What questions should I ask when evaluating an AI gateway?

Summary

• An AI gateway controls who can access which AI models, how requests are routed, what data can be used, and how costs are tracked.
• Prioritize data access governance, real-time policy enforcement, multi-provider support, and unified audit trails over single-ecosystem solutions.
• The strongest gateways extend your data governance policies directly to AI so that AI agents inherit the same permissions as direct data access.
• Key evaluation areas include security, cost management, runtime policy enforcement, observability, and integration with your existing identity layer.
• Avoid gateways that govern only models within a single ecosystem; you need control across external providers, open-source models, and coding agents.

What questions should I ask when evaluating an AI gateway?

An AI gateway sits between applications and AI models, controlling who uses which models, how data flows, and how costs are tracked. Evaluation requires questions about access control, cost management, runtime enforcement, observability, and integration with your existing governance. The strongest gateways unify AI governance with your data governance layer so identity, permissions, and audit trails are unified, not fragmented.

What is an AI gateway?

An AI gateway is middleware that routes requests from applications to AI models while enforcing organizational policies. It controls user access to models, enforces data access rules so AI requests inherit the same permissions as direct data queries, tracks costs per team and application, logs all requests for audit and debugging, and manages authentication across multiple providers. A gateway acts as a control plane at the chokepoint where organizational policy can be applied consistently before requests reach a model or external service.

Key evaluation questions

Access control: Does it enforce RBAC/ABAC? Can policies reference both AI roles (which teams use which models) and data roles (which data AI can retrieve)? Does it inherit permissions from your identity provider (LDAP, OAuth, SAML)?
Data governance: If AI queries your data warehouse, does it enforce the same permissions as direct queries? Can it classify data and block PII from external models? Does it track lineage?
Runtime enforcement: Can policies enforce at request time, not after? Does it support guardrails like prompt injection detection? Can you define conditional rules?
Cost management: Does it track spending by model, team, application, and user? Can you set hard caps that stop requests? Soft budgets? Intelligent routing to cheaper models?
Multi-provider support: Does it work with multiple vendors or only one ecosystem? Can you mix models in a single policy? Does it support MCP servers and coding agents?
Observability: Does it log every request, response, and cost queryably? Can you trace requests end-to-end? Can you generate compliance audit reports?
Governance integration: Does it integrate with your data platform using the same identity model? Do audit logs and dashboards unify data and AI activity?

How Databricks approaches this

Databricks Unity Gateway extends Unity Catalog to AI interactions. Policies defined for data automatically apply to AI. If you cannot query a table, your agents cannot either. This eliminates replicating access rules.
Unity Gateway enforces service policies (ALLOW, DENY, ASK), routes to multiple providers, tracks spending in real time with hard caps, logs to governed tables, and supports coding agents and MCP servers. Requests inherit user identity, so audit trails show who did what when. Because it integrates with Unity Catalog, you can classify models and agents, define policies at the asset level, and trace lineage through AI applications.

Evaluation checklist

Capability	Why it matters	Look for
Data access governance	Compliance risk if AI escapes data permissions.	Same identity model for data and AI; integrated with your platform.
Runtime enforcement	Detect and block issues before requests execute.	Request-time policies; guardrails; customizable rules.
Multi-provider	Avoid vendor lock-in; organizations use multiple models.	External provider support; standardized API.
Cost controls	AI spending is unpredictable without real-time tracking.	Per-team and per-model tracking; hard caps; smart routing.
Audit trail	Compliance and debugging require complete visibility.	Every request logged; queryable lineage; exportable logs.
Identity integration	Replicating rules across systems is error-prone.	Works with your identity provider; unified policies.
Emerging tools	New AI capabilities shouldn't require a new gateway.	Supports agents, MCP servers, coding agents.

FAQs

Do I need a separate gateway, or is cloud provider governance enough?

Cloud provider governance usually covers models within their ecosystem. As you adopt multiple foundation models, external agents, and open-source tools, you need a gateway that governs across vendors.

Can I skip integrating with my data platform?

Yes, but you'll replicate access rules in both systems and struggle to audit data flowing through AI applications. Integration simplifies governance and reduces risk.

How much latency does a gateway add?

Different gateways add different overhead. Caching minimizes impact. For batch applications, latency is less critical. For real-time systems, test the gateway's overhead before committing.

Should I use multiple gateways?

Possible but operationally complex. A unified gateway is simpler than a patchwork.

Can I run a gateway on-premises?

Some gateways support on-premises deployments. If data sovereignty matters, confirm deployment support before evaluating.