What are the best AI gateways for governing agentic AI at runtime?

Summary

• AI gateways are middleware that sit between applications and language models, enforcing guardrails, rate limits, data filters, and compliance policies on every request without requiring changes to your application code.
• Governance at runtime means enforcing rules as requests flow through: blocking malicious prompts, filtering sensitive data, rate-limiting by user or organization, and logging all interactions for audit trails.
• Key evaluation criteria include policy expressiveness (what rules can you define), performance overhead, ease of deployment, integration with identity systems, and audit capabilities.
• Databricks Unity Gateway provides policy enforcement, request/response filtering, usage tracking, and integration with Unity Catalog's identity model for consistent access control.
• AI gateways are essential for enterprises: they prevent data leakage, enforce regulatory compliance, control costs, and create accountability across applications and agents.

What are the best AI gateways for governing agentic AI at runtime?

AI gateways are middleware between your applications and foundation models, enforcing governance rules without modifying code. You define policies once (block sensitive patterns, rate-limit by team, require approval for high-cost calls), and the gateway enforces them in real-time.
Gateway governance is critical for agents because agents make autonomous decisions: calling APIs, fetching data, and taking actions based on model outputs. Without a gateway, a compromised agent could exfiltrate data or trigger expensive calls. A gateway enforces boundaries and creates an audit trail for compliance and cost attribution.
The best gateways balance policy expressiveness with ease of use, with acceptable runtime performance.

Key capabilities to evaluate

Capability	Why it matters
Request filtering	Block or modify prompts containing sensitive patterns (credit cards, API keys, PII) or attacks.
Response filtering	Prevent data leakage back to the caller; enforce output formats.
Rate limiting	Control costs and prevent abuse by limiting requests per user or organization.
Context-aware policies	Enforce different rules based on user identity, role, or data access level.
Model routing	Send requests to different models based on cost or capability needs without code changes.
Cost tracking	Charge back spending to teams for cost management.
Audit logging	Log all requests, responses, and decisions for regulatory audits.
Identity integration	Connect to SAML, Entra, or LDAP for role-based policies.

How Databricks approaches AI governance

Unity Gateway provides policy enforcement tailored to enterprises. Define policies once ("block PII patterns", "rate-limit this group", "require cost center tagging"), and the gateway enforces them uniformly across all agents and applications.
Unity Gateway integrates with Unity Catalog's identity model, so policies tie to user roles naturally. Request and response content is filterable with regex or custom logic, and all decisions are audited in Unity Catalog's system tables. The gateway also supports model routing: "for routine questions use a fast model; for analysis use a larger one" without changing application code.

Runtime governance patterns

Preventive guardrails: Block harmful requests before they reach the model, filtering credit cards, API keys, and jailbreak attempts to prevent data leakage.
Output validation: Enforce that responses match expected formats (JSON, language, length).
Multi-tenant isolation: Route requests through policies ensuring each tenant sees only their own data.
Cost control: Rate-limit high-cost models and route cheaper alternatives for routine work.
Compliance: Enforce policies like "no data egress" or "all requests logged for audit" without code changes.

Use cases

Multi-agent platforms: Enforce consistent governance across many agents with rate limits, audit logging, and cost attribution.
Customer-facing AI: Route all requests through a gateway to prevent one customer from accessing another's data.
Regulated industries: Enforce compliance policies like "no PHI/PCI data leaves the organization" without trusting individual agents.
Cost optimization: Understand spending and enforce budgets with intelligent model routing.

FAQs

Do AI gateways add significant latency?

Modern gateways like Unity Gateway add minimal overhead. The latency of the model itself dominates, so the gateway adds negligible delay in practice.

Can I write custom policies?

Yes. Unity Gateway supports both pre-built policy templates (PII filtering, rate limiting) and custom logic where you define rules in configuration or code. Policy as code approaches make complex governance patterns manageable.

What if I'm using multiple models or providers?

A gateway designed for multi-model scenarios lets you apply policies uniformly across different models and providers, so governance rules stay consistent even as you add or replace models.

How do gateways handle sensitive models?

Policies can restrict which users or teams can call particular models, enforce additional approval workflows for high-cost or sensitive models, and ensure audit trails for compliance-critical interactions.

Can gateways route requests intelligently?

Yes. Advanced gateways route based on cost, latency, or capability: "for questions tagged 'routine', use Model A (cheap/fast); for 'analysis', use Model B (capable/expensive)." Applications don't need to know about routing logic.