Customers who sign up for a Standard or Enterprise plan on or after August 18, 2025 cannot create pod-based indexes. Instead, create serverless indexes, and consider using dedicated read capacity for large workloads (millions of records or more, and moderate or high query rates).
This page shows you how to create a pod-based index. For guidance on serverless indexes, see Create a serverless index.

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Create a pod index

To create a pod index, use the create_index operation as follows:
  • Provide a name for the index.
  • Specify the dimension and metric of the vectors you’ll store in the index. This should match the dimension and metric supported by your embedding model.
  • Set spec.environment to the environment where the index should be deployed. For Python, you also need to import the ServerlessSpec class.
  • Set spec.pod_type to the pod type and size that you want.
Other parameters are optional. See the API reference for details. 
from pinecone.grpc import PineconeGRPC as Pinecone, PodSpec

pc = Pinecone(api_key="YOUR_API_KEY")

pc.create_index(
  name="docs-example",
  dimension=1536,
  metric="cosine",
  spec=PodSpec(
    environment="us-west1-gcp",
    pod_type="p1.x1",
    pods=1
  ),
  deletion_protection="disabled"

)

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Create a pod index from a collection

You can create a pod-based index from a collection. For more details, see Restore an index.