Understanding indexes

An index is the highest-level organizational unit of vector data in Pinecone. It accepts and stores vectors, serves queries over the vectors it contains, and does other vector operations over its contents.

Organizations on the Standard and Enterprise plans can create serverless indexes and pod-based indexes. Organizations on the free Starter plan can create only one starter pod-based index.

Serverless indexes

Serverless indexes are in public preview and are available only on AWS in the us-west-2 and us-east-1 regions. Check the current limitations and test thoroughly before using it in production.

With serverless indexes, you don’t configure or manage any compute or storage resources. Instead, based on a breakthough architecture, serverless indexes scale automatically based on usage, and you pay only for the amount of data stored and operations performed, with no minimums. This means that there’s no extra cost for having additional indexes.

For more details about how costs are calculated for a serverless index, see Understanding cost.

Cloud regions

When creating a serverless index, you must choose the cloud and region where you want the index to be hosted. The following table lists the available public clouds and regions and the corresponding spec parameter for the create_index operation:

Cloud	Region	spec
AWS	us-west-2 (Oregon)	spec=ServerlessSpec(cloud="aws", region="us-west-2")
AWS	us-east-1 (Virginia)	spec=ServerlessSpec(cloud="aws", region="us-east-1")

The cloud and region cannot be changed after a serverless index is created.

Pod-based indexes

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.

Pod types

Once a pod-based index is created, you cannot change its pod type. However, you can create a new index from that collection with a different pod type.

Different pod types are priced differently. See Understanding cost for more details.

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 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.

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 pod-based indexes.

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 operation:

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

Contact us if you need a dedicated deployment in other regions.

The environment cannot be changed after the index is created.

Starter indexes

On the free Starter plan, you get one project and one pod-based starter index with enough resources to support 100,000 vectors. Although the Starter plan does not support all Pinecone features, it’s easy to upgrade when you’re ready.

Starter indexes are hosted in the gcp-starter environment, which is us-central-1 (Iowa) region of the GCP cloud.

Distance metrics

You can choose from different metrics when creating a vector index.

Depending on your application, some metrics have better recall and precision performance than others. For more information, see What is Vector Similarity Search?

euclidean

This is used to calculate the distance between two data points in a plane. It is one of the most commonly used distance metric. For an example, see our IT threat detection example.

When you use metric='euclidean', the most similar results are those with the lowest score.

cosine

This is often used to find similarities between different documents. The advantage is that the scores are normalized to [-1,1] range. For an example, see our generative question answering example.

dotproduct

This is used to multiply two vectors. You can use it to tell us how similar the two vectors are. The more positive the answer is, the closer the two vectors are in terms of their directions. For an example, see our semantic search example.