including NMSLIB, Faiss, and Lucene, to facilitate vector indexing and searching.

Pinecone vs. OpenSearch on Purpose-built

PineconeOpenSearch

Purpose-built for Vectors

❌

No. Vector search is an add-on to OpenSearch.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

REST API, Python, Node.js

Java, Python, JavaScript, Go, and .Net

Database rollback

yes, with the collection backup & restore

❌

Pinecone vs. OpenSearch: what’s right for me?

Pinecone

Pinecone is a managed, cloud-native vector database.

SaaS

OpenSearch

OpenSearch is an open-source software suite for search, analytics, security monitoring, and observability applications. It is not purpose-built for vector storage and search workloads but introduces a vector search plugin to provide this capability. Amazon OpenSearch Service is an AWS-managed service for OpenSearch.

Apache 2.0