[This guide compares the top 12 LLM gateways for production AI workloads, with a focus on enterprise requirements. An AI gateway provides a unified API for multiple model providers, adding essential layers for routing, failover, governance, and security. After a detailed review, Bifrost emerges as the leading choice for teams that need high performance, comprehensive security, and flexible deployment options for mission-critical applications.]
Routing large-language model (LLM) traffic directly from an application to a provider's API is simple in development but introduces significant risk in production. A single provider outage can bring an entire AI feature down, and managing credentials and costs across multiple teams and models creates security and operational challenges. To solve this, engineering teams are increasingly deploying an LLM gateway as a centralized control plane for all AI traffic.
An LLM gateway acts as a proxy between applications and various LLM providers, offering unified access, routing, and governance. Bifrost is a high-performance, open-source AI gateway from Maxim AI designed for this purpose. It provides a common interface for thousands of models, enabling teams to enforce policies, manage costs, and ensure reliability without modifying application code. This article compares 12 of the best LLM gateways available today, evaluating them on the criteria most important for enterprise use.
Key Criteria for Evaluating Enterprise LLM Gateways
A production-ready LLM gateway needs to do more than just forward API calls. When evaluating options, engineering and platform teams should look for these core capabilities:
- Performance and Latency: The gateway itself should add minimal overhead. Low-latency processing is critical for real-time applications, and high throughput is necessary to handle production loads. Look for published benchmarks and efficient architecture.
- Provider Support and Flexibility: The gateway should support a wide range of LLM providers (OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, etc.) and open-source models (via Ollama, vLLM). It must allow teams to add, remove, or switch between providers without downtime.
- Reliability and Routing: Automatic failover is a primary reason to use a gateway. The system should detect provider outages or high latency and automatically reroute traffic to healthy alternatives. Advanced features include weighted load balancing and context-aware routing.
- Governance and Cost Control: The ability to set and enforce budgets, rate limits, and access controls is essential. Look for features like virtual keys that allow for granular, per-user or per-project policy management.
- Security and Compliance: The gateway is a critical point for security enforcement. It should integrate with enterprise identity systems (OIDC, SSO), manage secrets securely, and provide detailed audit logs for compliance with standards like SOC 2 and HIPAA.
- Deployment and Scalability: Enterprise teams need flexible deployment options, including in-VPC, on-premise, and air-gapped environments. The gateway should be ableto scale horizontally and operate in a high-availability cluster.
- Extensibility: The ability to add custom logic via plugins for tasks like data transformation, custom authentication, or integration with internal systems is a significant advantage.
The Top 12 LLM Gateways
Based on the criteria above, here is an analysis of the leading LLM gateways for enterprise teams.
1. Bifrost
Bifrost is an open-source gateway written in Go, engineered for extreme performance and enterprise-grade security. It stands out for its low latency (adding only 11µs of overhead at 5,000 RPS) and comprehensive feature set that covers the full lifecycle of AI traffic management.
Best for: Enterprise teams running mission-critical AI workloads that require best-in-class performance, comprehensive governance, and flexible, secure deployment options (in-VPC, on-premise, air-gapped). Its unified LLM, MCP, and Agents gateway capabilities make it a strong choice for organizations building complex, tool-using AI systems.
Key Features:
- High Performance: Published benchmarks demonstrate sustained high throughput with minimal added latency, making it suitable for demanding real-time applications.
- Unified Gateway: Natively supports LLM API calls, acts as an MCP gateway for agentic workflows, and integrates with CLI agents like Claude Code and Codex CLI.
- Advanced Governance: Uses virtual keys to manage budgets, rate limits, and access controls for individual users, teams, or applications.
- Enterprise Security: Offers clustering for high availability, RBAC, OIDC integration, and detailed audit logs. Beyond routing, Bifrost applies governance and security controls centrally, and Bifrost Edge extends that same governance and security to AI traffic on employee machines, with endpoint enforcement on each device.
- Reliability: Provides automatic fallbacks, intelligent load balancing, and semantic caching to improve reliability and reduce costs.
