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).
With pod-based indexes, you choose one or more pre-configured units of hardware (pods). Depending on the pod type, pod size, and number of pods used, you get different amounts of storage and higher or lower latency and throughput. Be sure to choose an appropriate pod type and size for your dataset and workload.

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Pod types

Different pod types are priced differently. See Understanding cost for more details.
Once a pod-based index is created, you cannot change its pod type. However, you can create a collection from an index and then create a new index with a different pod type from the collection.

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s1 pods

These storage-optimized pods provide large storage capacity and lower overall costs with slightly higher query latencies than p1 pods. They are ideal for very large indexes with moderate or relaxed latency requirements. Each s1 pod has enough capacity for around 5M vectors of 768 dimensions.

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p1 pods

These performance-optimized pods provide very low query latencies, but hold fewer vectors per pod than s1 pods. They are ideal for applications with low latency requirements (<100ms). Each p1 pod has enough capacity for around 1M vectors of 768 dimensions.

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p2 pods

The p2 pod type provides greater query throughput with lower latency. For vectors with fewer than 128 dimension and queries where topK is less than 50, p2 pods support up to 200 QPS per replica and return queries in less than 10ms. This means that query throughput and latency are better than s1 and p1. Each p2 pod has enough capacity for around 1M vectors of 768 dimensions. However, capacity may vary with dimensionality. The data ingestion rate for p2 pods is significantly slower than for p1 pods; this rate decreases as the number of dimensions increases. For example, a p2 pod containing vectors with 128 dimensions can upsert up to 300 updates per second; a p2 pod containing vectors with 768 dimensions or more supports upsert of 50 updates per second. Because query latency and throughput for p2 pods vary from p1 pods, test p2 pod performance with your dataset. The p2 pod type does not support sparse vector values.

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Pod size and performance

Each pod type supports four pod sizes: x1, x2, x4, and x8. Your index storage and compute capacity doubles for each size step. The default pod size is x1. You can increase the size of a pod after index creation. To learn about changing the pod size of an index, see Configure an index.

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Pod environments

When creating a pod-based index, you must choose the cloud environment where you want the index to be hosted. The project environment can affect your pricing. The following table lists the available cloud regions and the corresponding values of the environment parameter for the create_index endpoint:
CloudRegionEnvironment
GCPus-west-1 (N. California)us-west1-gcp
GCPus-central-1 (Iowa)us-central1-gcp
GCPus-west-4 (Las Vegas)us-west4-gcp
GCPus-east-4 (Virginia)us-east4-gcp
GCPnorthamerica-northeast-1northamerica-northeast1-gcp
GCPasia-northeast-1 (Japan)asia-northeast1-gcp
GCPasia-southeast-1 (Singapore)asia-southeast1-gcp
GCPus-east-1 (South Carolina)us-east1-gcp
GCPeu-west-1 (Belgium)eu-west1-gcp
GCPeu-west-4 (Netherlands)eu-west4-gcp
AWSus-east-1 (Virginia)us-east-1-aws
Azureeastus (Virginia)eastus-azure
Contact us if you need a dedicated deployment in other regions. The environment cannot be changed after the index is created.

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Pod costs

For each pod-based index, billing is determined by the per-minute price per pod and the number of pods the index uses, regardless of index activity. The per-minute price varies by pod type, pod size, account plan, and cloud region. For the latest pod-based index pricing rates, see Pricing. Total cost depends on a combination of factors:
  • Pod type. Each pod type has different per-minute pricing.
  • Number of pods. This includes replicas, which duplicate pods.
  • Pod size. Larger pod sizes have proportionally higher costs per minute.
  • Total pod-minutes. This includes the total time each pod is running, starting at pod creation and rounded up to 15-minute increments.
  • Cloud provider. The cost per pod-type and pod-minute varies depending on the cloud provider you choose for your project.
  • Collection storage. Collections incur costs per GB of data per minute in storage, rounded up to 15-minute increments.
  • Plan. The free plan incurs no costs; the Standard or Enterprise plans incur different costs per pod-type, pod-minute, cloud provider, and collection storage.
The following equation calculates the total costs accrued over time: 
(Number of pods) * (pod size) * (number of replicas) * (minutes pod exists) * (pod price per minute) 
+ (collection storage in GB) * (collection storage time in minutes) * (collection storage price per GB per minute)
To see a calculation of your current usage and costs, go to Settings > Usage in the Pinecone console.

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Known 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.