What features should an enterprise AI gateway have?

Summary

• An enterprise AI gateway combines access control, traffic routing, and cost management to help organizations scale AI safely while controlling who uses which models and tracking spend.
• Critical capabilities include fine-grained governance over AI services, real-time cost attribution by team and use case, and automated guardrails that prevent unauthorized access or cost overruns.
• Observability is essential: you need end-to-end visibility into agent behavior, model interactions, and data lineage so you can audit decisions and investigate incidents.
• Security features like PII redaction, prompt injection mitigation, and contextual policies protect data in transit and ensure compliance with governance rules.
• Smart routing lets you send requests to the most cost-effective or highest-quality model based on your requirements, balancing performance and economics.

What features should an enterprise AI gateway have?
An enterprise AI gateway is a control plane that governs how teams access and consume AI services. Instead of letting developers connect directly to models, your gateway acts as a single point of authorization, routing, cost tracking, and security. The best gateways combine access control, intelligent routing, spend management, observability, and safety guardrails.

Key capabilities to evaluate

Capability	Why it matters
Fine-grained access control	Control which teams use which models. Attribute-based access control (ABAC) enforces policies based on role, department, or project.
Traffic routing and fallback	Route requests intelligently across multiple models. Automatic fallback if your primary model fails. Balance cost and quality by sending different request types to different models.
Granular cost attribution	Charge costs back to the right team or project. Without this, AI spending becomes invisible, buried in aggregate costs.
Hard spend caps	Enforce maximum budgets automatically. When a team hits its limit, the gateway halts requests.
Real-time usage monitoring	Dashboard showing who's using what and for what purpose. Alerts catch anomalies early.
End-to-end tracing	Trace the full chain: who authorized it, what data was accessed, which model answered. Essential for debugging and compliance.
PII detection and blocking	Automatically detect and block personally identifiable information in prompts and responses.
Prompt injection mitigation	Block malicious prompts designed to manipulate models or leak data.
Audit logging	Comprehensive logs of all gateway decisions: who accessed what, whether policy allowed or denied each request.
Data governance integration	AI access inherits the same policies, roles, and audit trails as your data warehouse.

How Databricks approaches enterprise AI gateways

Databricks Unity Gateway, built on Unity Catalog, extends governance that protects your data to also protect your AI.
Access control. Unity Gateway uses Unity Catalog to define who can use which models, agents, and MCP servers. Policies cascade: when you restrict a model, all applications using it respect the restriction.
Spend tracking. Unity Gateway tracks costs across Databricks-hosted models, external models like GPT or Claude, and custom agents. You see costs by user, team, tool, and use case. Hard spend caps prevent budget overruns.
Smart routing. The gateway routes requests based on cost, latency, or availability. If an external provider is unavailable, requests automatically fall back. This flexibility lets you mix hosted and external models.
Agent tracing. The gateway captures which models were called, which tools were invoked, what data was accessed, and whether each action passed policy checks. Lakewatch integration detects suspicious activity.
Security guardrails. Unity Gateway includes PII detection, prompt injection filtering, and contextual policies that allow, deny, or require approval. Policies depend on user, team, data, and model.
Unified governance. Because Unity Gateway sits on Unity Catalog, it inherits role-based access control, data lineage, and audit logs. Your data and AI governance are unified.

FAQs

Can a gateway prevent agents from accessing unauthorized data?

Yes. Unity Gateway uses Unity Catalog policies, so an agent's data access inherits the same restrictions as human access. This prevents data leaks.

What if an external model provider goes down?

Unity Gateway supports fallback: requests automatically route to a secondary provider. You define the fallback policy, keeping applications running.

Can I use multiple models with unified governance?

Yes. Unity Gateway connects multiple external providers and routes all requests through the same access control and cost tracking, so everything is audited.

Is audit logging sufficient for compliance?

Unity Gateway captures who authorized each request, what data was accessed, and whether policy was enforced. Verify logs meet your specific regulatory requirements.