This page summarizes current Pinecone limitations.

General limitations

  • Upserts

    • Max size for an upsert request is 2MB. Recommended upsert limit is 100 vectors per request.

    • 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.

    • Dense vectors must contain at least one non-zero value.

    • Max dimensionality for dense vectors is 20,000.

    • Sparse vectors can contain no more than 1000 non-zero values.

    • Max dimensionality for sparse vectors is 4.2 billion.

    • Records with sparse vector values must also contain dense vector values.

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

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

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

  • Metadata

    • Max metadata size per vector is 40 KB.

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

  • Queries

    • Max value for top_k, the number of results to return, is 10,000.
  • Fetches and deletes

    • Max vectors per fetch or delete request is 1,000.

Serverless index limitations

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Warning

Serverless indexes are in public preview and are available only on AWS in the us-west-2 and us-east-1 regions. Test thoroughly before using serverless indexes in production.

Pod-based index limitations

  • 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

    Only s1 and p1 pod-based indexes using the dotproduct distance metric support sparse-dense vectors.

Starter index limitations