xmpuspus

xmpuspus / cloudwright

Public

Architecture intelligence for cloud engineers

12
1
100% credibility
Found Mar 04, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Cloudwright is an open-source tool that turns plain English descriptions of cloud systems into structured designs, cost breakdowns, compliance reports, diagrams, and deployable infrastructure code.

How It Works

1
👋 Discover Cloudwright

You hear about Cloudwright from a colleague who uses it to quickly plan cloud setups without drawing diagrams by hand.

2
📦 Get it set up

With one simple command, you add Cloudwright to your computer so it's ready to use anytime.

3
🧠 Connect a smart helper

You link it to a thinking service so it can understand your ideas and create plans.

4
💭 Describe your idea

You type a simple sentence like 'a website with user logins and a database' and press enter.

5
✨ See your full plan

Instantly, you get a clear picture of your cloud setup, including costs, safety checks, and pretty diagrams.

6
📤 Turn it into action files

One click creates ready-to-use building blocks and code to make your cloud real.

✅ Your cloud is planned!

Now you have everything needed to build, check, and launch your project confidently.

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Star Growth

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AI-Generated Review

What is cloudwright?

Cloudwright is a Python CLI for AI architecture intelligence that turns natural language descriptions into deployable cloud designs across AWS, GCP, Azure, and Databricks. You describe a system like "HIPAA-compliant healthcare API," and it generates a YAML ArchSpec with components, connections, cost breakdowns, compliance scores for HIPAA/PCI/SOC2/GDPR, and exports to Terraform, CloudFormation, Mermaid diagrams, or SBOMs. It fills the design-phase gap before IaC, handling imports from TF states, diffs, drift detection, and multi-cloud comparisons.

Why is it gaining traction?

Unlike IaC tools like Terraform or cost scanners like Infracost that kick in post-design, cloudwright does design-time validation, blast radius analysis, and IaC generation from one spec—no tribal knowledge or spreadsheets needed. Benchmarks crush raw LLMs (68% vs 28% overall), with reproducible outputs, policy engines, and a catalog for instance pricing/equivalences. Devs hook on the chat mode for iterative tweaks and one-command pipelines from prompt to deployable infra.

Who should use this?

Cloud engineers sketching enterprise architecture intelligence for compliance-heavy workloads like fintech PCI or healthcare HIPAA. Architects generating architecture decision records github-style or comparing github architecture patterns with python across clouds. Teams doing cloud migrations, rightsizing, or greenfield MVPs needing quick cost/lint/score before committing code.

Verdict

Grab it for prototyping cloud architecture—solid docs, 1000+ tests, and PyPI-ready CLI make it instantly usable despite 12 stars and 1.0% credibility signaling early days. Maturity lags on edge imports, but benchmarks prove it's lightyears ahead of chatty LLMs; watch for v1.0 stability.

(198 words)

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