Cloudflare Workers AI

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Cloudflare Workers AI

Run machine learning models, powered by serverless GPUs, on Cloudflare's global network. Workers AI allows you to run AI models in a serverless way, without having to worry about scaling, maintaining, or paying for unused infrastructure. You can invoke models running on GPUs on Cloudflare's network from your own code â€” from Workers, Pages, or anywhere via the Cloudflare API.

General
Author	Cloudflare, Inc.
Website	Cloudflare Workers AI
Documentation	https://developers.cloudflare.com/workers-ai/
Type	Serverless AI Inference Platform
Launch Year	2023
GPU Network	180+ cities globally

Features

50+ Open-Source Models

Text generation (Llama, Mistral, and more)
Text embeddings and classification
Image generation and classification
Automatic speech recognition
Translation models
Object detection capabilities

Serverless Infrastructure

Pay-for-what-you-use pricing model
Automatic scaling with demand
No infrastructure management required
Fast cold start times with V8 isolates

Global Edge Network

AI inference close to users for low latency
Models available in 180+ cities worldwide
Reduced network bottlenecks
Consistent performance globally

Developer Platform Integration

Seamless integration with Cloudflare Workers
Works with Pages for full-stack AI applications
REST API for platform-agnostic access
Integration with Vectorize (vector database)
AI Gateway for monitoring and control

Key Capabilities

Edge AI Computing: Run AI models at the network edge for minimal latency
Serverless GPU Access: Access powerful GPU infrastructure without provisioning
Model Catalog: Curated selection of popular open-source AI models
Real-time Inference: Low-latency AI processing for interactive applications
Global Deployment: Deploy once, run everywhere on Cloudflare's network
Integrated Ecosystem: Works with R2 storage, D1 database, and other Cloudflare services

Use Cases

Building AI-powered chatbots and conversational interfaces
Real-time content moderation and classification
Image and video processing at scale
Personalization and recommendation engines
Automated translation and localization
Voice recognition and text-to-speech applications
RAG (Retrieval-Augmented Generation) systems
AI-powered API endpoints and microservices

Supported Model Categories

Large Language Models: For text generation and chat applications
Embedding Models: For semantic search and similarity matching
Image Models: For generation, classification, and analysis
Speech Models: For transcription and synthesis
Vision Models: For object detection and recognition
Translation Models: For multilingual content processing

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Cloudflare Cloudflare Workers AI AI technology Hackathon projects

Discover innovative solutions crafted with Cloudflare Cloudflare Workers AI AI technology, developed by our community members during our engaging hackathons.

Spec6: The enterprise intelligence operator

Spec6 uses Speechmatics in two distinct but connected ways: first as the live speech interface for the product, and second as a transcription engine for “spoken web” intelligence. That split is important, because this is not just “we added voice chat.” In the code, Speechmatics sits both at the front door of the system, where a user talks to Spec6 in real time, and deeper in the intelligence pipeline, where audio and video from the web can be converted into analyzable evidence. The live voice side starts in the frontend voice assistant at [/Users/nadhi/Desktop/win-win/src/frontend/react/chat/voice-assistant.tsx](/Users/nadhi/Desktop/win-win/src/frontend/react/chat/voice-assistant.tsx). The browser captures microphone input with `getUserMedia`, then converts the audio into the format Speechmatics expects: 16kHz mono PCM16. That matters because the browser does not just upload a blob after the user is done. It streams audio continuously in small chunks, which makes the experience feel like an actual assistant instead of a push-to-record form. The comment in that file describes the full loop clearly: mic input becomes PCM16, that stream goes to `/api/voice/transcribe/ws`, partial and final transcripts come back, and the final transcript is fed into the normal Spec6 agent loop. So the voice layer is not a separate AI product. It is an alternate input mode into the same reasoning engine that powers the rest of the app.

Adversarial Infrastructure Analyzer

Every phishing campaign, every business email compromise, every credential-harvesting operation begins the same way: an attacker registers a domain. That moment of registration is the earliest possible signal, and it happens in public. Certificate transparency logs record it. WHOIS feeds publish it. Passive DNS aggregators pick it up within minutes. The entire adversarial infrastructure lifecycle — from registration through weaponization to first victim — plays out across sources that are open, crawlable, and continuous. The core insight is that adversarial infrastructure has a predictable birth cycle — a domain gets registered, gets a TLS cert, gets DNS configured, and only then gets pointed at victims. The window between registration and first use is where you want to catch it, before any SIEM sees a packet. The pipeline works by watching the public signals of that birth cycle in real time.

