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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 for low-latency access.
• Integration: Connect with other AWS services like Amazon S3, Amazon RDS, and AWS Secrets Manager.
• Cost Control: Optimize expenses with various instance types and pricing models.
• Security and Compliance: Deploy Spice.ai within your AWS security perimeter using features like VPC isolation, security groups, IAM roles to meet organizational compliance requirements.

Deployment Options

Amazon EKS (Elastic Kubernetes Service)

Leverage Kubernetes orchestration with Amazon EKS for containerized Spice.ai deployments.

1. Create an EKS Cluster:

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

2. Deploy Spice.ai on EKS:

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

For comprehensive instructions and advanced configuration options, refer to the Amazon EKS User Guide, EKS Best Practices Guide, and Spice.ai Kubernetes Deployment Guide.

EC2 / AWS CloudFormation

Deploy Spice.ai directly on Amazon EC2 instances for maximum control over the environment.

1. Manual EC2 Deployment:

   • Launch an EC2 instance with your preferred Linux distribution
   • Install Docker
   • Run Spice.ai as a Docker Container on your EC2 instance
   • (Optional) Use Infrastructure as Code (IaC) tools like AWS CloudFormation or Terraform 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, including EC2 instances (using a Linux AMI), security groups, IAM roles, VPC, and subnets
   • Use EC2 UserData to automate Docker installation, pull the Spice.ai Docker image, retrieve configuration or secrets from AWS Parameter Store or Secrets Manager, and run the container with required environment variables
   • (Optional) Add parameters to your template for VPC ID, Subnet ID, , instance type, and secret names to enable flexible deployments

For detailed guidance and best practices, refer to the AWS CloudFormation User Guide, EC2 User Guide for Linux Instances, and AWS Systems Manager Parameter Store Documentation.

Amazon ECS (Elastic Container Service)

Deploy Spice.ai as containerized tasks on Amazon ECS for easy container management and flexible scaling.

1. Create an ECS Cluster:

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

2. Define a Task Definition:

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

3. Deploy Spice.ai on ECS:

   • Create an ECS Service to run and manage Spice.ai tasks.
   • Set up load balancing with NLB.
   • (Optional) Configure auto-scaling based on resource usage or CloudWatch metrics.

For more details, see the Amazon ECS Developer Guide and Spice.ai Docker Deployment Guide.

Authentication

Most AWS services that Spice connects to have explicit parameters for configuring authentication (usually by setting an access_key_id and secret_access_key). If explicit credentials are not provided, Spice follows the standard AWS SDK behavior for loading credentials from the environment based on the following sources in order:

1. Environment Variables:

   • AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
   • AWS_SESSION_TOKEN (if using temporary credentials)

2. Shared AWS Config/Credentials Files:

   • Config file: ~/.aws/config (Linux/Mac) or %UserProfile%\.aws\config (Windows)

   • Credentials file: ~/.aws/credentials (Linux/Mac) or %UserProfile%\.aws\credentials (Windows)

   • The AWS_PROFILE environment variable can be used to specify a named profile, otherwise the [default] profile is used.

   • Supports both static credentials and SSO sessions

   • Example credentials file:

   :::tip To set up SSO authentication:

   1. Run aws configure sso to configure a new SSO profile
   2. Use the profile by setting AWS_PROFILE=sso-profile
   3. Run aws sso login --profile sso-profile to start a new SSO session :::

3. AWS STS Web Identity Token Credentials:

   • Used primarily with OpenID Connect (OIDC) and OAuth

The connector will try each source in order until valid credentials are found. If no valid credentials are found, an authentication error will be returned.

:::note[IAM Permissions] Regardless of the credential source, the IAM role or user must have appropriate permissions (e.g., s3:ListBucket, s3:GetObject) to access the service. If the Spicepod connects to multiple different AWS services, the permissions should cover all of them. :::

Learn More

• Using AWS Secrets Manager Secret Store with Spice.ai