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Workers AI
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Demo Applications and Reference Architectures

Workers AI can be used to build dynamic and performant services. The following demo applications and reference architectures showcase how to use Workers AI optimally within your architecture.

​​ Demos

Explore the following demo applications for Workers AI.

​​ Reference architectures

Explore the following reference architectures that use Workers AI:

  • Automatic captioning for video uploads: By integrating automatic speech recognition technology into video platforms, content creators, publishers, and distributors can reach a broader audience, including individuals with hearing impairments or those who prefer to consume content in different languages.
  • Composable AI architecture: The architecture diagram illustrates how AI applications can be built end-to-end on Cloudflare, or single services can be integrated with external infrastructure and services.
  • Content-based asset creation: Combining text-generation models with text-to-image models can lead to powerful AI systems capable of generating visual content based on input prompts. This integration can be achieved through a collaborative framework where a text-generation model generates prompts for the text-to-image model based on input text.
  • Fullstack Applications: Full-stack web applications leverage a combination of frontend and backend technologies, collectively forming a stack that powers the entire application. This technology stack encompasses various tools, frameworks, and languages, each serving a specific purpose within the development ecosystem.
  • Multi-vendor AI observability and control: By shifting features such as rate limiting, caching, and error handling to the proxy layer, organizations can apply unified configurations across services and inference service providers.
  • Retrieval Augmented Generation (RAG): Retrieval-Augmented Generation (RAG) is an innovative approach in natural language processing that integrates retrieval mechanisms with generative models to enhance text generation.
  • Serverless image content management: In this reference architecture diagram, we reveal how to leverage various components of Cloudflare’s ecosystem to construct a scalable image management solution. This solution integrates moderation principles via Cloudflare’s Workers AI platform and performs image classification through inference at the edge. The storage of images is handled by Cloudflare’s R2 product, an S3 API-like object storage system, while metadata is stored in a key/value store to enable content augmentation.