As AI deployments scale across multiple teams, applications, and customers, centrally managing access has become a critical security requirement. This guide compares the top 9 AI gateways that provide enterprise-grade Role-Based Access Control (RBAC) and Single Sign-On (SSO) for secure, multi-team AI governance.
As organizations move large language model (LLM) applications into production, the need for robust security and access control becomes paramount. When dozens or hundreds of developers, data scientists, and business units all require access to various AI models, managing permissions on a per-user, per-provider basis is untenable. This is where AI gateways with integrated Role-Based Access Control (RBAC) and Single Sign-On (SSO) become essential infrastructure.
An AI gateway acts as a centralized control plane for all AI-related traffic, intercepting requests from applications before they reach model providers like OpenAI, Anthropic, or Google. By integrating with an enterprise's existing identity provider (IdP) via SSO, a gateway can authenticate users and then authorize their requests based on predefined roles and permissions (RBAC). This ensures that a sales team can only access the models permitted for their projects, while an engineering team can access a broader set, all without managing separate API keys for every user and service.
Key Criteria for Evaluation
When comparing AI gateways for multi-team access, the following features are critical:
- SSO Integration: Support for standard protocols like OIDC and SAML to connect with identity providers such as Okta, Azure AD (Entra), and Google Workspace.
- Granular RBAC: The ability to define custom roles with specific permissions (e.g., model access, budget limits, admin rights) and assign them to users and teams.
- Multi-Tenancy: A hierarchical structure to manage organizations, teams, and projects, ensuring logical separation of resources and spend.
- Audit Logs: Immutable records of all administrative actions and access requests for compliance and security forensics.
- Provider Support: Compatibility with a wide range of LLM providers and the ability to manage access to them centrally.
Here are nine of the best AI gateways that deliver on these requirements.
1. Bifrost
Bifrost is an open-source, high-performance AI gateway built in Go, designed for enterprise-grade governance and low-latency production workloads. Its approach to access control is comprehensive, combining virtual keys with fine-grained RBAC and SSO.
Key RBAC & SSO Features:
- Identity Provider Integration: Bifrost Enterprise integrates with OIDC providers like Okta and Microsoft Entra for SSO, allowing organizations to manage user access through their existing identity systems.
- Role-Based Access Control: Administrators can define custom roles with specific permissions, controlling who can create, view, or manage resources like virtual keys, provider configurations, and routing rules.
- Virtual Keys: Access is primarily managed through virtual keys, which can be scoped to teams, projects, or individual users. Each key can have its own budget, rate limits, and model access policies, providing a powerful layer of granular control.
- Audit Logs: All authentication events and administrative changes are recorded in immutable audit logs, supporting compliance standards like SOC 2 and ISO 27001.
Best for: Enterprises that require a self-hosted, high-performance gateway with a deep and flexible governance model built for production scale.
2. LiteLLM
LiteLLM is a popular open-source Python gateway known for its simplicity and broad support for over 100 LLM providers. While the open-source version provides basic key management, the Enterprise tier activates the advanced access control features needed for multi-team deployments.
Key RBAC & SSO Features:
- SSO and SCIM: LiteLLM Enterprise supports SSO via Okta, Azure AD, and Google Workspace, along with SCIM for automated user provisioning and de-provisioning.
- Multi-Tenant Architecture: Provides a hierarchical structure for Organizations, Teams, and Projects, allowing administrators to delegate management and isolate spend.
- OIDC/JWT Authentication: In addition to SSO for the UI, requests can be authenticated using JWTs from an IdP, enabling programmatic and secure access for services and developers.
- Scoped Admin Roles: Central administrators can delegate admin responsibilities to team leads, reducing bottlenecks and enabling self-service management within defined boundaries.
Best for: Teams looking for an easy-to-deploy open-source solution that can scale with a commercial license to add enterprise-grade user management.
3. TrueFoundry
TrueFoundry positions itself as a comprehensive enterprise AI platform where the AI gateway is one component of a larger system for building, deploying, and monitoring AI applications. Its access control is designed for complex, multi-tenant enterprise environments.
Key RBAC & SSO Features:
- OIDC/SAML SSO: Offers broad support for SSO with major identity providers, allowing users to authenticate via their corporate credentials.
- YAML-Based RBAC: Roles and permissions are defined declaratively in YAML, which fits well into GitOps workflows and provides a clear, version-controlled definition of access policies.
- Service Accounts: Supports the creation of non-human identities with scoped tokens, enabling secure machine-to-machine communication with least-privilege access.
- Kubernetes-Native Multi-Tenancy: TrueFoundry's architecture uses Kubernetes namespaces for tenant isolation, providing a strong security boundary between different teams or customers.
Best for: Organizations that want a fully integrated AI platform with deep, Kubernetes-native multi-tenancy and declarative, GitOps-friendly policy management.
4. Kong AI Gateway
Kong AI Gateway extends the widely adopted Kong API Gateway with a suite of plugins specifically for AI traffic. For organizations already using Kong for API management, this provides a natural path to governing LLM access.
