CloudAI Fusion

Cloud-Native AI Unified Management Platform

Honest by design: it refuses to pretend a fake backend is real.

CloudAI Fusion unifies cloud-native infrastructure management with AI-assisted GPU scheduling across multiple clouds. It is built around one principle that most "platform" projects ignore: a component is either backed by a real dependency, or it says so — and in production it refuses to run on a simulation.

Why this project is different: Run Modes & Capability Transparency

Every external-dependency boundary (cache, messaging, consensus, GitOps, scheduling, cloud, DB, evidence) reports whether it is running on a real backend or a simulated in-memory fallback. A single run_mode setting governs what is allowed:

• GET /api/v1/capabilities returns, per subsystem, real vs simulated + the active run mode.
• /readyz reports simulated backends and fails readiness in production.
• At startup, capability.Enforce() aborts a production boot if any subsystem is simulated.

// GET /api/v1/capabilities  (run_mode=degraded, no infra attached)
{
  "run_mode": "degraded",
  "all_real": false,
  "simulated_count": 1,
  "backends": [
    {"component": "messaging.producer", "mode": "simulated", "driver": "memory",
     "detail": "nats backend requested but server unreachable"}
  ]
}

In production, that same state makes the API server exit 1 at boot instead of serving fake data. This is the core guarantee of the platform.

What is real vs. simulated

Real drivers are used automatically when the dependency is reachable; otherwise the component falls back to an in-memory simulation and reports it (allowed only outside production).

Real Flux reconcile-status reads, cross-cluster failover, and hashicorp/raft consensus are now implemented and integration-tested against kind (run with -tags integration). What remains gated on external resources (and therefore reported simulated until configured): real multi-cloud SDK calls (credentials), red-team tools (the binaries), a live LLM endpoint, etcd election. Progress is measured objectively by /api/v1/capabilities, not by marketing claims.

Key Features

Core Advantages (Benchmark-Backed Moats)

Unlike "platform" projects that ship glue code, CloudAI Fusion's differentiation is verifiable in code and reproducible benchmarks. Every number below comes from real go test -bench runs (Intel Core Ultra 9, windows/amd64, Go 1.25.7); reproduce with go test ./pkg/<pkg> -bench=. -benchmem -run='^$'. Where no independent-competitor benchmark exists, we say so instead of inventing one.

Honesty note: some subsystems (messaging drivers, adapters, standard state machines) are solid engineering without a unique algorithmic moat — we label those as such rather than inflating them. Hardware-bound modules (real GPU topology/MIG, CRIU migration, SGX/eBPF capability probing) require physical hardware and are marked accordingly. M53 GPU WASI Extensions is NOT hardware-bound: runs on pure-Go wazero interpreter with simulated GPU service, no physical GPU required.

Baselines labelled "vs X" compare against our reimplementation of X's documented semantics unless marked "(head-to-head)". Only FastTracer vs OpenTelemetry SDK is a true head-to-head (real third-party library imported and benchmarked in-process).

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                          CloudAI Fusion                              │
├─────────────┬──────────────┬─────────────┬─────────────────────────┤
│  API Server │  Scheduler   │    Agent    │      AI Engine          │
│   (Go/Gin)  │ (GPU-aware)  │ (DaemonSet) │   (Python/FastAPI)     │
├─────────────┴──────────────┴─────────────┴─────────────────────────┤
│  runmode + capability registry (real-vs-simulated policy & report)  │
├───────┬───────┬─────────┬──────────┬─────────┬──────┬──────┬───────┤
│  Auth │ Cloud │ Cluster │ Security │ Monitor │ Mesh │ Wasm │ Edge  │
├───────┴───────┴─────────┴──────────┴─────────┴──────┴──────┴───────┤
│  PostgreSQL │ Redis │ Kafka │ NATS │ Kubernetes │ Prometheus         │
└─────────────────────────────────────────────────────────────────────┘

A cross-cutting Verifiable Control Plane (pkg/evidence) signs every consequential action into a hash-chained, Merkle-transparency-logged ledger; the Verifiable AI Red Team (pkg/redteam) runs authorized, evidence-grade security validation on top of it.

Plugin Ecosystem

CloudAI Fusion provides a Kubernetes Scheduler Framework-style plugin system with 9 extension points. The pkg/plugin/contrib/ package ships 9 production-ready plugins across 3 domains: