Troubleshooting network connectivity and performance with Cloudflare AI

Monitoring a corporate network and troubleshooting any performance issues across that network is a hard problem, and it has become increasingly complex over time. Imagine that you’re maintaining a corporate network, and you get the dreaded IT ticket. An executive is having a performance issue with an application, and they want you to look into it. The ticket doesn’t have a lot of details. It simply says: “Our internal documentation is taking forever to load. PLS FIX NOW”.

In the early days of IT, a corporate network was built on-premises. It provided network connectivity between employees that worked in person and a variety of corporate applications that were hosted locally.

The shift to cloud environments, the rise of SaaS applications, and a “work from anywhere” model has made IT environments significantly more complex in the past few years. Today, it’s hard to know if a performance issue is the result of:

  • An employee’s device

  • Their home or corporate wifi

  • The corporate network

  • A cloud network hosting a SaaS app

  • An intermediary ISP

A performance ticket submitted by an employee might even be a combination of multiple performance issues all wrapped together into one nasty problem.

Cloudflare built Cloudflare One, Continue reading

The crawl-to-click gap: Cloudflare data on AI bots, training, and referrals

In 2025, Generative AI is reshaping how people and companies use the Internet. Search engines once drove traffic to content creators through links. Now, AI training crawlers — the engines behind commonly-used LLMs — are consuming vast amounts of web data, while sending far fewer users back. We covered this shift, along with related trends and Cloudflare features (like pay per crawl) in early July. Studies from Pew Research Center (1, 2) and Authoritas already point to AI overviews — Google’s new AI-generated summaries shown at the top of search results — contributing to sharp declines in news website traffic. For a news site, this means lots of bot hits, but far fewer real readers clicking through — which in turn means fewer people clicking on ads or chances to convert to subscriptions.

Cloudflare's data shows the same pattern. Crawling by search engines and AI services surged in the first half of 2025 — up 24% year-over-year in June — before slowing to just 4% year-over-year growth in July. How is the space evolving? Which crawling purposes are most common, and how is that changing? Spoiler: training-related crawling is leading the way. In this post, we track Continue reading

Running Palo Alto Firewalls on Proxmox

Running Palo Alto Firewalls on Proxmox

If you follow me or my blog, you may know that I moved my homelab to Proxmox. Even though I already have a physical Palo Alto firewall, I also needed to set up a Palo Alto VM. After some reading and research, and with the help of a great guide I found, I managed to get Palo Alto running on Proxmox. I thought it would be useful to write a post about it for anyone else trying to do the same.

At a high level, you need to download the Palo Alto QCOW image. I’m using PAN-OS 11.2.5 and downloaded the image called PA-VM-KVM-11.2.5.qcow2. You will also need multiple network interfaces on Proxmox. With Palo Alto, you need at least two to begin with, one for management and one for data.

When I say Proxmox interfaces or NICs, I mean the virtual network adapters that you can assign to your VM. These map to your physical or virtual bridges on the Proxmox host, and they let you connect the firewall VM to different parts of your network.

Running Palo Alto Firewalls on Proxmox
I have two linux bridges

The first step is to copy the Palo Alto QCOW image over to Continue reading

N4N037: IPsec Basics

It’s time to talk crypto. No, not the Bitcoin kind. Ethan and Holly introduce the basics of IPsec, the protocol that authenticates and encrypts traffic between endpoints. They discuss what it is, how it provides trustworthiness and secrecy to IP traffic, and common use cases. They review the different types of IPsec protocols and modes,... Read more »

A deeper look at AI crawlers: breaking down traffic by purpose and industry

Search platforms historically crawled web sites with the implicit promise that, as the sites showed up in the results for relevant searches, they would send traffic on to those sites — in turn leading to ad revenue for the publisher. This model worked fairly well for several decades, with a whole industry emerging around optimizing content for optimal placement in search results. It led to higher click-through rates, more eyeballs for publishers, and, ideally, more ad revenue. However, the emergence of AI platforms over the last several years, and the incorporation of AI "overviews" into classic search platforms, has turned the model on its head. When users turn to these AI platforms with queries that used to go to search engines, they often won't click through to the original source site once an answer is provided — and that assumes that a link to the source is provided at all! No clickthrough, no eyeballs, and no ad revenue. 

