Save on Pinecone costs

You can lower your Pinecone bill with discounted rates and by changing how you load and read data. For how usage is measured, see Understand Pinecone cost.

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Credits and discounts

In addition to the workload optimizations below, you can lower your effective rate:

• Prepaid credits and annual commitments earn discounted usage rates. See Prepaid credits.
• Dedicated read nodes can lower cost for sustained, high read throughput when you fully use the provisioned capacity (see Choose the right index capacity mode).
• Volume discounts. Standard and Enterprise customers can contact Support to discuss cost optimization and discounts.

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Prefer bulk import over upsert for large loads

When you need to populate a new namespace or load a large dataset (for example, millions of records or hundreds of GB), importing from object storage is usually the most efficient and cost-effective path compared to streaming upserts.

• Import is optimized for one-time or bulk loads from Parquet in your object store and is priced based on data read during the job. See Import cost.
• Upsert is priced in write units based on request size; many small requests can cost more than fewer large ones for the same total data. See Write unit pricing.

Use upsert (including batch upsert) for ongoing, incremental ingestion after your initial load. For how import and upsert compare, see the data ingestion overview.

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Use namespaces for multitenancy

If you need to isolate each customer’s or user’s data, partition tenants with namespaces instead of creating separate indexes. This often lowers storage overhead and query cost, because a query’s cost depends on the size of the namespace it scans.

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Right-size reads and queries

• Avoid returning vector values from a query when you don’t need them (include_values=false, the default), especially at high top_k. Values are typically the largest part of a response, so leaving them out lowers egress and helps you stay within your plan’s egress allowance.
• Use metadata filters so queries scan fewer records where your workload allows.
• To retrieve known records without a query, use a hierarchical ID schema, list record IDs by prefix, and then fetch the records you need. A query’s read units grow with namespace size, while fetch uses 1 read unit per 10 records.

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Choose the right index capacity mode

For sustained, high read throughput, dedicated read nodes can be more cost-effective than on-demand when you fully use provisioned read capacity. For spiky or low-QPS workloads, on-demand may be cheaper. See When to use dedicated read nodes and Understand Pinecone cost.

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See also

• Decrease latency
• Increase throughput
• Understand Pinecone cost
• Monitor usage and costs