Cloudflare’s Edge AI Infrastructure Playbook

In Building AI Infra at Cloudflare (Oct 2025), Dane Knecht from Cloudflare shares how their edge platform evolved into a global AI infrastructure layer. Source: https://www.youtube.com/watch?v=EIV2QlZfqWw.

In my work deploying agentic AI at scale, I’ve seen that opinionated primitives often beat “flexible” abstractions for speed, security, and cost control.

Main Story

Cloudflare’s journey started with Workers—an internal compute primitive designed to accelerate experiments without the overhead of containers. The aim was simple: secure, cost‑efficient, globally distributed compute that could scale to millions. Early success in zero‑trust products led to its release for customers.

"When we start at the top and for the enterprise those features are usually a mistake… we built the best products when we start with the software customers." — Dane Knecht, Cloudflare

From Workers came a series of incremental primitives: Workers KV for basic state, Durable Objects for flexible stateful workloads, R2 for blob storage, and D1 for lightweight databases. Each addition was built bottom‑up, tested with free‑tier users, and then scaled upmarket. Containers were later added to meet developers “where they are” while keeping strong defaults.

Durable Objects became the backbone for the Agents SDK. This gave developers a simple way to build long‑lived, composable AI agents with durable workflows—critical for multi‑step AI tasks. “Code mode” extended flexibility further, letting agents generate and execute bespoke code instead of relying on static tools.

Our secret really is utilization… any given point, half the world's asleep… there’s no real cost for us to fill them up. — Dane Knecht, Cloudflare

Cost efficiency is engineered into the model: charging only for CPU time, maximizing utilization with global scheduling, and routing workloads to idle capacity. GPU utilization jumped from ~30% to over 75% through tailored routing.

Technical Considerations

• Stateful serverless with Durable Objects enables per‑user/session state without complex orchestration
• Opinionated defaults reduce developer error and security risk in distributed deployments
• Global scheduling mitigates idle capacity waste and smooths demand spikes
• Incremental primitives allow modular adoption without full platform migration
• “Code mode” enables dynamic tool creation for agents, increasing flexibility

Business Impact & Strategy

• Bottom‑up development lowers feature risk by validating with free users before enterprise rollout
• Utilization optimization cuts infrastructure cost while supporting freemium tiers
• Modular primitives reduce time‑to‑value for new workloads
• Opinionated frameworks improve developer onboarding and retention
• Edge‑native architecture supports compliance with localized data residency

Key Insights

• Start small: validate features with grassroots users before scaling
• Opinionated primitives speed development and improve reliability
• Stateful serverless unlocks complex workflows without heavyweight stacks
• Utilization optimization is a major cost lever in global infrastructure
• Dynamic tool generation increases agent versatility
• Meeting developers in familiar paradigms accelerates adoption

Why It Matters

Cloudflare’s approach shows that building AI infrastructure at edge scale is less about raw compute and more about orchestration, utilization, and developer experience. Opinionated primitives, tested bottom‑up, create a foundation that scales without collapsing under complexity.

For technical teams, this means focusing on stateful serverless patterns and utilization optimization rather than chasing every new runtime. For business leaders, it’s a case study in aligning cost models with product strategy—engineering and pricing working hand in hand.

Actionable Playbook

• Prototype with free‑tier users: Validate demand and refine UX before scaling; track early adoption rates
• Adopt stateful serverless: Use Durable Objects for per‑user/session state; measure latency and data residency compliance
• Integrate durable workflows: Implement multi‑step processes for AI tasks; track completion success rates
• Schedule workloads to idle regions: Route batch jobs to low‑utilization zones; monitor utilization gains
• Experiment with dynamic tool generation: Enable agents to create code on demand; measure task coverage increase

Conclusion

Cloudflare’s edge AI infrastructure is built on a clear sequence: start small, add opinionated primitives, optimize utilization globally. It’s a pragmatic path that balances developer agility with cost discipline.

Questions or feedback? Reach out—and dive deeper by watching the full discussion here: https://www.youtube.com/watch?v=EIV2QlZfqWw.