LongTail Scout

LongTail Scout is a GTM-intelligence agent that surfaces the operators Apollo, ZoomInfo, and Clay can't find: the small, local, niche businesses — roofers, HVAC techs, dental clinics, salons — that aren't on LinkedIn. Those tools are built on the LinkedIn employee graph, so the long tail is invisible to them. LongTail Scout finds these operators through their own websites + Google + news in real time via Bright Data, and proves they're Apollo-blind. Type a niche x city — "roofing contractors in Houston" — and a 3-phase agent runs on a Cloudflare Worker: 1. Discovery — SERP queries via the Bright Data Scraping Browser, rendering Google to bypass bot detection. 2. Enrichment — each homepage and careers page rendered through the Scraping Browser; hiring, size, and recent activity extracted. 3. Synthesis — DeepSeek ranks the operators and writes a sales angle, with every claim citation-linked to the Bright Data fetch that produced it. Each operator's drill-down closes the GTM loop, all Bright-Data-backed: - Apollo-blind verification: a live LinkedIn search confirms the operator has no company page — hard proof Apollo can't see them. - Contact discovery: a real email, phone, and named contact from the contact pages. - Decision-maker finder: the owner/founder plus their LinkedIn profile. - Signal radar: live third-party news (funding, expansion, awards) as buying triggers. - Account brief: a one-click Markdown dossier with every source cited. Niche Recon reverses the funnel: paste what you sell, get the top verticals ranked by "Apollo-thinness" — the share of operators with no own domain Apollo can match — cross-referenced against a private 7M-business demand index. All 12 capabilities are also exposed as MCP tools, so any MCP client (Claude Desktop, Cursor) can drive the whole pipeline.

SignalReach AI

B2B sales is broken. Mass emails get a 0.1% reply rate, while manual research takes 45+ minutes per prospect. SignalReach AI solves this by automating the research and outreach workflow. It is an Intent-Based GTM Intelligence Engine that scrapes live web signals—like LinkedIn job openings, Reddit technical complaints, and recent funding news—in real-time. It then feeds this unstructured data into Gemini AI to analyze buying intent and automatically drafts a hyper-personalized B2B sales email addressing those exact pain points in under 10 seconds. We built the app using Next.js 16 and Tailwind CSS. For data ingestion, we integrated the Bright Data SERP API to bypass captchas. Our AI engine is powered by Google Gemini 2.5 Flash, utilizing strict JSON schemas to ensure reliable output. We hosted it on Vercel, using Supabase (PostgreSQL) for database management. Our biggest challenge was serverless timeouts. Scraping and AI generation took 10-12 seconds, breaking Vercel's standard 10-second limit. We overcame this by migrating our core API routes to Vercel Edge Runtime. We are incredibly proud to have built a resilient, end-to-end pipeline that magically turns a raw Reddit complaint into a polished sales pitch. Next, we plan to build a Chrome extension for LinkedIn integration and CSV bulk-processing features.

Competitor Intelligence Firehose

Competitor Intelligence Firehose is an automated competitor monitoring system built for the Web Data UNLOCKED 2026 Hackathon. It monitors four major technology companies (Microsoft, Google, Amazon, Apple) by scraping their public websites and LinkedIn company pages. The project demonstrates real-world business value by calculating Return on Investment (ROI) for automated competitor research. Manual competitor monitoring typically takes 8 hours per week for a business analyst at $75/hour, costing companies $31,200 annually in labor costs. This solution automates that entire process using Bright Data's web infrastructure. The system is configured with a Bright Data API key and $250 in credits, ready to integrate Web Unlocker, Browser API, and Proxy Network products for production-scale deployment. Key features include: - Automatic competitor data collection - Success rate tracking and error handling - JSON report generation for easy integration - ROI calculation showing 5,206% return on investment The architecture is designed to scale from 4 competitors to 100+ by leveraging Bright Data's infrastructure for bypassing CAPTCHAs, rotating IP addresses, and rendering JavaScript-heavy pages. This reduces maintenance overhead from hours per week to zero while providing always-fresh competitor intelligence. Technical implementation uses Python 3 with the requests library, runs on Termux (Android), and is version-controlled on GitHub. The project successfully scrapes 4/4 competitor sites and generates comprehensive JSON reports ready for enterprise analytics pipelines.

Executor

Executor is an autonomous meeting-to-execution enforcement system built for enterprise teams that lose accountability after meetings. Most AI meeting tools stop at summaries, but Executor continues into execution by extracting commitments, identifying owners, deadlines, priorities, and source quotes, then turning those decisions into tracked work. Users can upload transcripts or run live meetings with Speechmatics real-time transcription. Gemini analyzes the meeting, extracts structured action items, scores execution risk, and powers follow-up drafting. Each commitment can be synced directly into Jira, Asana, and Notion, connecting meeting decisions to the tools teams already use. Executor also runs a background enforcement agent deployed on Vultr. The monitor checks deadlines every six hours, marks overdue commitments, drafts follow-up emails, retries failed sends, and escalates to managers after repeated missed follow-ups. Human review remains available before sensitive messages are sent, while every autonomous action is stored in an audit log. The application is deployed publicly on Cloudflare Workers, uses Supabase for authentication and database storage, Gemini for reasoning, Speechmatics for voice transcription, and Vultr for the scheduled enforcement worker. Executor closes the gap between “we decided” and “it actually got done.”