> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Production checklist

> Prepare your indexes for production with best practices.

This page provides recommendations and best practices for preparing your Pinecone indexes for production, anticipating production issues, and enabling reliability and growth.

## Prepare your project structure

One of the first steps towards building a production-ready Pinecone index is configuring your project correctly.

* Consider [creating a separate project](/guides/projects/create-a-project) for your development and production indexes, to allow for testing changes to your index before deploying them to production.
* Ensure that you have properly [configured user access](/guides/projects/understanding-projects#project-roles) to the Pinecone console, so that only those users who need to access the production index can do so.
* Ensure that you have properly configured access through the API by [managing API keys](/guides/projects/manage-api-keys) and using API key permissions.

Consider how best to [manage the API keys](/guides/projects/manage-api-keys) associated with your production project. In order to [make calls to the Pinecone API](/guides/get-started/quickstart), you must provide a valid API key for the relevant Pinecone project.

## Enforce security

Use Pinecone's [security features](/guides/production/security-overview) to protect your production data:

* Data security
  * Private endpoints
  * Customer-managed encryption keys (CMEK) for hosted projects; for BYOC, use KMS on your own resources ([Bring your own cloud](/guides/production/bring-your-own-cloud))
* Authorization
  * API keys
  * Role-based access control (RBAC)
  * Organization single sign-on (SSO)
* Audit logs
* Bring your own cloud

## Design your indexes for scale

Follow these best practices when designing and populating your indexes:

* **Data ingestion**: For large datasets (10M+ records), [import from object storage](/guides/index-data/import-data) for the most efficient and cost-effective ingestion. For ongoing ingestion, [upsert in batches](/guides/index-data/upsert-data#upsert-in-batches) to optimize speed and efficiency. See the [data ingestion overview](/guides/index-data/data-ingestion-overview) for details.
* **Dimensionality**: Consider the dimensionality of your vectors. Higher dimensions can offer more accuracy but require more resources.
* **Data modeling**: Use [structured IDs](/guides/index-data/data-modeling#use-structured-ids) (e.g., `document_id#chunk_number`) for efficient operations. Design [metadata](/guides/index-data/data-modeling#include-metadata) to support filtering, linking related chunks, and traceability. See the [data modeling guide](/guides/index-data/data-modeling) for details.
* **Namespaces**: When indexing, try to [use namespaces to keep your data among tenants separate](/guides/index-data/implement-multitenancy), and do not use multiple indexes for this purpose. Namespaces are more efficient and more affordable in the long run.

## Understand database limits

Architect your application to work within Pinecone's [database limits](/reference/api/database-limits):

* **Rate limits**: Serverless indexes have per-second operation limits for queries, upserts, updates, and deletes. [Implement error handling with exponential backoff](/guides/production/error-handling) to handle rate limit errors gracefully.
* **Size limits**: Be aware of constraints on vector dimensionality, metadata size per record, record ID length, maximum `top_k` values, and query result sizes. Design your [data model](/guides/index-data/data-modeling) accordingly.
* **Index limits**: Plan for index capacity based on your [plan tier](https://www.pinecone.io/pricing/). Use [namespaces](/guides/index-data/implement-multitenancy) to partition data within indexes rather than creating multiple indexes.
* **Plan limits**: The Starter and Builder plans have monthly read/write unit limits. Upgrade to Standard or Enterprise for unlimited read/write units and higher throughput needs.

<Note>
  Indexes built on [Dedicated Read Nodes](/guides/index-data/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](/guides/index-data/dedicated-read-nodes) guide.
</Note>

## Test your query results

Before you move your index to production, make sure that your index is returning accurate results in the context of your application by [identifying the appropriate metrics](https://www.pinecone.io/learn/offline-evaluation/) for evaluating your results.

## Optimize performance

Before serving production workloads, optimize your Pinecone implementation:

* **Increase search relevance**: Use techniques like reranking, metadata filtering, hybrid search, and chunking strategies to improve result quality. See [increase search relevance](/guides/optimize/increase-relevance) for details.
* **Increase throughput**: Import from object storage, upsert in batches, use parallel operations, and leverage Python SDK optimizations like gRPC. See [increase throughput](/guides/optimize/increase-throughput) for details.
* **Decrease latency**: Use namespaces, filter by metadata, target indexes by host, reuse connections, and deploy in the same cloud region as your index. See [decrease latency](/guides/optimize/decrease-latency) for details.
* **Save on costs**: Prefer bulk import for large initial loads, use namespaces for multitenancy, and avoid unnecessary data in query responses. See [save on costs](/guides/optimize/save-on-costs) for details.

## Backup up your indexes

In order to enable long-term retention, compliance archiving, and deployment of new indexes, consider backing up your production indexes by [creating a backup or collection](/guides/manage-data/back-up-an-index).

## Implement error handling

Prepare your application to handle errors gracefully:

* Implement [error handling and retry logic](/guides/production/error-handling) with exponential backoff
* Handle different error types appropriately (4xx vs 5xx)
* Monitor error rates and set up alerts
* Check [status.pinecone.io](https://status.pinecone.io) before escalating issues

## Configure monitoring

Prepare to [monitor the production performance and availability of your indexes](/guides/production/monitoring).

## Configure CI/CD

Use [Pinecone in CI/CD](/guides/production/automated-testing) to safely test changes before deploying them to production.

## Know how to get support

If you need help, [contact Support](https://app.pinecone.io/organizations/-/settings/support/ticket), or talk to the [Pinecone community](https://www.pinecone.io/community/). Ensure that your [plan tier](https://www.pinecone.io/pricing/) matches the support and availability SLAs you need. This may require you to upgrade to Enterprise.