---

title: 'AWS Deployment Options' description: 'Guide to deploying Spice.ai applications on Amazon Web Services (AWS)' sidebar_label: 'AWS' sidebar_position: 2

Spice.ai provides multiple deployment options on Amazon Web Services (AWS), allowing you to leverage AWS's robust infrastructure for your data and AI applications. Whether you prefer virtual machines, container orchestration, or managed services, you can deploy Spice.ai to meet your specific requirements for performance, scalability, and cost efficiency.

Benefits of Deploying on AWS

• Scalability: Easily scale your Spice.ai applications with AWS's elastic infrastructure.
• Global Reach: Deploy across AWS's worldwide regions (opens in a new tab) for low-latency access.
• Integration: Connect with other AWS services like Amazon S3 (opens in a new tab), Amazon RDS (opens in a new tab), and AWS Secrets Manager (opens in a new tab).
• Cost Control: Optimize expenses with various instance types (opens in a new tab) and pricing models (opens in a new tab).
• Security and Compliance: Deploy Spice.ai within your AWS security perimeter using features like VPC (opens in a new tab) isolation, security groups (opens in a new tab), IAM roles (opens in a new tab) to meet organizational compliance requirements.

Deployment Options

Amazon EKS (Elastic Kubernetes Service)

Leverage Kubernetes (opens in a new tab) orchestration with Amazon EKS (opens in a new tab) for containerized Spice.ai deployments.

1. Create an EKS Cluster:

   • Use the AWS Management Console (opens in a new tab), AWS CLI (opens in a new tab), or eksctl (opens in a new tab) to create your cluster
   • Configure node groups (opens in a new tab) according to your workload requirements
   • (Optional) Use EKS Fargate profiles (opens in a new tab) for serverless container deployment

2. Deploy Spice.ai on EKS:

   • Apply Spice.ai Kubernetes manifests via Helm chart (opens in a new tab)
   • Configure persistent storage using Amazon EBS (opens in a new tab) or Amazon EFS (opens in a new tab)
   • Set up ingress with the AWS Network Load Balancer (NLB) (opens in a new tab)
   • (Optional) Automate cluster and resource provisioning with Infrastructure as Code (IaC) tools such as AWS CloudFormation (opens in a new tab) or Terraform (opens in a new tab) for consistent, repeatable deployments

For comprehensive instructions and advanced configuration options, refer to the Amazon EKS User Guide (opens in a new tab), EKS Best Practices Guide (opens in a new tab), and Spice.ai Kubernetes Deployment Guide (opens in a new tab).

EC2 / AWS CloudFormation

Deploy Spice.ai directly on Amazon EC2 (opens in a new tab) instances for maximum control over the environment.

1. Manual EC2 Deployment:

   • Launch an EC2 instance (opens in a new tab) with your preferred Linux distribution
   • Install Docker (opens in a new tab)
   • Run Spice.ai as a Docker Container (opens in a new tab) on your EC2 instance
   • (Optional) Use Infrastructure as Code (IaC) tools like AWS CloudFormation (opens in a new tab) or Terraform (opens in a new tab) to automate the provisioning, configuration, and management of EC2 resources for repeatable and consistent deployments

2. Automated EC2 Deployment with CloudFormation:

   • Define your infrastructure in a CloudFormation template (opens in a new tab), including EC2 instances (using a Linux AMI (opens in a new tab)), security groups, IAM roles, VPC, and subnets
   • Use EC2 UserData (opens in a new tab) to automate Docker installation, pull the Spice.ai Docker image (opens in a new tab), retrieve configuration or secrets from AWS Parameter Store (opens in a new tab) or Secrets Manager (opens in a new tab), and run the container with required environment variables
   • (Optional) Add parameters (opens in a new tab) to your template for VPC ID, Subnet ID, KeyPair (opens in a new tab), instance type, and secret names to enable flexible deployments
   • (Optional) Store sensitive data such as API keys in Parameter Store (opens in a new tab) or Secrets Manager (opens in a new tab) and reference them securely in UserData
   • (Optional) Deploy and manage your CloudFormation stack using the AWS Console (opens in a new tab), CLI (opens in a new tab), or CI/CD pipelines (opens in a new tab) for repeatable, version-controlled infrastructure

For detailed guidance and best practices, refer to the AWS CloudFormation User Guide (opens in a new tab), EC2 User Guide for Linux Instances (opens in a new tab), and AWS Systems Manager Parameter Store Documentation (opens in a new tab).

Amazon ECS (Elastic Container Service)

Deploy Spice.ai as containerized tasks on Amazon ECS (opens in a new tab) for easy container management and flexible scaling.

1. Create an ECS Cluster:

   • Choose a launch type: EC2 (opens in a new tab) (manage your own EC2 instances) or Fargate (opens in a new tab) (serverless).
   • Create the ECS cluster using the AWS Console (opens in a new tab), CLI (opens in a new tab), or Infrastructure as Code (CloudFormation (opens in a new tab), Terraform (opens in a new tab)).

2. Define a Task Definition:

   • Specify the Spice.ai Docker image (opens in a new tab), resource needs, networking, environment variables, and storage in a Task Definition (opens in a new tab).
   • (Optional) Use AWS Secrets Manager (opens in a new tab) or Parameter Store (opens in a new tab) to inject secrets securely.
   • Enable logging with Amazon CloudWatch (opens in a new tab).

3. Deploy Spice.ai on ECS:

   • Create an ECS Service (opens in a new tab) to run and manage Spice.ai tasks.
   • Set up load balancing with NLB (opens in a new tab).
   • (Optional) Configure auto-scaling (opens in a new tab) based on resource usage or CloudWatch metrics.
   • (Optional) Use CI/CD pipelines (opens in a new tab) for automated updates. Manage infrastructure with CloudFormation, Terraform, or the AWS CLI.

For more details, see the Amazon ECS Developer Guide (opens in a new tab) and Spice.ai Docker Deployment Guide (opens in a new tab).

Learn More

• Using AWS Secrets Manager Secret Store with Spice.ai (opens in a new tab)