Redis vs. Pinecone
Compare Redis vs. Pinecone by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Redis vs. Pinecone on Scalability
No. Redis primarily operates by keeping data in memory. The classic architecture of Redis does not inherently follow the storage-compute separation architecture. Instead, it tightly couples data storage and computation in the same node or instance to ensure data access performance.
Yes, for the Serverless tier.
No. It only scales at the server level. In addition, you need to reshard all the data when scaling out a Redis cluster.
Yes, for the Serverless tier.
Yes - built-in replication; HA provided in an additional layer by Reis Cluster or Redis Sentinel)
With Redis Enteprise
Static sharding
Redis Scalability
High Availabiltiy can be achieved with Redis Enterprise.
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.
Redis vs. Pinecone on Functionality
Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.
Furthermore, differences in insert rate, query rate, and underlying hardware may result in different application needs, making overall system tunability a mandatory feature for vector databases.
Yes, with limited roles (only Org Owner & members are supported)
Available with the Pinecone S1 solution only
Yes - Pre-filtering documents against an index containing searchable fields
Yes. Sparse & Dense Vectors and Scalar filtering.
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.
HNSW & IVFFlat
Closed source Index (proprietary)
Redis
Redis has supprt for similarity queries search with the use of vector fields; It is important to note that the k default LIMIT is 10.
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.
Redis vs. Pinecone on Purpose-built
Add on to Redis
No
Python for Vector Search
REST API, Python, Node.js
yes, with the collection backup & restore
Redis vs. Pinecone: what’s right for me?
Redis is an in-memory data structure store used as a database, cache, message broker, and streaming engine that has a vector field type for the storage, querying and indexing of vectors.
License: BSD License
Pinecone
Pinecone is a managed, cloud-native vector database.
SaaS
