Manage a dedicated read nodes index

Add a hosted embedding model, monitor fullness, change node types, pause, or convert a Pinecone dedicated read nodes index back to on-demand.

Add a hosted embedding model

To upsert and search with text instead of vectors, you can configure your index to use a hosted embedding model. To do this, call Configure an index and provide an embed object in the request body. In this object:

For the text field, specify the name of the field in your data that contains the text to be embedded.
Specify a model whose dimension requirements match the dimensions of your index.

Example

Request

PINECONE_API_KEY="YOUR_API_KEY"
INDEX_NAME="YOUR_INDEX_NAME"

curl -X PATCH "https://api.pinecone.io/indexes/$INDEX_NAME" \
     -H "Content-Type: application/json" \
     -H "Api-Key: $PINECONE_API_KEY" \
     -H "X-Pinecone-Api-Version: 2025-10" \
     -d '{
           "embed": {
             "field_map": {
               "text": "chunk_text"
             },
             "model": "llama-text-embed-v2",
             "read_parameters": {
               "input_type": "query",
               "truncate": "NONE"
             },
             "write_parameters": {
               "input_type": "passage"
             }
           }
         }'

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Response

{
  "name": "example-dedicated-index",
  "vector_type": "dense",
  "metric": "cosine",
  "dimension": 1024,
  "status": {
    "ready": true,
    "state": "Ready"
  },
  "host": "example-dedicated-index-1c6ab6aa.svc.aped-4627-b74a.pinecone.io",
  "spec": {
    "serverless": {
      "region": "us-east-1",
      "cloud": "aws",
      "read_capacity": {
        "mode": "Dedicated",
        "dedicated": {
          "node_type": "b1",
          "scaling": "Manual",
          "manual": {
            "shards": 2,
            "replicas": 1
          }
        },
        "status": {
          "state": "Ready",
          "current_shards": 2,
          "current_replicas": 1
        }
      }
    }
  },
  "deletion_protection": "enabled",
  "tags": {
    "environment": "testing"
  },
  "embed": {
    "model": "llama-text-embed-v2",
    "field_map": {
      "text": "chunk_text"
    },
    "dimension": 1024,
    "metric": "cosine",
    "write_parameters": {
      "dimension": 1024,
      "input_type": "passage",
      "truncate": "END"
    },
    "read_parameters": {
      "dimension": 1024,
      "input_type": "query",
      "truncate": "NONE"
    },
    "vector_type": "dense"
  }
}

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Monitor index fullness

To check index fullness, call Get index stats.

Example

Request

# To get the unique host for an index,
# see https://docs.pinecone.io/guides/manage-data/target-an-index
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="YOUR_INDEX_HOST"

curl -X GET "https://$INDEX_HOST/describe_index_stats" \
     -H "Api-Key: $PINECONE_API_KEY" \
     -H "X-Pinecone-Api-Version: 2025-10"

Response

{
  "namespaces": {
    "__default__": {
      "vectorCount": 705000
    }
  },
  "indexFullness": 0.01,
  "totalVectorCount": 705000,
  "dimension": 1536,
  "metric": "cosine",
  "vectorType": "dense",
  "memoryFullness": 0.01,
  "storageFullness": 0.01
}

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In the response, indexFullness describes how full the index is, on a scale of 0 to 1. It's set to the greater of memoryFullness and storageFullness.

Pinecone also emits these values. Use them to track fullness over time in Prometheus or Datadog, and to alert before your index reaches capacity.

Change node types

You can change node types in either direction (b1 → t1 or t1 → b1). This operation doesn't require downtime, but can take up to 30 minutes to complete.

To change node types, call Configure an index. In the request body, set the following fields:

Field	Value	Notes
`spec.serverless.read_capacity.mode`	`Dedicated`
`spec.serverless.read_capacity.dedicated.node_type`	`b1` or `t1`	See node types

Example

This example changes the node type from b1 to t1:

Request

PINECONE_API_KEY="YOUR_API_KEY"
INDEX_NAME="YOUR_INDEX_NAME"

curl -X PATCH "https://api.pinecone.io/indexes/$INDEX_NAME" \
     -H "Accept: application/json" \
     -H "Content-Type: application/json" \
     -H "Api-Key: $PINECONE_API_KEY" \
     -H "X-Pinecone-Api-Version: 2025-10" \
     -d '{
           "spec": {
             "serverless": {
               "read_capacity": {
                 "mode": "Dedicated",
                 "dedicated": {
                   "node_type": "t1"
                 }
               }
             }
           }
         }'

