Known limitations

This page describes known limitations and feature restrictions in Pinecone.

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General

• 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 querying sparse-dense vectors.

    Upserting, updating, and fetching sparse-dense vectors in 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.

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Serverless indexes

Serverless indexes do not support the following features:

• Creating collections from serverless indexes

• Migrating pod-based indexes to serverless indexes

  • Serverless migration is limited to pod-based indexes with less than 25 million records and 20,000 namespaces.
  • The pod-based indexes must be hosted on AWS or GCP. It is not currently possible to migrate pod-based indexes on Azure.
  • The migration process creates serverless indexes on AWS only, regardless of the cloud where the pod-based indexes are hosted. This means that pod-based indexes on GCP will be migrated to serverless indexes on AWS.

• Deleting records by metadata

  • Instead, you can delete records by ID prefix.

• Selective metadata indexing

  • Because high-cardinality metadata in serverless indexes does not cause high memory utilization, this operation is not relevant.

• Configuring indexes

  • Because serverless indexes scale automatically based on usage, this operation is not relevant

• Filtering index statistics by metadata

• Metrics

• Private endpoints

  • This feature is available on AWS only.

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