Understanding cost

For the latest pricing details, see Pricing.

Minimum usage

The Standard and Enterprise pricing plans include a monthly minimum usage committment:

Plan	Minimum usage
Starter	$0/month
Standard	$50/month
Enterprise	$500/month

Beyond the monthly minimum, customers are charged for what they use each month. Examples

Usage below monthly minimum

You are on the Standard plan.
Your usage for the month of August amounts to $20.
Your usage is below the $50 monthly minimum, so your total for the month is $50.

In this case, the August invoice would include line items for each service you used (totaling $20), plus a single line item covering the rest of the minimum usage commitment ($30).

Usage exceeds monthly minimum

You are on the Standard plan.
Your usage for the month of August amounts to $100.
Your usage exceeds the $50 monthly minimum, so your total for the month is $100.

In this case, the August invoice would only show line items for each service you used (totaling $100). Since your usage exceeds the minimum usage commitment, you are only charged for your actual usage and no additional minimum usage line item appears on your invoice.

Discounts

Pinecone offers up to an 18% discount for customers who enroll in an annual plan with an upfront payment. Eligibility requires a minimum annual commitment of $8,000. Throughout the contract term, the discount applies to List Price for all Pinecone services, excluding Storage and Support. Any usage that exceeds the prepaid credits will be billed at full List Price and will not qualify for the contractual discount. Customers on the Standard and Enterprise pay-as-you-go plans may upgrade directly by naviating in the Pinecone console to Settings > Billing > Plans.

The self-serve annual plan is not available through cloud marketplace billing. To purchase an annual plan through a cloud marketplace, contact ar@pinecone.io.

Serverless indexes

With serverless indexes, you pay for the amount of data stored and operations performed, based on three usage metrics: read units, write units, and storage. For the latest serverless pricing rates, see Pricing.

Read units

Read units (RUs) measure the compute, I/O, and network resources consumed by the following read requests:

Query
Fetch
List

Read requests return the number of RUs used. You can use this information to monitor read costs.

Indexes built on dedicated read nodes are not subject to read unit limits for query, fetch, and list operations. For sizing and capacity planning guidance, see the Dedicated Read Nodes guide.

Query

The cost of a query scales linearly with the size of the targeted namespace. Specifically, a query uses 1 RU for every 1 GB of namespace size, with a minimum of 0.25 RUs per query.

Namespace size	Read units per query
< 0.25 GB	0.25 RUs (minimum)
1 GB	1 RU
10 GB	10 RUs
50 GB	50 RUs
100 GB	100 RUs

To learn how to calculate your namespace size, see Storage.

Parameters that affect the size of the query response, such as top_k, include_metadata, and include_values, are not relevant for query cost; only the size of the namespace determines the number of RUs used.

Fetch

A fetch request uses 1 RU for every 10 records fetched, for example:

Fetched records	RUs
10	1
50	5
107	11

Specifying a non-existent ID or adding the same ID more than once does not increase the number of RUs used. However, a fetch request will always use at least 1 RU.

Fetching records by metadata uses the same cost model as fetching by ID: 1 RU for every 10 records fetched.

List

List has a fixed cost of 1 RU per call, with up to 100 records per call.

Write units

Write units (WUs) measure the storage and compute resources used by the following write requests:

Upsert
Update
Delete

Upsert

An upsert request uses 1 WU for each 1 KB of the request, with a minimum of 5 WUs per request. When an upsert modifies an existing record, the request uses 1 WU for each 1 KB of the existing record as well. For example, the following table shows the WUs used by upsert requests at different batch sizes and record sizes, assuming all records are new:

Records per batch	Dimension	Avg. metadata size	Avg. record size	WUs
1	768	100 bytes	3.2 KB	5
2	768	100 bytes	3.2 KB	7
10	1024	15,000 bytes	19.10 KB	191
100	768	500 bytes	3.57 KB	357
1000	1536	1000 bytes	7.14 KB	7140

Update

An update request uses 1 WU for each 1 KB of the new and existing record, with a minimum of 5 WUs per request. For example, the following table shows the WUs used by an update at different record sizes:

New record size	Previous record size	WUs
6.24 KB	6.50 KB	13
19.10 KB	15 KB	25
3.57 KB	5 KB	9
7.14 KB	10 KB	18
3.17 KB	3.17 KB	7

Updating records by metadata uses the same cost model as updating by ID: 1 WU for each 1 KB of the new and existing record.

Delete

A delete request uses 1 WU for each 1 KB of records deleted, with a minimum of 5 WUs per request. For example, the following table shows the WUs used by delete requests at different batch sizes and record sizes:

Records per batch	Dimension	Avg. metadata size	Avg. record size	WUs
1	768	100 bytes	3.2 KB	5
2	768	100 bytes	3.2 KB	7
10	1024	15,000 bytes	19.10 KB	191
100	768	500 bytes	3.57 KB	357
1000	1536	1000 bytes	7.14 KB	7140

Specifying a non-existent ID or adding the same ID more than once does not increase WU use. Deleting a namespace or deleting all records in a namespace using deleteAll uses 5 WUs.

Deleting records by metadata uses the same cost model as deleting by ID: 1 WU for each 1 KB of records deleted.

Storage

Storage costs are based on the size of an index on a per-gigabyte (GB) monthly rate. The size of an index is defined as the total size of its records across all namespaces. For the latest storage pricing rates, see Pricing. A record can include a dense vector, a sparse vector, or both. Use the formula for your index type to calculate total size:

Dense index
Sparse index
Hybrid index

A dense index contains records with one dense vector each.

Dense index records can also contain sparse vectors (when the index metric is set to dotproduct), which can be useful for hybrid search. To learn how to calculate the size of a hybrid index, see Hybrid index.

