Pinecone vs LanceDB

Pinecone vs. LanceDB

Compare Pinecone vs. LanceDB 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. LanceDB on Scalability

PineconeLanceDB

Separation of storage and compute

Yes, for the Serverless tier.

Yes.

Separation of query and insertions

Yes, for the Serverless tier.

❌

Multi-replication

❌

Dynamic segment placement vs. static data sharding

Static sharding

No (static data sharding coming soon)

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.

LanceDB

LanceDB is an open-source vector database that's designed to store, manage, query and retrieve embeddings on multi-modal data. LanceDB and its underlying data format, Lance, are built to scale to really large amounts of data (hundreds of terabytes, 200M+ vectors).

Pinecone vs. LanceDB on Functionality

PineconeLanceDB

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

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)

IVF-PQ, HNSW

(LanceDB adopts a disk-based indexing philosophy.)

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.

Pinecone vs. LanceDB on Purpose-built

PineconeLanceDB

Purpose-built for Vectors

❌

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

REST API, Python, Node.js

Python, Javascript/Typescript, and Rust

Database rollback

yes, with the collection backup & restore

✔️

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

Pinecone

Pinecone is a managed, cloud-native vector database.

SaaS

LanceDB

LanceDB is an open-source vector database that's designed to store, manage, query and retrieve embeddings on multi-modal data. It also provides a SaaS solution called LanceDB Cloud that runs serverless in the cloud.

Apache 2.0