Why Datsugi (often) builds AI systems on Cloudflare

When most companies think about deploying AI systems, they default to AWS, Azure, or Google Cloud. These are solid platforms — but they come with complexity, unpredictable costs, and infrastructure overhead that smaller teams struggle to manage.

At Datsugi, we've been building AI-powered systems on Cloudflare Workers for the past year. The results have consistently surprised us — and our clients.

The problem with traditional cloud providers

AWS and Azure are powerful, but they're built for scale that most mid-market companies don't need. You end up paying for flexibility you won't use, managing services you don't fully understand, and watching your monthly bill climb in ways that are hard to predict.

A typical AI deployment on AWS might involve Lambda for compute, S3 for storage, SQS for queues, API Gateway for routing, CloudWatch for logs, and IAM for permissions. Each service has its own pricing model, its own configuration, and its own failure modes. Before you've written any business logic, you're already managing six different systems.

For companies that need enterprise-grade infrastructure and have dedicated DevOps teams, this complexity is manageable. For everyone else, it's a tax on every project.

What Cloudflare Workers does differently

Cloudflare Workers takes a different approach.

Instead of assembling infrastructure from dozens of services, you get a unified platform where compute, storage, databases, queues, and AI inference all work together natively.

Need a database? Cloudflare D1 is built in.

Object storage? R2 is there.

Key-value store for caching? KV is ready.

Vector database for RAG systems? Vectorize handles it.

Scheduled tasks? Cron triggers are native.

And with Workers AI, you can run inference on models like Llama directly at the edge — no external API calls required. The architecture is simpler. The deployment is faster.

And the pricing is predictable.

Real cost savings for real projects

One example: we built a MySQL-to-HubSpot sync pipeline for a client that processes tens of thousands of records. The entire infrastructure — scheduled triggers, database queries via Hyperdrive, data transformation, and API calls to HubSpot — runs for under $25 per month.

On AWS, the same system would involve Lambda invocations, RDS proxy connections, CloudWatch logs, and API Gateway requests. The architecture would be more complex, debugging would take longer, and costs would be at least 3-5x higher.

For AI workloads specifically, Cloudflare's edge network means lower latency for users worldwide. When you're building document processing pipelines or RAG systems that need to respond quickly, that matters.

What this means for our clients

We're not anti-AWS or anti-Azure. For certain workloads — especially those requiring specialized services or extreme scale — they're still the right choice.

But for most AI and data projects we deliver, Cloudflare Workers offers a better balance: lower costs, simpler architecture, faster time-to-production, and infrastructure that doesn't require a dedicated team to manage.

Our clients get AI systems that work in production — not proofs of concept that need six months of infrastructure work before they're usable.

That's the competitive advantage we're passing on.

Bottom line

Cloudflare Workers won't replace AWS for everyone. But for companies that want AI systems deployed quickly, reliably, and affordably — it's become our platform of choice.