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

# Sidecar on GCP

The Stigg Sidecar service is available as a Docker container and can be deployed on Google Cloud Platform (GCP) using various compute services.

## Deployment options on GCP

You can deploy the Sidecar on GCP in one of the following ways:

1. **Google Kubernetes Engine (GKE)**: Ideal for production deployments using the [sidecar pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/sidecar).
2. **Google Cloud Run**: For event-driven or serverless-style deployments where scale-to-zero is needed.
3. **Google Compute Engine (GCE)**: Running the container directly on a VM with Docker installed.
4. **Standalone service**: Run the Sidecar as a central service accessible over an internal/external IP and port.

All deployment methods use the same Sidecar Docker image hosted on AWS ECR.

## Pulling the Sidecar Docker image

To deploy the Sidecar, start by pulling the image:

<CodeGroup>
  ```shell theme={null}
  docker pull public.ecr.aws/stigg/sidecar:latest
  ```
</CodeGroup>

## Running the Sidecar service

To run the Sidecar container locally or as part of a custom deployment:

<CodeGroup>
  ```shell theme={null}
  docker run -it -p 80:80 \
    -e SERVER_API_KEY="<SERVER_API_KEY>" \
    public.ecr.aws/stigg/sidecar:latest
  ```
</CodeGroup>

## Using the Sidecar pattern in Kubernetes

In Kubernetes environments (such as GKE), the Sidecar container can be deployed alongside your application container in the same pod. This allows for low-latency gRPC communication via `localhost`.

You can install the Sidecar using either `helm` or `kustomize`. For detailed instructions, resources, and examples, visit our [GitHub repository](https://github.com/stiggio/stigg-helm-charts).

## Key features for GCP deployment

* **Cross-platform**: Runs anywhere Docker is supported.
* **Language neutral**: gRPC APIs defined using Protocol Buffers.
* **Caching support**: Supports both in-memory and Redis-based cache layers.
* **Horizontal scaling**: Stateless design allows scaling with your GCP workloads.
* **Health monitoring**: Supports `/livez`, `/readyz`, and `/metrics` endpoints.
* **Configurable**: Uses environment variables for flexible runtime setup.