From one GPU to
the whole rack.

Python

import fantasti

fx = fantasti.Client()                          # reads FANTASTI_API_KEY

cluster = fx.clusters.create(
    name="acme-train",
    gpu="H200:8",                               # 8-GPU nodes
    nodes=2,                                    # placed on one InfiniBand fabric
    capacity=fantasti.Spot(max_price=3.10),     # your ceiling, USD per GPU-hour
    interfaces=["kubernetes", "skypilot"],
)
cluster.wait("ready")
cluster.kubeconfig.save("~/.kube/fantasti-acme.yaml")

CLI

# create the cluster
$ fantasti cluster create acme-train --gpu H200:8 --nodes 2 --spot --max-price 3.10

# read why it was placed where it was
$ fantasti explain cl_8f2k

MCP

{
  "mcpServers": {
    "fantasti": { "type": "http", "url": "https://mcp.fantasti.ai/mcp" }
  }
}

catalog      gpus_list
sandboxes    sandboxes_create  sandboxes_exec  sandboxes_read_file
             sandboxes_write_file  sandboxes_delete
workspaces   workspaces_list  workspaces_start  workspaces_stop
jobs         jobs_create  jobs_logs
usage        usage_get

REST

curl -X POST https://api.fantasti.ai/v1/clusters \
  -H "Authorization: Bearer $FANTASTI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "name": "acme-train",
        "gpu": "H200:8",
        "nodes": 2,
        "capacity": { "mode": "spot", "max_price": "3.10", "currency": "USD", "unit": "gpu_hour" },
        "interfaces": ["kubernetes", "skypilot"]
      }'
# { "id": "cl_8f2k…", "status": "placing", "fabric": "single" }

YAML

# policy.yaml · applies to every request from team "research"
team: research
placement:
  gpus: [H100, H200, B200]
  fabric: infiniband            # multi-node jobs stay on one fabric
  regions: ["us-*"]
capacity:
  allow: [on_demand, spot, reserved]
  spot:
    max_price: { H100: 2.40, H200: 3.10 }   # USD per GPU-hour, your ceilings
limits:
  gpus: { H200: 32 }
  sandboxes: { concurrent: 200, max_ttl: 60m }
  spend: { monthly_usd: 40000 }
cost_center: research-llm