2. LiteLLM
LiteLLM is a popular open-source Python library that provides a lightweight, unified interface for calling over 100 LLM APIs. It is valued for its simplicity and broad provider support, making it an excellent tool for abstracting away provider-specific SDKs.
Best for: Development teams and smaller projects that need a quick and simple way to manage multi-provider LLM calls without the overhead of a full gateway deployment.
Key Features:
- Broad Model Support: One of the most comprehensive lists of supported models and providers.
- Simple Interface: Easy to integrate into existing Python applications.
- Basic Routing: Includes features for fallbacks and timeouts.
- Cost Tracking: Provides functions for tracking spending across different models.
3. Kong AI Gateway
The Kong AI Gateway is an extension of the widely used Kong API Gateway. It brings AI-specific capabilities to a mature, enterprise-grade platform known for its performance and extensibility through a large plugin ecosystem.
Best for: Organizations that have already invested in the Kong ecosystem for their microservices architecture and want to apply similar patterns of governance and observability to their LLM traffic.
Key Features:
- AI-Specific Plugins: Offers plugins for prompt engineering, response transformation, and AI-specific analytics.
- Unified Governance: Allows teams to manage LLM APIs alongside their existing REST and gRPC APIs with consistent policies.
- Enterprise-Ready: Inherits Kong's robust features for security, traffic control, and observability.
- Multi-LLM Support: Can route requests to various LLM providers and self-hosted models.
4. Cloudflare AI Gateway
Cloudflare AI Gateway is a managed service that sits on Cloudflare's global network, offering caching, analytics, and rate limiting for LLM applications. Its primary strength is leveraging Cloudflare's existing infrastructure for performance and DDoS protection.
Best for: Teams already using the Cloudflare stack who want a simple, managed solution for observing and caching their AI traffic with minimal setup.
Key Features:
- Global Caching: Caches responses at the edge to reduce latency for repeated requests.
- Analytics and Logging: Provides insights into usage, costs, and errors without requiring additional instrumentation.
- Rate Limiting: Protects backend models from traffic spikes and abuse.
- Simple Setup: Easy to configure for any application running behind Cloudflare.
5. OpenRouter
OpenRouter is a managed service that provides a unified API endpoint for a wide variety of open-source and proprietary models, including many that are not available from major cloud providers. It focuses on offering model choice and a simple, pay-as-you-go pricing model.
Best for: Developers and startups who want access to a diverse range of models for experimentation and production without managing multiple API keys and billing relationships.
Key Features:
- Extensive Model Variety: Offers access to a curated list of top-performing models from various sources.
- Standardized API: Uses an OpenAI-compatible API format for all models.
- Usage-Based Billing: A single account and billing system for all models.
- Community-Driven: Ranks models based on community feedback and usage.
6. NVIDIA NIM
NVIDIA NIM is a set of microservices that are part of the NVIDIA AI Enterprise software platform. NIM provides optimized inference engines for NVIDIA's models and a growing number of community models, exposing them through a standard API endpoint.
Best for: Enterprises with significant on-premise or private cloud infrastructure running on NVIDIA GPUs that want to deploy and manage LLMs with optimized performance.
Key Features:
- Optimized Inference: Tuned for maximum performance on NVIDIA hardware.
- Self-Hosted: Designed for deployment within an organization's own infrastructure.
- Standard API: Offers an OpenAI-compatible API for easy integration.
- Enterprise Support: Backed by NVIDIA AI Enterprise support and services.
7. Databricks AI Gateway
The Databricks AI Gateway, now part of Databricks Mosaic AI Agent Framework, provides a centralized interface for managing and governing access to LLMs within the Databricks ecosystem.
Best for: Organizations that use Databricks as their central data and AI platform and want to integrate LLM governance directly into their existing data workflows.
Key Features:
- Unified Interface: A single endpoint for accessing SaaS models and models served from Databricks.
- Credential Management: Securely stores and manages API keys.
- Rate Limiting: Enforces usage limits to control costs.
