Over the past weeks we ran a simple internal test: take two identical Go backends, put them through Claude Code (AWS Bedrock) and Cursor, and see what actually happens.
Same prompts, same tasks, same engineers.
Very quickly it became obvious that choosing “an AI tool” is the wrong question.
The model strength barely matters anymore.
The workflow around the model determines delivery speed, token usage, stability, and whether your team will even adopt it.
The conclusion is almost boring in its simplicity:
AI tools are not interchangeable, even if the models are equally strong.
There’s this persistent idea in tech leadership:
“If the model is strong enough, the workflow will follow.”
It doesn’t. Our test showed the opposite.
When code quality is more or less the same, everything else suddenly becomes the real bottleneck. Latency grows quietly until a simple task takes 10–15 minutes. Token consumption explodes when nobody is watching. Terminal-only workflows slow junior and mid-level engineers. And tools optimised for speed rarely satisfy enterprise security teams.
These are the things that never show up in a demo. They show up the moment you actually try to ship something.
Across identical Go projects:
Both delivered high-quality, production-ready code. But they serve very different realities inside an organisation.
Two projects — a full e-commerce API and a pricing module — gave us consistent results.
Cursor is simply faster. It sits in your IDE, doesn’t wait for AWS infrastructure, and delivers immediate feedback. Claude Code, through Bedrock, introduced long waits. Not seconds. Minutes.
The pricing difference is even bigger. Claude burned through millions of cached tokens in a single flow. Cursor didn’t.
Development is rhythm. Cursor keeps the rhythm high. Claude slows it down.
Both tools deliver clean, structured Go code. But they encourage different habits.
Claude Code feels engineered for long-term systems: predictable structures, strong patterns, easy to audit.
Cursor feels like working with a fast senior engineer who doesn’t overthink. Leaner output, easy to iterate, great for rapid cycles.
If your culture relies on strict reviews, Claude is more comfortable.
If your culture thrives on speed, Cursor wins.
Cursor feels natural for teams that live inside the IDE. Diff views, instant feedback, minimal friction. It’s easy to onboard junior engineers and keep momentum high.
Claude Code is better suited for teams with strong terminal fluency. Senior engineers handle it well. Juniors will struggle more.
In other words: your team’s habits decide whether Claude is a fit, not the other way around.
If you're in banking, healthcare, government, or anything with sensitive data, the choice becomes almost automatic.
Claude Code sits inside AWS. You get PrivateLink, VPC boundaries, SOC2, HIPAA, FedRAMP pathways, versioned models, zero public exposure. For most enterprises, this is the only acceptable setup.
Cursor’s architecture is cloud-hosted and dynamic by design. It’s great for fast work, but it’s not built for regulated environments.
Perfect for product teams and startups.
Perfect for mature SMEs and enterprises.
This entire evaluation reaffirms something we see in every AI transformation project:
The model is never the differentiator.
The architecture and workflow around it are.
If you align your tools, infrastructure, and teams properly, you get predictable delivery, lower long-term cost, fewer surprises, and a workflow your engineers actually enjoy. If you skip this thinking, you accumulate operational debt before your AI initiative even launches.
We design and implement secure, efficient, and scalable AI engineering environments — from Bedrock deployments to private LLM stacks and fast prototyping pipelines.
If you want to understand which tools fit your organisation:
👉 Book a short discovery call here, and we’ll map the right architecture for you