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Response

{
  "name": "example-dedicated-index",
  "vector_type": "dense",
  "metric": "cosine",
  "dimension": 1024,
  "status": {
    "ready": true,
    "state": "Ready"
  },
  "host": "example-dedicated-index-1c6ab6aa.svc.aped-4627-b74a.pinecone.io",
  "spec": {
    "serverless": {
      "region": "us-east-1",
      "cloud": "aws",
      "read_capacity": {
        "mode": "Dedicated",
        "dedicated": {
          "node_type": "t1",
          "scaling": "Manual",
          "manual": {
            "shards": 1,
            "replicas": 1
          }
        },
        "status": {
          "state": "Scaling",
          "current_shards": 1,
          "current_replicas": 1
        }
      }
    }
  },
  "deletion_protection": "disabled",
  "tags": null,
  "embed": {
    "model": "llama-text-embed-v2",
    "field_map": {
      "text": "text"
    },
    "dimension": 1024,
    "metric": "cosine",
    "write_parameters": {
      "dimension": 1024,
      "input_type": "passage",
      "truncate": "END"
    },
    "read_parameters": {
      "dimension": 1024,
      "input_type": "query",
      "truncate": "END"
    },
    "vector_type": "dense"
  }
}

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Pause an index

To pause an index, set the number of replicas to 0. This operation can take up to 30 minutes to complete.

Check the status of a configuration change

After making a configuration change to a dedicated read nodes index (changing shards, replicas, or node type), check the status of the change by calling Describe an index.

Example

Request

PINECONE_API_KEY="YOUR_API_KEY"
INDEX_NAME="YOUR_INDEX_NAME"

curl -X GET "https://api.pinecone.io/indexes/$INDEX_NAME" \
     -H "Api-Key: $PINECONE_API_KEY" \
     -H "X-Pinecone-Api-Version: 2025-10"

Response

{
  "name": "example-dedicated-index",
  "vector_type": "dense",
  "metric": "cosine",
  "dimension": 1536,
  "status": {
    "ready": true,
    "state": "Ready"
  },
  "host": "example-dedicated-index-1c6ab6aa.svc.aped-4627-b74a.pinecone.io",
  "spec": {
    "serverless": {
      "region": "us-east-1",
      "cloud": "aws",
      "read_capacity": {
        "mode": "Dedicated",
        "dedicated": {
          "node_type": "b1",
          "scaling": "Manual",
          "manual": {
            "shards": 1,
            "replicas": 2 // <---- desired state
          }
        },
        "status": {
          "state": "Scaling",
          "current_shards": 1,
          "current_replicas": 1 // <---- current state
        }
      }
    }
  },
  "deletion_protection": "enabled",
  "tags": {
    "tag0": "value0"
  }
}

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The response includes two status fields:

Field	Description
`status.state`	Overall index status (for example, `Initializing`, `Ready`, `Terminating`)
`spec.serverless.read_capacity.status.state`	Read capacity status (`Migrating`, `Scaling`, `Ready`, `Error`)

When changing node types, shards, or replicas, monitor the read capacity status (spec.serverless.read_capacity.status.state). Possible values:

State	Description
`Ready`	The change is complete and the index is ready to serve queries at full capacity.
`Scaling`	A change to the number of shards or replicas is in progress.
`Migrating`	A change to the node type or read capacity mode is in progress.
`Error`	The operation failed. For migrations to dedicated, this typically means you didn't allocate enough shards for your index size. Check `error_message` for details, and retry with more shards.

Convert to on-demand

To convert a dedicated read nodes index back to on-demand, call Configure an index and set spec.serverless.read_capacity.mode to OnDemand. This converts the index in place, keeping the same index name and host.

Request

PINECONE_API_KEY="YOUR_API_KEY"
INDEX_NAME="YOUR_INDEX_NAME"

curl -X PATCH "https://api.pinecone.io/indexes/$INDEX_NAME" \
     -H "Accept: application/json" \
     -H "Content-Type: application/json" \
     -H "Api-Key: $PINECONE_API_KEY" \
     -H "X-Pinecone-Api-Version: 2025-10" \
     -d '{
           "spec": {
             "serverless": {
               "read_capacity": {
                 "mode": "OnDemand"
               }
             }
           }
         }'

Response

{
  "name": "example-index",
  "vector_type": "dense",
  "metric": "cosine",
  "dimension": 1024,
  "status": {
    "ready": true,
    "state": "Ready"
  },
  "host": "example-index-1c6ab6aa.svc.aped-4627-b74a.pinecone.io",
  "spec": {
    "serverless": {
      "region": "us-east-1",
      "cloud": "aws",
      "read_capacity": {
        "mode": "OnDemand",
        "status": {
          "state": "Ready",
          "current_shards": null,
          "current_replicas": null
        }
      }
    }
  },
  "deletion_protection": "disabled",
  "tags": null,
  "embed": {
    "model": "llama-text-embed-v2",
    "field_map": {
      "text": "text"
    },
    "dimension": 1024,
    "metric": "cosine",
    "write_parameters": {
      "dimension": 1024,
      "input_type": "passage",
      "truncate": "END"
    },
    "read_parameters": {
      "dimension": 1024,
      "input_type": "query",
      "truncate": "END"
    },
    "vector_type": "dense"
  }
}

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If you'd rather return to on-demand on a fresh index, use the backup and restore path instead:

Create a backup of your dedicated read nodes index.
Create a new index from the backup, without specifying dedicated read node configuration.
Verify the new on-demand index and update your application to use it.
Delete the old dedicated read nodes index.

If you have concerns or need assistance, contact support.