vastai-enterprise-rbac

# Vast.ai Enterprise RBAC ## Overview Control access to Vast.ai GPU instances and spending through API key management, team-level budgets, and GPU allocation policies. Vast.ai uses a marketplace model with per-GPU-hour pricing (RTX 4090 ~$0.20/hr, A100 ~$1.50/hr, H100 ~$3.00/hr). ## Prerequisites - Vast.ai account(s) with API keys - Understanding of team GPU usage patterns - Budget allocation per team/project ## Instructions ### Step 1: Team API Key Strategy ```python # Separate API keys per team for billing isolation # Option A: Separate Vast.ai accounts per team # Option B: Single account with application-level controls TEAM_CONFIGS = { "ml-research": { "api_key_env": "VASTAI_KEY_RESEARCH", "gpu_whitelist": ["A100", "H100_SXM"], "max_instances": 8, "daily_budget": 200.00, "max_dph": 4.00, }, "ml-engineering": { "api_key_env": "VASTAI_KEY_ENGINEERING", "gpu_whitelist": ["RTX_4090", "A100"], "max_instances": 4, "daily_budget": 50.00, "max_dph": 2.00, }, "data-science": { "api_key_env": "VASTAI_KEY_DATASCIENCE", "gpu_whitelist": ["RTX_4090", "RTX_3090"], "max_instances": 2, "daily_budget": 10.00, "max_dph": 0.30, }, } ``` ### Step 2: Policy Enforcement Layer ```python class VastPolicyEnforcer: def __init__(self, team_config): self.config = team_config self.client = VastClient(api_key=os.environ[team_config["...