How we built Cloudflare's data platform and an AI agent on top of it
**TL;DR:** How we built Cloudflare's data platform and an AI agent on top of it
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What we know
Cloudflare processes more than a billion events every second. Our network spans 330+ cities in 120+ countries. Behind every HTTP request, every Worker invocation, every R2 read operation, there is data, and a lot of it. For years, that data was not very easy to access. It lived in dozens of production databases, ClickHouse clusters, Kafka streams, Google Cloud buckets, BigQuery datasets, and a long tail of pipelines.
", an analyst at Cloudflare had to know which system to ask, what credentials to use, what query language to write, and whether the data they were looking at was sampled, fresh, or seven-days stale. As a result, it was difficult to glean informed insights from the data. To solve this problem, we built two in-house tools: Town Lake, Cloudflare's unified data analytics platform, and Skipper, an AI data agent that runs on top of it.
Town Lake is a single SQL interface to everything Cloudflare knows, and Skipper is how anyone at Cloudflare can ask questions in plain English and get correct, auditable answers back in seconds. This is the story of how we built both. The shap
Source: Cloudflare Blog
Context
AI coverage on iByte separates shipped capability from roadmap talk. The practical lens is cost, access, safety, and what changes for builders and everyday users.
Why this matters
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What to watch next
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Practical takeaways
1) Separate the announcement from the shipping date. 2) Compare alternatives if pricing or terms shift. 3) Revisit the story when independent verification lands.
FAQ
**Q: Is everything in this article confirmed?** A: The summary reflects publicly reported information at publication time. Analysis sections are clearly framed as context, not new reporting.
**Q: Will iByte update this page?** A: Yes. As primary sources publish more detail, this article can be refreshed without changing the URL.
Last updated: June 16, 2026.
Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.