workers-ai

Build serverless AI workflows by orchestrating LLMs, image generation, and embeddings at the edge. You can configure Wrangler, expose REST-like endpoints, and deploy minimal Workers that call the AI binding to process AI tasks with low latency.

Implement RAG by building edge vector stores with Vectorize and generating embeddings via Workers AI. You can perform fast similarity search across these vectors to retrieve relevant context, enabling scalable retrieval-augmented generation workflows directly on edge compute.

Yes, Cloudflare AI Gateway supports centralized management of multiple AI providers with logging, caching, rate limiting, and fallbacks. This allows you to control traffic, optimize costs, and ensure reliability when routing requests across different AI services.

Yes, you can run image generation and speech recognition at the edge using Workers AI. The platform provides simple bindings and tooling to execute multimodal AI tasks, allowing you to generate images and process speech directly within your serverless edge environment.

Configure Wrangler to set up the edge environment for AI, then deploy a Worker that uses AI bindings to expose REST-like endpoints. This approach allows you to interact with LLM services, orchestrate workflows, and provide concrete usage examples for production.

Running embeddings and LLMs on edge compute is subject to platform-specific execution limits and model availability. While Workers AI enables scalable, low-latency environments, you must configure AI Gateway fallbacks and rate limiting to manage provider limits and ensure workflow reliability.