To provide a perspective on the scope of this problem, Radar launched crawl/refer ratios on July 1, based on traffic seen across our whole customer base. These ratios effectively compare the number of crawling requests for HTML pages from the crawler Continue reading

The age of agents: cryptographically recognizing agent traffic

On the surface, the goal of handling bot traffic is clear: keep malicious bots away, while letting through the helpful ones. Some bots are evidently malicious — such as mass price scrapers or those testing stolen credit cards. Others are helpful, like the bots that index your website. Cloudflare has segmented this second category of helpful bot traffic through our verified bots program, vetting and validating bots that are transparent about who they are and what they do.

Today, the rise of agents has transformed how we interact with the Internet, often blurring the distinctions between benign and malicious bot actors. Bots are no longer directed only by the bot owners, but also by individual end users to act on their behalf. These bots directed by end users are often working in ways that website owners want to allow, such as planning a trip, ordering food, or making a purchase.

Our customers have asked us for easier, more granular ways to ensure specific bots, crawlers, and agents can reach their websites, while continuing to block bad actors. That’s why we’re excited to introduce signed agents, an extension of our verified bots program that gives a new bot Continue reading

Make Your Website Conversational for People and Agents with NLWeb and AutoRAG

Publishers and content creators have historically relied on traditional keyword-based search to help users navigate their website’s content. However, traditional search is built on outdated assumptions: users type in keywords to indicate intent, and the site returns a list of links for the most relevant results. It’s up to the visitor to click around, skim pages, and piece together the answer they’re looking for. 

AI has reset expectations and that paradigm is breaking: how we search for information has fundamentally changed.

Your New Type of Visitors

Users no longer want to search websites the old way. They’re used to interacting with AI systems like Copilot, Claude, and ChatGPT, where they can simply ask a question and get an answer. We’ve moved from search engines to answer engines. 

At the same time, websites now have a new class of visitors, AI agents. Agents face the same pain with keyword search: they have to issue keyword queries, click through links, and scrape pages to piece together answers. But they also need more: a structured way to ask questions and get reliable answers across websites. This means that websites need a way to give the agents they trust controlled access, so Continue reading

The next step for content creators in working with AI bots: Introducing AI Crawl Control

Empowering content creators in the age of AI with smarter crawling controls and direct communication channels

Imagine you run a regional news site. Last month an AI bot scraped 3 years of archives in minutes — with no payment and little to no referral traffic. As a small company, you may struggle to get the AI company's attention for a licensing deal. Do you block all crawler traffic, or do you let them in and settle for the few referrals they send? 

It’s picking between two bad options.

Cloudflare wants to help break that stalemate. On July 1st of this year, we declared Content Independence Day based on a simple premise: creators deserve control of how their content is accessed and used. Today, we're taking the next step in that journey by releasing AI Crawl Control to general availability — giving content creators and AI crawlers an important new way to communicate.

AI Crawl Control goes GA

Today, we're rebranding our AI Audit tool as AI Crawl Control and moving it from beta to general availability. This reflects the tool's evolution from simple monitoring to detailed insights and control over how AI systems can access your content. 

The Continue reading

Evaluating image segmentation models for background removal for Images

Last week, we wrote about face cropping for Images, which runs an open-source face detection model in Workers AI to automatically crop images of people at scale.

It wasn’t too long ago when deploying AI workloads was prohibitively complex. Real-time inference previously required specialized (and costly) hardware, and we didn’t always have standard abstractions for deployment. We also didn’t always have Workers AI to enable developers — including ourselves — to ship AI features without this additional overhead.

And whether you’re skeptical or celebratory of AI, you’ve likely seen its explosive progression. New benchmark-breaking computational models are released every week. We now expect a fairly high degree of accuracy — the more important differentiators are how well a model fits within a product’s infrastructure and what developers do with its predictions.

This week, we’re introducing background removal for Images. This feature runs a dichotomous image segmentation model on Workers AI to isolate subjects in an image from their backgrounds. We took a controlled, deliberate approach to testing models for efficiency and accuracy.

Here’s how we evaluated various image segmentation models to develop background removal.

A primer on image segmentation

In computer vision, image segmentation is the process of splitting Continue reading

Network Automation Reality Check with William Collins

In early August, William Collins invited me to chat about a sarcastic comment I made about a specific automation tool I have a love-hate relationship with on LinkedIn.

We quickly agreed not to go (too deep) into tool-bashing. Instead, we discussed the eternal problems of network automation, from unhealthy obsession with tools to focus on point solutions while lacking the bigger picture or believing in vendor-delivered nirvana.

