AI Gateway

Top 5 Enterprise AI Gateways to Ensure AI Governance in Your AI Apps

As enterprises scale LLM deployments across customer support, code assistants, and autonomous agents, the gap between "AI that works in a demo" and "AI that works at enterprise scale" becomes a serious operational risk. Uncontrolled model access, unpredictable costs, compliance blind spots, and zero visibility into production behavior are now board-level concerns.

The EU AI Act now imposes fines of up to €35 million or 7% of global turnover for non-compliance. The NIST AI Risk Management Framework has become a baseline for U.S. organizations. In this environment, AI governance cannot remain a static policy document—it must be enforced at the infrastructure layer.

AI gateways serve as that enforcement layer. They sit between your applications and LLM providers, centralizing access control, cost management, audit logging, and compliance policies across every model call. According to Gartner's Hype Cycle for Generative AI 2025, AI gateways have emerged as critical infrastructure components for scaling AI responsibly.

Here are the top 5 enterprise AI gateways that can operationalize AI governance in your applications.

1. Bifrost by Maxim AI

Bifrost is an open-source, high-performance AI gateway built in Go by Maxim AI. It is purpose-built to embed governance directly into the LLM infrastructure layer rather than bolting it on as an afterthought.

Why Bifrost stands out for governance:

11 microsecond mean latency overhead at 5,000 RPS: The lowest in the category, ensuring governance controls never become a production bottleneck
**Hierarchical budget management:** Set spending limits and usage quotas at virtual key, team, project, or customer level to prevent any single workflow from exceeding its allocation
**Unified OpenAI-compatible interface:** Route requests across 12+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral) through a single API, simplifying audit trails
**Automatic failover and load balancing:** Seamless provider failover with zero downtime, ensuring governance policies apply even during outages
**Semantic caching:** Reduce costs and latency through intelligent response caching based on semantic similarity
**Native observability:** Prometheus metrics, distributed tracing, and comprehensive logging for full audit compliance
**Vault support:** Secure API key management with HashiCorp Vault integration for enterprise security requirements
**MCP support:** Model Context Protocol integration for governing AI agent tool access

Bifrost also integrates natively with Maxim AI's observability platform, enabling teams to run automated quality evaluations on production logs and set real-time alerts for governance violations.

Best for: Engineering teams that need production-grade governance without sacrificing performance or adding latency overhead.

See More: Bifrost AI Gateway | Bifrost Governance Docs | Agent Observability

2. Kong AI Gateway

Kong AI Gateway extends Kong's established API management platform with AI-specific governance capabilities. For organizations already running Kong for traditional API traffic, this provides a unified control plane across both API and AI workloads.

Key governance capabilities:

Centralized AI traffic management: Apply rate limiting, authentication, and access control policies consistently across all AI model requests
Plugin-based architecture: Extend governance with custom plugins for PII detection, prompt validation, and content moderation
Enterprise API governance heritage: Leverages Kong's mature RBAC, audit logging, and compliance tooling that has been proven at scale
Multi-cloud and hybrid deployment support: Enforce governance policies across on-premises and cloud environments

Best for: Enterprises with existing Kong deployments that want to extend their API governance framework to AI traffic without adding a separate tool.

3. Azure API Management AI Gateway

Azure API Management has expanded its capabilities with a Unified AI Gateway pattern that provides centralized governance for organizations heavily invested in the Microsoft ecosystem.

Key governance capabilities:

Unified authentication enforcement: Consistent API key and JWT validation across all AI requests with managed identity for backend authentication
Dynamic routing and cost optimization: Model-aware backend selection based on capacity, cost, and performance factors
Centralized audit and traceability: All AI requests and responses are logged centrally, enabling unified auditing
Policy-driven governance: YAML-based policy definitions for rate limiting, content filtering, and access control

Best for: Organizations already using Azure services that need to govern AI model access alongside their existing API infrastructure.

4. LiteLLM

LiteLLM is a Python-based open-source AI gateway that provides a unified OpenAI-format interface across 100+ LLM providers. It has gained popularity for its broad provider compatibility and straightforward setup.

Key governance capabilities:

Virtual key management: Create and manage API keys per team or project with individual spend limits
Basic budget tracking and controls: Monitor usage and set spend limits per virtual key or user
Model whitelisting: Restrict which models specific teams or projects can access
Request and response logging: Track all LLM interactions for audit purposes

Considerations: LiteLLM lacks formal enterprise support, SLAs, and advanced security controls. Teams have reported performance degradation and instability at higher request volumes, making it less suitable for latency-sensitive production workloads. Its governance features are functional but not as granular as purpose-built enterprise solutions.

Best for: Engineering teams with strong internal DevOps capabilities that need broad provider coverage and can manage the open-source complexity.

5. IBM watsonx.governance

IBM watsonx.governance takes a different approach by focusing on AI model lifecycle governance rather than API-layer traffic management. It is designed for organizations with complex compliance programs that need centralized oversight across their entire AI portfolio.

Key governance capabilities:

AI model inventory and lifecycle tracking: Centralized registry cataloging all AI models across the organization with development stage, deployment status, and risk classification
Automated compliance workflows: Policy checks against the EU AI Act, NIST AI RMF, and ISO/IEC 42001, with automated documentation and audit report generation
Bias and fairness monitoring: Continuous monitoring of deployed models for bias drift and fairness degradation with automated alerts
Explainability reporting: Generates model explanation reports documenting decision-making processes for high-risk AI systems

Considerations: Watsonx.governance focuses on model-level governance rather than LLM API infrastructure. It is complementary to gateway-level tools like Bifrost rather than a direct replacement, organizations with both API routing and model lifecycle governance needs benefit from pairing both approaches.

Best for: Large enterprises in regulated industries (financial services, healthcare, government) that need comprehensive model risk management and regulatory documentation.

How to Choose the Right AI Gateway for Governance

Selecting an AI gateway depends on your organization's specific governance requirements. Here are the key dimensions to evaluate:

Performance overhead: For real-time AI applications, every millisecond matters. Bifrost's 11µs overhead is orders of magnitude lower than alternatives, ensuring governance enforcement does not degrade user experience.
Depth of cost controls: Hierarchical budget management (by team, project, and customer) is essential for organizations with multiple business units consuming LLM resources.
Compliance and audit readiness: EU AI Act and NIST AI RMF compliance require comprehensive logging, traceability, and policy enforcement at the infrastructure layer.
Deployment flexibility: Self-hosted, cloud, and hybrid deployment options matter for data residency and security requirements.
Integration with evaluation and observability: Governance does not end at access control. The ability to run automated quality evaluations on production data and measure AI application reliability over time is critical for sustained governance.

For teams building production-grade AI applications that require both infrastructure-level governance and continuous quality monitoring, Bifrost paired with Maxim AI's evaluation and observability platform provides the most comprehensive stack available.

Ready to operationalize AI governance in your applications? Book a demo to deploy enterprise-grade governance in minutes.