This page describes known limitations and feature restrictions in Pinecone.


  • Upserts
    • Pinecone is eventually consistent, so there can be a slight delay before upserted records are available to query.

      After upserting records, use the describe_index_stats operation to check if the current vector count matches the number of records you expect, although this method may not work for pod-based indexes with multiple replicas.

    • Only indexes using the dotproduct distance metric support sparse-dense vectors.

      Upserting sparse-dense vectors into indexes with a different distance metric will succeed, but querying will return an error.

    • Indexes created before February 22, 2023 do not support sparse vectors.

  • Metadata
    • Null metadata values are not supported. Instead of setting a key to hold a null value, remove the key from the metadata payload.

Serverless indexes

Serverless indexes do not support the following features:

Pod-based indexes

  • Pod storage capacity
    • Each p1 pod has enough capacity for 1M vectors with 768 dimensions.
    • Each s1 pod has enough capacity for 5M vectors with 768 dimensions.
  • Metadata
    • Metadata with high cardinality, such as a unique value for every vector in a large index, uses more memory than expected and can cause the pods to become full.
  • Collections
    • You cannot query or write to a collection after its creation. For this reason, a collection only incurs storage costs.
    • You can only perform operations on collections in the current Pinecone project.
  • Sparse-dense vectors

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