Compose-to-Cloud Pulumi Providers for AWS, GCP, and Azure
Details
- External ID
- 48239954
- Source
- HN
- Company
- —
- Product
- Compose-to-Cloud Pulumi Providers for AWS, GCP, and Azure
- Website domain
- github.com
- Launched
- May 22, 2026
- Cohort
- —
- Upvotes
- 5
- Upvotes percentile
- 0.1147011308562197
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Hi HN,These are three FOSS Pulumi providers (defang-aws, defang-gcp, defang-azure) that take a Docker Compose-like project and provision it as real infrastructure to the respective cloud: VPC, subnets, load balancers, serverless containers (ECS Fargate, Cloud Run, Container App), builds, managed PostgreSQL, Redis, LLMs, and public/private DNS.Develop once, deploy anywhere: all three providers expose the same Compose-shaped surface. They contain the same Pulumi resources: Project, Build, Service, Postgres, Redis, LLM. Each service in the Compose services map gets provisioned as one of those resources. Moving between clouds should be as simple as changing the import and nothing else. The Project resource sets up private DNS for service discovery, so Compose services can connect using their Compose service names.These providers have been rewritten from scratch in Go and are based on the (internal) TypeScript Pulumi code we've been working on for the last 2 years. We haven't reached parity quite yet, but we encourage everybody to use them and share your feedback.The Pulumi tooling generates SDKs for TypeScript, Python, Go, and C#. The repo has examples for all clouds in all those languages, eg.: const app = new defang_aws.Project("demo", { services: { web: { image: "nginx:latest", ports: [{ target: 80, mode: "ingress", appProtocol: "http" }], }, }, }); export const endpoints = app.endpoints; A few notes:- The Compose surfaces is opinionated. We check the service requirements and map it to a concrete cloud resource. For example, on GCP a service without exposed ports would run on Compose Engine, not Cloud Run.- Portability is at the Compose level. Each cloud's behaviour differs: IAM, native containers, etc. The API is the same, but the materialization isn't.- The Project resource owns the shared infra (VPC, build pipeline, DNS, LB). The standalone Service, Postgres, Redis resources skip that, so the Service needs a built image.- Licensing is dual: the provider engine is AGPL-3.0, because we want forks to contribute back to the community. The generated SDKs are Apache-2.0, so no copyleft in your own Pulumi code.- We're waiting for Pulumi devs to merge our registry PR, so the registry still reflects the old v1 provider docs.- There's support for runtime "config", ie. parameters, using a ConfigProvider interface, which also support interpolation.Repo: https://github.com/DefangLabs/pulumi-defangHappy to dig into how the Compose translation works, where the API diverges between clouds (and why), why Pulumi over CDK/Terraform, or the cloud-specific tradeoffs (AWS uses ECS+ALB, GCP uses Cloud Run with optional CE fallback, Azure uses Container Apps).- Lio
Enrichment
- Theme
- Vertical
- Horizontal
- Function
- Dev tools
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- infrastructure as code for cloud providers
- Manually corrected
- False
Could you build this?
Partial Parsing Docker Compose files into Pulumi component abstractions is vibe-codeable, but building production-grade multi-cloud infrastructure providers requires deep cloud architecture and DevOps expertise.
What it would actually take: A robust implementation uses Go or TypeScript with the Pulumi Provider SDK to compile Docker Compose abstractions into declarative cloud resources across AWS (ECS/ALB/VPC/RDS), GCP (Cloud Run/Cloud SQL), and Azure (Container Apps). The hard parts are managing robust VPC networking, IAM role least-privilege generation, zero-downtime updates, state drift, and edge-case lifecycle semantics across three distinct hyperscalers.
Discussion
2 comments analyzed.
Feature requests: More examples for deploying AI agents
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Launched 204 days after the earliest competitor.
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Other launches for this product
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