Listen to the podcast

IBM And AMD Tag Team On Hybrid Classical-Quantum Supercomputers

As we talked about a decade ago in the wake of launching The Next Platform, quantum computers – at least the fault tolerant ones being built by IBM, Google, Rigetti, and a few others – need a massive amount of traditional Von Neumann compute to help maintain their state, assist with qubit error correction, and assist with their computations. …

IBM And AMD Tag Team On Hybrid Classical-Quantum Supercomputers was written by Timothy Prickett Morgan at The Next Platform.

AI Gateway now gives you access to your favorite AI models, dynamic routing and more — through just one endpoint

Getting the observability you need is challenging enough when the code is deterministic, but AI presents a new challenge — a core part of your user’s experience now relies on a non-deterministic engine that provides unpredictable outputs. On top of that, there are many factors that can influence the results: the model, the system prompt. And on top of that, you still have to worry about performance, reliability, and costs. 

Solving performance, reliability and observability challenges is exactly what Cloudflare was built for, and two years ago, with the introduction of AI Gateway, we wanted to extend to our users the same levels of control in the age of AI. 

Today, we’re excited to announce several features to make building AI applications easier and more manageable: unified billing, secure key storage, dynamic routing, security controls with Data Loss Prevention (DLP). This means that AI Gateway becomes your go-to place to control costs and API keys, route between different models and providers, and manage your AI traffic. Check out our new AI Gateway landing page for more information at a glance.

Connect to all your favorite AI providers

When using an AI provider, you typically have to sign up for Continue reading

State-of-the-art image generation Leonardo models and text-to-speech Deepgram models now available in Workers AI

When we first launched Workers AI, we made a bet that AI models would get faster and smaller. We built our infrastructure around this hypothesis, adding specialized GPUs to our datacenters around the world that can serve inference to users as fast as possible. We created our platform to be as general as possible, but we also identified niche use cases that fit our infrastructure well, such as low-latency image generation or real-time audio voice agents. To lean in on those use cases, we’re bringing on some new models that will help make it easier to develop for these applications.

Today, we’re excited to announce that we are expanding our model catalog to include closed-source partner models that fit this use case. We’ve partnered with Leonardo.Ai and Deepgram to bring their latest and greatest models to Workers AI, hosted on Cloudflare’s infrastructure. Leonardo and Deepgram both have models with a great speed-to-performance ratio that suit the infrastructure of Workers AI. We’re starting off with these great partners — but expect to expand our catalog to other partner models as well.

The benefits of using these models on Workers AI is that we don’t only have a standalone inference Continue reading

How Cloudflare runs more AI models on fewer GPUs: A technical deep-dive

As the demand for AI products grows, developers are creating and tuning a wider variety of models. While adding new models to our growing catalog on Workers AI, we noticed that not all of them are used equally – leaving infrequently used models occupying valuable GPU space. Efficiency is a core value at Cloudflare, and with GPUs being the scarce commodity they are, we realized that we needed to build something to fully maximize our GPU usage.

Omni is an internal platform we’ve built for running and managing AI models on Cloudflare’s edge nodes. It does so by spawning and managing multiple models on a single machine and GPU using lightweight isolation. Omni makes it easy and efficient to run many small and/or low-volume models, combining multiple capabilities by:  

  • Spawning multiple models from a single control plane,

  • Implementing lightweight process isolation, allowing models to spin up and down quickly,

  • Isolating the file system between models to easily manage per-model dependencies, and

  • Over-committing GPU memory to run more models on a single GPU.

Cloudflare aims to place GPUs as close as we possibly can to people and applications that are using them. With Omni in place, we’re now able to run Continue reading

How we built the most efficient inference engine for Cloudflare’s network

Inference powers some of today’s most powerful AI products: chat bot replies, AI agents, autonomous vehicle decisions, and fraud detection. The problem is, if you’re building one of these products on top of a hyperscaler, you’ll likely need to rent expensive GPUs from large centralized data centers to run your inference tasks. That model doesn’t work for Cloudflare — there’s a mismatch between Cloudflare’s globally-distributed network and a typical centralized AI deployment using large multi-GPU nodes. As a company that operates our own compute on a lean, fast, and widely distributed network within 50ms of 95% of the world’s Internet-connected population, we need to be running inference tasks more efficiently than anywhere else.

This is further compounded by the fact that AI models are getting larger and more complex. As we started to support these models, like the Llama 4 herd and gpt-oss, we realized that we couldn’t just throw money at the scaling problems by buying more GPUs. We needed to utilize every bit of idle capacity and be agile with where each model is deployed. 

After running most of our models on the widely used open source inference and serving engine vLLM, we figured out it Continue reading

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