Calculate dense index size (assuming no sparse vectors)

Index size = Number of records × (
               ID size + 
               Metadata size +
               Dense vector dimensions × 4 bytes
             )

Where:

ID size and Metadata size are measured in bytes, averaged across all records.
Each Dense vector dimension uses 4 bytes.

Example dense index calculationsThese examples assume 8-byte IDs:

Records	Dense vector dimensions	Avg metadata size	Index size
500,000	768	500 bytes	1.79 GB
1,000,000	1536	1,000 bytes	7.15 GB
5,000,000	1024	15,000 bytes	95.5 GB
10,000,000	1536	1,000 bytes	71.5 GB

Example: 500,000 records × (8-byte ID + (768 dense vector dimensions × 4 bytes) + 500 bytes of metadata) = 1.79 GB

A sparse index contains records with one sparse vector each.Calculate sparse index size

Index size = Number of records × (
               ID size + 
               Metadata size +
               Number of non-zero sparse values × 8 bytes
             )

Where:

ID size and Metadata size are measured in bytes, averaged across all records.
Number of non-zero sparse values: Average number across all records. To find the count for a single record, check the length of the sparse vector’s indices or values array. Each non-zero value uses 8 bytes.

Example sparse index calculationsThese examples assume 8-byte IDs:

Records	Avg number of non-zero sparse values	Avg metadata size	Index size
500,000	10	500 bytes	0.29 GB
1,000,000	50	1,000 bytes	1.41 GB
5,000,000	100	15,000 bytes	79.0 GB
10,000,000	50	1,000 bytes	14.1 GB

Example: 500,000 records × (8-byte ID + (10 non-zero sparse values × 8 bytes) + 500 bytes of metadata) = 0.29 GB

A hybrid index contains records that each have one dense vector and an optional sparse vector.Calculate hybrid index size

Index size = Number of records × (
               ID size + 
               Metadata size +
               Dense vector dimensions × 4 bytes + 
               Number of non-zero sparse values × 8 bytes
             )

Where:

ID size and Metadata size are measured in bytes, averaged across all records.
Each Dense vector dimension uses 4 bytes.
Number of non-zero sparse values: Average number across all records, including those without sparse vectors. To find the count for a single record, check the length of the sparse vector’s indices or values array. Each non-zero value uses 8 bytes.

Example hybrid index calculationsThese examples assume 8-byte IDs:

Records	Dense vector dimensions	Avg number of non-zero sparse values	Avg metadata size	Index size
500,000	768	10	500 bytes	1.83 GB
1,000,000	1536	50	1,000 bytes	7.54 GB
5,000,000	1024	100	15,000 bytes	99.5 GB
10,000,000	1536	50	1,000 bytes	75.4 GB

Example: 500,000 records × (8-byte ID + (768 dense vector dimensions × 4 bytes) + (10 non-zero sparse values × 8 bytes) + 500 bytes of metadata) = 1.83 GB

Imports

Importing from object storage is the most efficient and cost-effective method to load large numbers of records into an index. The cost of an import is based on the size of the records read, whether the records were imported successfully or not. If the import operation fails (e.g., after encountering a vector of the wrong dimension in an import with on_error="abort"), you will still be charged for the records read. However, if the import fails because of an internal system error, you will not incur charges. In this case, the import will return the error message "We were unable to process your request. If the problem persists, please contact us at https://support.pinecone.io". For the latest import pricing rates, see Pricing.

Backups and restores

A backup is a static copy of a serverless index. Both the cost of storing a backup and restoring an index from a backup is based on the size of the index. For the latest backup and restore pricing rates, see Pricing.

Embedding

Pinecone hosts several embedding models so it’s easy to manage your vector storage and search process on a single platform. You can use a hosted model to embed your data as an integrated part of upserting and querying, or you can use a hosted model to embed your data as a standalone operation. Embedding costs are determined by how many tokens are in a request. In general, the more words contained in your passage or query, the more tokens you generate. For example, if you generate embeddings for the query, “What is the maximum diameter of a red pine?”, Pinecone Inference generates 10 tokens, then converts them into an embedding. If the price per token for your billing plan is $.08 per million tokens, then this API call costs $.00001. To learn more about tokenization, see Choosing an embedding model. For the latest embed pricing rates, see Pricing.

Embedding requests returns the total tokens generated. You can use this information to monitor and manage embedding costs.

Reranking

Pinecone hosts several reranking models so it’s easy to manage two-stage vector retrieval on a single platform. You can use a hosted model to rerank results as an integrated part of a query, or you can use a hosted model to rerank results as a standalone operation. Reranking costs are determined by the number of requests to the reranking model. For the latest rerank pricing rates, see Pricing.

Assistant

For details on how costs are incurred in Pinecone Assistant, see Assistant pricing.

Get started

Index data

Search

Optimize

Manage data

Manage cost

Move to production

Admin

Operations

Using pods

Minimum usage

Discounts

Serverless indexes

Read units

Query

Fetch

List

Write units

Upsert

Update

Delete

Storage

Imports

Backups and restores

Embedding

Reranking

Assistant

See also

Get started

Index data

Search

Optimize

Manage data

Manage cost

Move to production

Admin

Operations

Using pods

​Minimum usage

​Discounts

​Serverless indexes

​Read units

​Query

​Fetch

​List

​Write units

​Upsert

​Update

​Delete

​Storage

​Imports

​Backups and restores

​Embedding

​Reranking

​Assistant

​See also

Minimum usage

Discounts

Serverless indexes

Read units

Query

Fetch

List

Write units

Upsert

Update

Delete

Storage

Imports

Backups and restores

Embedding

Reranking

Assistant

See also