# Jeremylongshore Claude Code Plugins Plus Skills Vastai Enterprise Rbac

> Vast.ai Enterprise RBAC

- Skill: `tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-enter` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-enter`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-enter/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-enter

---

# 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["api_key_env"]])

    def can_provision(self, gpu_name, num_gpus=1):
        """Check if provisioning is allowed by team policy."""
        if gpu_name not in self.config["gpu_whitelist"]:
            return False, f"GPU {gpu_name} not in team whitelist"

        running = len([i for i in self.client.show_instances()
                      if i.get("actual_status") == "running"])
        if running >= self.config["max_instances"]:
            return False, f"Instance limit reached ({running}/{self.config['max_instances']})"

        return True, "OK"

    def provision_with_policy(self, gpu_name, image, disk_gb=20):
        allowed, reason = self.can_provision(gpu_name)
        if not allowed:
            raise PermissionError(f"Policy violation: {reason}")

        offers = self.client.search_offers({
            "gpu_name": {"eq": gpu_name},
            "dph_total": {"lte": self.config["max_dph"]},
            "reliability2": {"gte": 0.95},
            "rentable": {"eq": True},
        })
        if not offers.get("offers"):
            raise RuntimeError("No offers matching policy constraints")

        return self.client.create_instance(
            offers["offers"][0]["id"], image, disk_gb)
```

### Step 3: Audit Logging

```python
import json, datetime

class AuditLogger:
    def __init__(self, log_file="vast_audit.jsonl"):
        self.log_file = log_file

    def log(self, team, action, details):
        entry = {
            "timestamp": datetime.datetime.utcnow().isoformat(),
            "team": team,
            "action": action,
            **details,
        }
        with open(self.log_file, "a") as f:
            f.write(json.dumps(entry) + "\n")

# Usage
audit = AuditLogger()
audit.log("ml-research", "provision", {
    "gpu": "A100", "offer_id": 12345, "dph": 1.50})
audit.log("ml-research", "destroy", {
    "instance_id": 67890, "duration_hours": 4.2, "total_cost": 6.30})
```

### Step 4: Spending Reports

```python
def team_spending_report(audit_file="vast_audit.jsonl"):
    """Generate spending report from audit log."""
    import json
    costs = {}
    with open(audit_file) as f:
        for line in f:
            entry = json.loads(line)
            if entry["action"] == "destroy" and "total_cost" in entry:
                team = entry["team"]
                costs.setdefault(team, 0)
                costs[team] += entry["total_cost"]

    print("Team Spending Report:")
    for team, cost in sorted(costs.items(), key=lambda x: -x[1]):
        print(f"  {team}: ${cost:.2f}")
```

## Output
- Team-specific API key configuration
- Policy enforcement layer (GPU whitelist, instance limits, budget caps)
- Audit logging for all provisioning and destruction events
- Spending reports per team

## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| Policy violation on provision | GPU not in whitelist or limit reached | Request policy change or destroy idle instances |
| Budget exceeded | Team exceeded daily limit | Alert team lead; pause provisioning until next day |
| Missing API key | Environment variable not set | Configure key in secrets manager |
| Audit log missing entries | Logger not wired into all operations | Audit the code paths for missing log calls |

## Resources
- [Vast.ai Account](https://cloud.vast.ai)
- [REST API](https://vast.ai/developers/api)

## Next Steps
For migration strategies, see `vastai-migration-deep-dive`.

## Examples

**Team onboarding**: Create a new team config entry with conservative limits (2 instances, RTX 4090 only, $10/day). Increase limits after the team demonstrates responsible usage.

**Monthly chargeback**: Parse the audit log to generate per-team invoices for internal cost allocation.

---
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<!-- tomevault:4.0:skill_md:2026-04-11 -->