Pinecone vs TiDB

Pinecone vs. TiDB

Compare Pinecone vs. TiDB 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. TiDB on Scalability

PineconeTiDB

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

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.

TiDB

TiDB is designed with scalability as one of its core features. It offers both horizontal and vertical scaling capabilities to handle growing workloads and data volumes.

Pinecone vs. TiDB on Functionality

PineconeTiDB

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 & SQL 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)

HNSW

Multiple in-memory indexes support

❌

No. HNSW only

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.

TiDB

TiDB offers vector search through its serverless cluster and supports vectors with a maximum dimension of 16,000. The Vector data type in TiDB is designed to store single-precision floating-point numbers (Float32). It only supports cosine distance and L2 distance for similarity measurement.

Pinecone vs. TiDB on Purpose-built

PineconeTiDB

Purpose-built for Vectors

❌

No, vector search is an add-on to TiDB Cloud serverless.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

REST API, Python, Node.js

No. TiDB does not provide specific SDKs. Instead, it is designed to be compatible with MySQL, which means TiDB can be used with any language with MySQL client or driver support.

Database rollback

yes, with the collection backup & restore

❌

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

Pinecone

Pinecone is a managed, cloud-native vector database.

SaaS

TiDB

TiDB is an open-source distributed SQL database for OLAP and OLTP workloads. It now offers a vector search capability (in public beta) as an add-on to its SaaS solution, TiDB Cloud Serverless.

Apache 2.0