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 nodes for large workloads (millions of records or more, and moderate or high query rates).
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.
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.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.p2 pods
The p2 pod type provides greater query throughput with lower latency. For vectors with fewer than 128 dimension and queries wheretopK
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.
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.
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 theenvironment
parameter for the create_index
endpoint:
Cloud | Region | Environment |
---|---|---|
GCP | us-west-1 (N. California) | us-west1-gcp |
GCP | us-central-1 (Iowa) | us-central1-gcp |
GCP | us-west-4 (Las Vegas) | us-west4-gcp |
GCP | us-east-4 (Virginia) | us-east4-gcp |
GCP | northamerica-northeast-1 | northamerica-northeast1-gcp |
GCP | asia-northeast-1 (Japan) | asia-northeast1-gcp |
GCP | asia-southeast-1 (Singapore) | asia-southeast1-gcp |
GCP | us-east-1 (South Carolina) | us-east1-gcp |
GCP | eu-west-1 (Belgium) | eu-west1-gcp |
GCP | eu-west-4 (Netherlands) | eu-west4-gcp |
AWS | us-east-1 (Virginia) | us-east-1-aws |
Azure | eastus (Virginia) | eastus-azure |
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.
Example
Example
While our pricing page lists rates on an hourly basis for ease of comparison, this example lists prices per minute, as this is how Pinecone calculates billing.An example application has the following requirements:
The invoice for this example is given in Table 2 below:Table 2: Example invoice
Amount due $514.54
- 1,000,000 vectors with 1536 dimensions
- 150 queries per second with
top_k
= 10 - Deployment in an EU region
- Ability to store 1GB of inactive vectors
p1.x2
pod with three replicas and a collection containing 1 GB of data. This project runs continuously for the month of January on the Standard plan. The components of the total cost for this example are given in Table 1 below:Table 1: Example billing componentsBilling component | Value |
---|---|
Number of pods | 1 |
Number of replicas | 3 |
Pod size | x2 |
Total pod count | 6 |
Minutes in January | 44,640 |
Pod-minutes (pods * minutes) | 267,840 |
Pod price per minute | $0.0012 |
Collection storage | 1 GB |
Collection storage minutes | 44,640 |
Price per storage minute | $0.00000056 |
Product | Quantity | Price per unit | Charge |
---|---|---|---|
Collections | 44,640 | $0.00000056 | $0.025 |
P2 Pods (AWS) | 0 | $0.00 | |
P2 Pods (GCP) | 0 | $0.00 | |
S1 Pods | 0 | $0.00 | |
P1 Pods | 267,840 | $0.0012 | $514.29 |
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
andp1
pod-based indexes using the dotproduct distance metric support sparse-dense vectors.
- Only