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

# Enterprise Components

Portkey's Enterprise Components provide the core infrastructure needed for production deployments. Each component handles a specific function - analytics, logging, or caching - with multiple implementation options to match your requirements.

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## Analytics Store

Portkey leverages Clickhouse as the primary Analytics Store for the Control Panel, offering powerful capabilities for handling large-scale analytical workloads.

<CardGroup cols={3}>
  <Card title="Clickhouse" icon="columns-3" href="https://github.com/Portkey-AI/helm-chart/tree/main/helm/enterprise#analytics-store" />
</CardGroup>

<Note>
  Portkey supports exporting your Clickhouse analytics data to OpenTelemetry (OTel) compatible collectors, allowing you to integrate Portkey's analytics with your existing observability infrastructure.
</Note>

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## Log Store

Portkey provides flexible options for storing and managing logs in your enterprise deployment. Choose from various storage solutions including MongoDB for document-based storage, AWS S3 for cloud-native object storage, or Wasabi for cost-effective cloud storage. Each option offers different benefits in terms of scalability, cost, and integration capabilities.

<CardGroup cols={3}>
  <Card title="MongoDB" icon="leaf" href="/integrations/libraries/mongodb" />

  <Card title="AWS S3" icon="cubes" href="https://github.com/Portkey-AI/helm-chart/tree/main/helm/enterprise#log-storage" />

  <Card title="Wasabi" href="https://github.com/Portkey-AI/helm-chart/tree/main/helm/enterprise#log-storage" icon="trees" />
</CardGroup>

***

## Cache Store

Portkey supports robust caching solutions to optimize performance and reduce latency in your enterprise deployment. Choose between Redis for in-memory caching or AWS ElastiCache for a fully managed caching service.

<CardGroup cols={3}>
  <Card title="Redis" icon="r" href="https://github.com/Portkey-AI/helm-chart/tree/main/helm/enterprise#cache-store" />

  <Card title="AWS Elastic Cache" icon="aws" href="https://github.com/Portkey-AI/helm-chart/tree/main/helm/enterprise#cache-store" />
</CardGroup>

## Semantic Cache Store

Portkey supports semantic caching capabilities that allow for intelligent caching based on the semantic meaning of requests rather than exact matches. For this functionality, Portkey requires a vector database to efficiently store and retrieve embeddings. Both Milvus and Pinecone are supported vector databases for Portkey's semantic cache implementation.

* You need a **Milvus** deployment with a collection configured with 1536 dimensions

* **Pinecone** can be configured with a Pinecone index of 1536 dimensions to store the embeddings

This specific dimension size is required as Portkey uses OpenAI's text-embedding-small model to create embeddings