Key RBAC & SSO Features:
- Inherited Kong Gateway RBAC: Leverages the powerful RBAC and identity management features of the core Kong Gateway, including support for OIDC, LDAP, and OAuth 2.0.
- User and Model Tiering: Administrators can set up different access tiers for users, controlling token quotas and model access at the gateway level.
- Centralized Credential Management: Manages all upstream provider credentials securely within the gateway, abstracting them from end-users and applications.
- Audit Trails: Integrates with Kong's logging and analytics plugins to provide detailed audit trails of all requests and policy enforcement decisions.
Best for: Enterprises already invested in the Kong ecosystem for API management who want to apply the same control plane to their AI workloads.
5. Azure API Management (AI Gateway Pattern)
Microsoft's Azure API Management (APIM) is not a standalone AI gateway but a set of capabilities within APIM that allow it to function as one. It offers deep integration with the Azure ecosystem for access control.
Key RBAC & SSO Features:
- Microsoft Entra ID Integration: Provides seamless SSO and user management through native integration with Entra ID (formerly Azure AD).
- OAuth 2.0 Authorization: Secures access to AI APIs using standard OAuth 2.0 flows, allowing fine-grained control over what applications and users can do.
- Managed Identities: Authenticates to backend Azure AI services using managed identities, eliminating the need to store and manage API keys in code.
- Policy-Based Control: Uses APIM's flexible policy engine to enforce access rules, token limits, and content safety checks on every request.
Best for: Organizations deeply integrated with the Microsoft Azure ecosystem that want to manage AI APIs alongside their other services.
6. Cloudflare AI Gateway
Cloudflare AI Gateway extends Cloudflare's global edge network to manage and secure AI traffic. Its primary advantage is leveraging Cloudflare's existing security and performance infrastructure.
Key RBAC & SSO Features:
- Cloudflare Access Integration: Utilizes Cloudflare Zero Trust to enforce authentication policies, allowing organizations to use their existing SSO providers to control access to the gateway.
- Unified Control Plane: Provides a single dashboard for managing users, models, and logs, giving visibility and control over all AI requests.
- Security Controls: While full-fledged RBAC is evolving, it offers robust security features like rate limiting, credential protection, and PII redaction at the edge.
- Guardrails: Enforces content safety policies on both prompts and responses, ensuring a consistent moderation layer across all models.
Best for: Companies already using Cloudflare's network and security services who want a simple way to add visibility, caching, and basic access controls to their AI applications.
7. AWS API Gateway (for Amazon Bedrock)
For teams building on AWS, using Amazon API Gateway in front of Amazon Bedrock provides a native solution for controlling access. This pattern leverages AWS's mature Identity and Access Management (IAM) service.
Key RBAC & SSO Features:
- AWS IAM Integration: Defines fine-grained permissions for which users, roles, and services can invoke specific Bedrock models.
- Cognito User Pools: Integrates with Amazon Cognito for user authentication and management, supporting social and SAML-based identity providers.
- Lambda Authorizers: Uses AWS Lambda functions to implement custom authorization logic, enabling integration with any OIDC-compliant identity provider.
- VPC Endpoints: Secures traffic by routing requests through a VPC endpoint powered by AWS PrivateLink, keeping all communication within the AWS network.
Best for: Organizations committed to the AWS ecosystem that need a serverless, highly scalable way to apply IAM policies to their generative AI workloads.
8. MLflow AI Gateway
The MLflow AI Gateway is a component of the popular open-source MLOps platform, MLflow. It is designed to provide a unified interface to various model providers within the context of the broader machine learning lifecycle.
Key RBAC & SSO Features:
- Integrated RBAC: As of recent versions, MLflow includes RBAC for gateway resources, allowing administrators to control permissions on routes and endpoints.
- Centralized Key Management: Securely stores and manages API keys for different providers in one central location.
- MLflow Tracking Integration: Every request made through the gateway is automatically logged as an MLflow trace, tying production usage back to experiments and model versions.
- Extensible Authentication: Can be deployed behind a reverse proxy with an external authentication layer for integration with enterprise SSO systems.
Best for: Data science and MLOps teams that already use MLflow for experiment tracking and model management and want to extend its governance capabilities to production inference.
9. Google Cloud Agent Gateway
A newer entrant, Google Cloud's Agent Gateway is designed to provide secure and governed connectivity for AI agents. It focuses on integrating with a strong ecosystem of identity and security partners.
Key RBAC & SSO Features:
- Partner Ecosystem: Integrates with leading identity providers like Ping Identity and Saviynt to bring real-time, fine-grained authorization to agent and tool traffic.
- Programmable Data Plane: Allows teams to inject custom logic and third-party security controls directly into the request path for flexible policy enforcement.
- Focus on Agentic Traffic: Designed specifically to handle the complex interactions of user-to-agent, agent-to-agent, and agent-to-tool communication.
- Centralized Governance: Provides a single point of control and visibility for all agent interactions, regardless of where they are running.
Best for: Organizations building complex, multi-agent systems on Google Cloud who need a flexible and extensible control plane that integrates with best-of-breed security partners.
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