# 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 POST "https://$INDEX_HOST/describe_index_stats" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2025-10"
// EXAMPLE RESPONSE 1: Serverless index (on-demand)
{
"namespaces": {
"example-namespace": {
"vectorCount": 10000
}
},
"indexFullness": 0,
"totalVectorCount": 10000,
"dimension": 1024,
"metric": "cosine",
"vectorType": "dense",
"memoryFullness": 0,
"storageFullness": 0
}
// EXAMPLE RESPONSE 2: Serverless index (dedicated read nodes)
{
"namespaces": {
"example-namespace": {
"vectorCount": 705000
}
},
"indexFullness": 0.01,
"totalVectorCount": 705000,
"dimension": 1536,
"metric": "cosine",
"vectorType": "dense",
"memoryFullness": 0.01,
"storageFullness": 0.01
}
Get index stats
Return statistics about the contents of an index, including the vector count per namespace, the number of dimensions, and the index fullness.
Serverless indexes scale automatically as needed, so index fullness is relevant only for pod-based indexes.
# To get the unique host for an index,
# see https://docs.pinecone.io/guides/manage-data/target-an-index
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="YOUR_INDEX_HOST"
curl -X POST "https://$INDEX_HOST/describe_index_stats" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2025-10"
// EXAMPLE RESPONSE 1: Serverless index (on-demand)
{
"namespaces": {
"example-namespace": {
"vectorCount": 10000
}
},
"indexFullness": 0,
"totalVectorCount": 10000,
"dimension": 1024,
"metric": "cosine",
"vectorType": "dense",
"memoryFullness": 0,
"storageFullness": 0
}
// EXAMPLE RESPONSE 2: Serverless index (dedicated read nodes)
{
"namespaces": {
"example-namespace": {
"vectorCount": 705000
}
},
"indexFullness": 0.01,
"totalVectorCount": 705000,
"dimension": 1536,
"metric": "cosine",
"vectorType": "dense",
"memoryFullness": 0.01,
"storageFullness": 0.01
}
memoryFullness and storageFullness alongside indexFullness, which summarizes overall capacity usage. For serverless on-demand indexes, all three values are typically 0 because storage and compute scale automatically. For dedicated indexes, monitor these values and add shards before the index reaches capacity.# To get the unique host for an index,
# see https://docs.pinecone.io/guides/manage-data/target-an-index
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="YOUR_INDEX_HOST"
curl -X POST "https://$INDEX_HOST/describe_index_stats" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2025-10"
// EXAMPLE RESPONSE 1: Serverless index (on-demand)
{
"namespaces": {
"example-namespace": {
"vectorCount": 10000
}
},
"indexFullness": 0,
"totalVectorCount": 10000,
"dimension": 1024,
"metric": "cosine",
"vectorType": "dense",
"memoryFullness": 0,
"storageFullness": 0
}
// EXAMPLE RESPONSE 2: Serverless index (dedicated read nodes)
{
"namespaces": {
"example-namespace": {
"vectorCount": 705000
}
},
"indexFullness": 0.01,
"totalVectorCount": 705000,
"dimension": 1536,
"metric": "cosine",
"vectorType": "dense",
"memoryFullness": 0.01,
"storageFullness": 0.01
}
Authorizations
Headers
Required date-based version header
Body
The request for the describe_index_stats operation.
If this parameter is present, the operation only returns statistics for vectors that satisfy the filter. See Understanding metadata.
Serverless indexes do not support filtering describe_index_stats by metadata.
Response
A successful response.
The response for the describe_index_stats operation.
A mapping for each namespace in the index from the namespace name to a summary of its contents. If a metadata filter expression is present, the summary will reflect only vectors matching that expression.
Show child attributes
Show child attributes
The dimension of the indexed vectors. Not specified if sparse index.
1024
The fullness of the index, regardless of whether a metadata filter expression was passed. The granularity of this metric is 10%.
Serverless indexes scale automatically as needed, so index fullness is relevant only for pod-based indexes.
The index fullness result may be inaccurate during pod resizing; to get the status of a pod resizing process, use describe_index.
0.4
The total number of vectors in the index, regardless of whether a metadata filter expression was passed
80000
The metric used to measure similarity.
"cosine"
The type of vectors stored in the index.
"dense"
The amount of memory used by a dedicated index
The amount of storage used by a dedicated index
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