> ## 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.

***

## 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>

***

## 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>

### Log Object Path Format

Configure how log files are organized in your blob storage using the `LOG_STORE_FILE_PATH_FORMAT` environment variable.

| Value          | Format            | Example Path                                                                      |
| -------------- | ----------------- | --------------------------------------------------------------------------------- |
| `v1` (default) | Flat structure    | `30/<organisation-id>/<log-id>.json`                                              |
| `v2`           | Time-hierarchical | `30/<organisation-id>/<workspace-slug>/<year>/<month>/<day>/<hour>/<log-id>.json` |

The `v2` format organizes logs by time hierarchy, making it easier to manage retention policies and query logs for specific time periods.

<Note>
  Changing `LOG_STORE_FILE_PATH_FORMAT` only affects newly written logs. Previously written logs retain their original path format and are not migrated.
</Note>

<Note>
  The `v2` format is not supported for air-gapped deployments where `LOG_STORE` is set to `control_plane`.
</Note>

***

## 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

Semantic caching stores embeddings in a vector database (**Milvus** or **Pinecone**) so similar prompts can reuse LLM responses. Embedding provider settings, vector store environment variables, and provider-specific setup (collection schema for Milvus, index naming and metric for Pinecone) are documented in **[Cache (Simple & Semantic)](/product/ai-gateway/cache-simple-and-semantic#set-up-semantic-caching-self-hosted)** under *Set up semantic caching (self-hosted)*.