# Guidance for an AI-Powered Studio on AWS

## Overview

This Guidance helps studios create cinematic-level AI-generated content at scale while maintaining full IP provenance and creative consistency across all assets. A unified artist interface abstracts complex AI tools into a streamlined experience, automatically tracking asset versions and lineage as creative teams work. The platform supports both hosted models through Amazon Bedrock and Amazon SageMaker AI, as well as third-party models, with all configurations managed centrally so artists can focus on creativity rather than tooling. You can produce high-quality creative content faster with confidence that every asset is traceable, governed, and built on a scalable foundation that grows with your studio's needs.

## Benefits

### Unify AI tools for artists

Consolidate multiple AI content generation models into a single governed interface. Enable your creative teams to produce images, video, and audio without managing disparate tools or infrastructure.


### Track content lineage automatically

Capture full provenance for every AI-generated asset, including models, parameters, and inputs used. Reduce IP and compliance risk with immutable audit trails stored in Amazon DynamoDB.


### Scale GPU inference on demand

Leverage AWS Deadline Cloud to provision GPU infrastructure only when needed for parallel inference workloads. Avoid fixed compute costs while supporting studio-wide content generation at scale.


## How it works

This architecture diagram shows how to build an AI-powered studio platform on AWS that enables creative teams to generate cinematic-level content at scale while maintaining full IP provenance and creative consistency. [Download the architecture diagram.](https://d1.awsstatic.com/onedam/marketing-channels/website/aws/en_US/solutions/approved/documents/architecture-diagrams/ai-powered-studio-on-aws.pdf)

![Architecture diagram for an AI-Powered Studio on AWS](/images/solutions/ai-powered-studio-on-aws/images/ai-powered-studio-on-aws-1.png)

1. **Step 1**: Artists access the studio platform through a unified web interface served by Amazon CloudFront for low-latency global delivery.
1. **Step 2**: User authentication and authorization are managed through Amazon Cognito to control access to studio resources and AI models.
1. **Step 3**: API requests route through Amazon API Gateway to backend services that orchestrate AI content generation workflows.
1. **Step 4**: AWS Lambda functions handle request processing, model selection, and workflow orchestration for content generation tasks.
1. **Step 5**: Amazon Bedrock provides access to foundation models for generating images, video, and audio content with built-in safety and governance controls.
1. **Step 6**: Amazon SageMaker AI hosts custom and third-party models with GPU-accelerated inference endpoints that scale based on studio workload demands.
1. **Step 7**: AWS Deadline Cloud provisions render farm infrastructure for parallel GPU inference workloads, scaling compute resources on demand.
1. **Step 8**: Asset provenance and lineage metadata are stored in Amazon DynamoDB, creating immutable audit trails for every generated asset including model versions, parameters, and input references.
1. **Step 9**: Generated content assets are stored in Amazon S3 with versioning enabled, providing durable storage and enabling asset lifecycle management.
[Read usage guidelines](/solutions/guidance-disclaimers/)