Comparisons
Pinecone vs KDB.AI

Pinecone vs. KDB.AI

Compare Pinecone vs. KDB.AI 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. KDB.AI on Scalability

PineconeKDB.AI

Separation of storage and compute

Yes, for the Serverless tier.

❌

Separation of query and insertions

Yes, for the Serverless tier.

Yes.

Multi-replication

❌

Dynamic segment placement vs. static data sharding

Static sharding

Neither.

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.

KDB.AI

KDB.AI is a scalable vector database.

Pinecone vs. KDB.AI on Functionality

PineconeKDB.AI

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

Yes (qFlat and qHNSW)

Hybrid Search (ie Scalar filtering)

Yes. Sparse & Dense Vectors and Scalar filtering.

Yes. Hybrid Sparse & Dense 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)

Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.

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.

KDB.AI

Built by KX, a database provider known for time-series data management, KDB.AI enables developers to bring temporal and semantic context and relevancy to their applications. It supports various search types, including vector similarity search, hybrid sparse and vector search, and Non-Transformed TSS, a similarity search algorithm specific for time series data. It uses Cosine Similarity, Inner Product, and L2 Distance (Euclidean) for similarity metrics.

Pinecone vs. KDB.AI on Purpose-built

PineconeKDB.AI

Purpose-built for Vectors

❌

Yes.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

REST API, Python, Node.js

Python

Database rollback

yes, with the collection backup & restore

❌

Pinecone vs. KDB.AI: what’s right for me?

Pinecone

Pinecone is a managed, cloud-native vector database.

SaaS

KDB.AI

KDB.AI is a powerful knowledge-based vector database and search engine that allows you to build scalable, reliable AI applications using real-time data.

Proprietary license