Most AI dev tools focus on code. Cloudwright focuses on the design phase — the gap where engineers currently use spreadsheets, ad-hoc Terraform, and tribal knowledge.

You describe an architecture in plain English. It produces a structured YAML spec (ArchSpec), then gives you:

- Compliance validation: HIPAA, PCI-DSS, SOC 2, FedRAMP, GDPR, Well-Architected - Per-component cost estimates across AWS, GCP, Azure, and Databricks - Terraform/CloudFormation/Mermaid/SBOM export - Drift detection (compare design vs deployed tfstate) - Security scanning (missing encryption, IAM wildcards, open 0.0.0.0/0) - Architecture Decision Record (ADR) generation

Benchmarked against raw Claude Sonnet 4.6 across 54 use cases: cloudwright wins on 6 of 8 metrics. Weakest areas are cost accuracy and import fidelity (both actively improving).

  $ pip install cloudwright-ai[cli]
  $ cloudwright design "3-tier web app on AWS with Redis and RDS PostgreSQL"
  $ cloudwright validate spec.yaml --compliance hipaa
  $ cloudwright export spec.yaml --format terraform -o ./infra
  $ cloudwright security spec.yaml

112 services across 4 providers. 17 starter templates. Pure Python, MIT licensed, no cloud credentials required for design/validate/export.

The Databricks provider was the hardest to build — it's an overlay platform (runs on top of AWS/Azure), uses DBU-based pricing instead of per-hour instances, and has no CloudFormation support. Happy to talk through any of the design tradeoffs.