- Integration: Deeply integrated with the Databricks Lakehouse Platform.
8. Amazon API Gateway
While not a dedicated LLM gateway, Amazon API Gateway can be configured to serve this purpose. Teams can use it to create a unified API endpoint that routes requests to models hosted on Amazon Bedrock or SageMaker.
Best for: AWS-native teams with the expertise to configure and manage API Gateway, Lambda authorizers, and other AWS services to build a custom LLM routing layer.
Key Features:
- AWS Integration: Natively integrates with the entire AWS ecosystem, including IAM for security and CloudWatch for logging.
- Highly Scalable: A fully managed service that scales automatically.
- Flexible: Can be customized with AWS Lambda to implement complex routing, transformation, and authentication logic.
- Cost-Effective: Pay-per-request pricing model can be economical for variable workloads.
9. Azure AI Gateway
Similar to AWS, Microsoft's Azure API Management can function as a powerful LLM gateway for teams building on the Azure stack. It can centralize access to models from Azure OpenAI Service, as well as other providers.
Best for: Enterprises committed to the Microsoft Azure ecosystem that need to apply consistent API management policies to their AI and non-AI services.
Key Features:
- Azure Ecosystem Integration: Seamlessly works with Microsoft Entra ID for authentication, Azure Monitor, and other Azure services.
- Policy Engine: A flexible policy engine for transformations, security enforcement, and routing.
- Hybrid and Multi-cloud: Can manage APIs across different environments.
- Developer Portal: Includes a customizable portal for API discovery and documentation.
10. Apigee (Google Cloud)
Google's Apigee API Management platform is another enterprise-grade API gateway that can be adapted for LLM traffic. It provides robust capabilities for security, analytics, and traffic management, connecting to models on Google's Vertex AI or other providers.
Best for: Google Cloud Platform (GCP) customers and large enterprises that require advanced API security, monetization features, and deep analytics for their AI services.
Key Features:
- Advanced Security: Strong features for threat protection, OAuth, and identity management.
- AI-Powered Analytics: Uses machine learning to provide insights into API usage and performance.
- Hybrid Deployment: Supports on-premise, cloud, and hybrid deployment models.
- Extensibility: Can be extended with custom policies and integrations.
11. AWS Bedrock
Amazon Bedrock is not a gateway in the traditional sense, but it provides a single API to access a wide range of foundation models from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon. This simplifies access and billing.
Best for: Teams building on AWS who want easy, managed access to a curated set of high-performing models without managing multiple provider relationships.
Key Features:
- Single API: A unified interface for many popular foundation models.
- Managed Service: AWS handles the infrastructure for hosting and scaling the models.
- Security and Compliance: Integrates with AWS security services and is eligible for compliance programs like HIPAA.
- Customization: Supports fine-tuning models with your own data.
12. Vertex AI
Like Bedrock, Google's Vertex AI provides a platform with a unified API for accessing Google's own models (like Gemini) and models from third parties. It provides a single endpoint for accessing and managing a variety of models.
Best for: Organizations building on GCP who want access to Google's state-of-the-art models and a managed MLOps platform for building, deploying, and scaling AI applications.
Key Features:
- Access to Gemini Models: Provides API access to Google's powerful family of Gemini models.
- MLOps Platform: A comprehensive suite of tools for the entire machine learning lifecycle.
- Search and Conversation: Tools specifically for building RAG and conversational AI applications.
- Responsible AI: Includes tools for understanding, evaluating, and explaining model behavior.
Recommendation and Next Steps
For enterprises building production AI applications, an LLM gateway is no longer optional; it is a core component of a reliable, secure, and cost-effective AI stack. While managed services from cloud providers offer convenience, they often lack the performance, flexibility, and deep governance controls required for mission-critical systems.
Among the dedicated gateways, Bifrost stands out as the most complete solution for enterprise teams. Its combination of high performance, an open-source core, advanced governance through virtual keys, and deployment flexibility makes it the top choice for organizations that cannot compromise on speed, security, or control.
Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository to explore its capabilities further.
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