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A modern storefront can look healthy while malicious JavaScript quietly siphons revenue, hijacks clicks, or rewrites analytics. See how Cloudflare's machine learning models surface evasive client-side attacks for analyst investigation.
Cloudflare Radar Researcher is a new AI-powered tool that lets you explore global Internet trends and traffic data using plain language. Built entirely on Cloudflare's Developer Platform, it turns natural language queries into real, interactive charts.
Cloudflare is unifying AI Gateway and Workers AI into a single control plane, giving developers observability, billing, and dynamic routing across both managed GPUs and external providers. Learn how unified bindings and model-first routing simplify building resilient AI applications.
Michelle Chen and Ming Lu
By replacing manual issue verification with isolated AI subagents running in GitHub Actions, the Astro maintainers reduced open issue count by 85%. This post explores the architecture behind automated bug reproduction, patch verification, and preview releases.
Serving frontier models like Kimi and GLM means fighting for GPU memory. Here's how we quantize KV caches, compress model weights, and add integrity checks to serve them faster, cheaper, and safely.
Cloudflare is deepening our investment in AI with the addition of team members from Ensemble AI, focusing on machine learning infrastructure and efficiency.
VoidZero, the team behind Vite, Vitest, Rolldown, Oxc, and Vite+, is joining Cloudflare. Vite stays open source, vendor-agnostic, and built for everyone.
We built our internal AI engineering stack on the same products we ship. That means 20 million requests routed through AI Gateway, 241 billion tokens processed, and inference running on Workers AI, serving more than 3,683 internal users. Here's how we did it.
Agents Week 2026 is a wrap. Let’s take a look at everything we announced, from compute and security to the agent toolbox, platform tools, and the emerging agentic web. Everything we shipped for the agentic cloud.
We're building AI Gateway into a unified inference layer for AI, letting developers call models from 14+ providers. New features include Workers AI binding integration and an expanded catalog with multimodal models.
Ming Lu and Michelle Chen
We built a custom technology stack to run fast large language models on Cloudflare’s infrastructure. This post explores the engineering trade-offs and technical optimizations required to make high-performance AI inference accessible.
Agents are becoming multi-channel. That means making them available wherever your users already are — including the inbox. Today, Cloudflare Email Service enters public beta with the infrastructure layer to make that easy: send, receive, and process email natively from your agents.
Agent Lee is an in-dashboard agent that shifts Cloudflare’s interface from manual tab-switching to a single prompt. Using sandboxed TypeScript, it helps you troubleshoot and manage your stack as a grounded technical collaborator.
Cloudflare Mesh provides secure, private network access for users, nodes, and autonomous AI agents. By integrating with Workers VPC, developers can now grant agents scoped access to private databases and APIs without manual tunnels.
Cloudflare's mission has always been to help build a better Internet. Sometimes that means building for the Internet as it exists. Sometimes it means building for the Internet as it's about to become. This week, we're kicking off Agents Week, dedicated to what comes next.
Cloudflare’s global network has officially crossed 500 Tbps of external capacity, enough to route more than 20% of the web and absorb the largest DDoS attacks ever recorded.
We’re introducing Dynamic Workers, which allow you to execute AI-generated code in secure, lightweight isolates. This approach is 100 times faster than traditional containers, enabling millisecond startup times for AI agent sandboxing.
Kenton Varda, Sunil Pai, and Ketan Gupta
Kimi K2.5 is now on Workers AI, helping you power agents entirely on Cloudflare’s Developer Platform. Learn how we optimized our inference stack and reduced inference costs for internal agent use cases.
