What AI
Gateways Control

If the gateway is the front door, this is what happens once traffic passes through it. An AI Gateway is not just a routing layer. It is the enforcement point for the policies, identity controls, cost constraints, and architectural decisions that define how AI operates inside an enterprise.

Guardrails only work if applied consistently. In practice, teams implement them differently or forget them entirely. A gateway removes that variability by enforcing guardrails centrally and automatically on every request.

• Policies bound directly to virtual keys per user/app.
• Developers inherit correct rules by default.
• Eliminates direct provider bypass risk.

Gateways enable granular control over the AI lifecycle. Guardrails can vary by Team (Finance vs. Marketing), Model type (experimental vs. production), and Environment.

• Pre-request: Injection defense & sensitive-data stripping.
• Post-response: Toxicity filtering & hallucination detection.
• Compliance: Enforcing allowed-domain and schema rules.

Enterprise AI must align with existing identity systems. Gateways treat SSO as the source of truth. Model access, tool permissions, and budgets are issued based on roles, groups, or attributes defined in the identity provider.

• Team-based permissions.
• Model-level restrictions.
• Budget enforcement per user or group.
• Immediate revocation when roles change.

Provider API keys grant broad account access; if leaked, they expose the environment. Gateways centralize real keys and issue virtual keys restricted by Model, Provider, and Team.

• Limited Radius: Blast radius is strictly restricted.
• Traceability: Every leak is traceable to its source.
• Simple Rotation: Only the virtual key must be rotated.

In a multi-model environment, model selection becomes policy-driven. Gateways route requests dynamically based on Latency, Cost, Capability, Region, and Availability.

• Canary testing and traffic splitting.
• Reserve premium models for specific teams.
• Maintain regional compliance constraints.
• Automatic retries on provider failure.

Without a gateway, every provider integration is different. With a gateway, developers integrate once. The gateway translates requests and normalizes responses across vendors.

• Unified Message structure and Tool calls.
• Normalized Error handling across vendors.
• Standardized Token usage reporting.

Enterprise AI requires visibility into how requests flow and why decisions are made. Gateways provide end-to-end telemetry on model selection, guardrails, and token usage.

• Supports Incident response and performance tuning.
• Enables Compliance audits and cost optimization.
• Identity-tracked request and tool attribution.

AI costs scale quickly without constraints. Gateways enable spend tracking, per-team budgets, and real-time throttling to ensure proactive financial oversight.

• Aggregated spend across all providers.
• Internal chargeback and showback models.
• Billing end users based on specific usage.

Not all models carry the same risk profile. Gateways enforce governance through allow/deny lists, per-team restrictions, and blocking of high-risk models.

• Restrict experimental models to research labs.
• Enforce mandatory model security reviews.
• Audit trails for every model usage event.