# QQ3221197721/cloudai-fusion

Repository: https://github.com/QQ3221197721/cloudai-fusion
Canonical: https://ross.abutalabs.com/products/cloudai-fusion
Language: Go
License: NOASSERTION
License Family: other
Last push: 2026-08-23T15:48:03+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 12
- inputs: {"age_days": 174, "days_push": 10, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1115, forks 50 (observed 2026-08-28T04:03:38.295673+00:00)

## What it is
CloudAI Fusion is a Go-based cloud-native platform that unifies multi-cloud infrastructure management with AI-assisted GPU scheduling across Kubernetes clusters. Its distinguishing feature is capability transparency: every subsystem reports whether it runs on a real backend or a simulated in-memory fallback, and production mode refuses to boot on simulated components.

## Use cases
- manage kubernetes clusters across multiple clouds from one platform
- schedule ai gpu workloads across cloud providers
- prevent services from silently running on fake or simulated backends in production
- self-host a cloud management console with real postgres redis nats backends
- monitor which subsystems are degraded via a capabilities endpoint
- run local development with in-memory fallbacks and enforce real infra in production

## When to choose
- you need multi-cloud kubernetes and GPU scheduling with honest backend reporting
- you want a platform that fails fast instead of serving simulated data in production
- you run staging/dev with graceful degradation to in-memory fallbacks

## When to avoid
- you need a lightweight single-purpose tool rather than a full platform
- you cannot run real dependencies like PostgreSQL, Redis, NATS, or Kubernetes
- you need a mature enterprise product with long-term vendor support

## Facets
- artifact type: service
- maturity: active
- function: cloud, container-orchestration, monitoring, scheduling, api-framework, http-server, caching, message-queue, database, llm-inference
- domain: cloud-computing, infrastructure-as-code, artificial-intelligence, microservices, self-hosted
- platform: go, cloud, self-hosted, cli
- tags: multi-cloud, gpu-scheduling, capability-transparency, run-modes, simulation-fallback, leader-election, gitops, kubernetes-operator, devops, containers, linux, docker, kubernetes

## Member repositories
- QQ3221197721/cloudai-fusion (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:38.295673+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:42:09.319933+00:00, confidence not recorded.
  - readme: https://github.com/QQ3221197721/cloudai-fusion (fetched 2026-08-28T04:03:38.295673+00:00, sha 413b1cb09fb9)
- Data as of 2026-08-30T08:39:29.467469+00:00.