AI Governance Gateway for LLM & MCP Traffic
One controlled plane for all AI and MCP traffic. Enforce access policies, security, budgets, quotas, compliance controls, custom workflows, and routing before requests reach providers or tools.
AI adoption scales faster than AI control.
Direct provider access creates unmanaged spend, scattered keys, inconsistent model access, weak auditability, and new agent/tool security risks.
Shared credentials
Provider keys spread across apps, agents, notebooks, and CI jobs. Nobody has a reliable access boundary.
Costs without owners
Token spend appears after the fact. Teams cannot stop runaway jobs or attribute usage cleanly.
Agents with broad tool access
MCP tools can reach sensitive systems, but access rules and audit evidence remain inconsistent.
Security logic scattered
Prompt safety, DLP, rate limits, and custom checks are reimplemented differently by every team.
One request path. Every control in place.
Every LLM or MCP request passes through the same governance lifecycle. Blocked before upstream on access, safety, budget, or compliance failures.
Authenticate
Validate virtual API key
Authorize
Check model/MCP access
Inspect
Apply security policies
Reserve
Reserve budget
Route
Route to provider
Record
Emit usage records
{
"request_id": "req_9f4c21b8",
"key": "vk_prod_web_***",
"model": "openai:gpt-4.1",
"status": "blocked",
"blocked_at": "budget.reserve",
"reason": "Monthly budget exceeded",
"requested_budget_usd": 0.25,
"remaining_budget_usd": 0,
"latency_ms": 12
}