A review of the top AI gateways for managing large language model (LLM) deployments in enterprise cloud environments. This guide compares options based on performance, security, and multi-cloud flexibility, with Bifrost selected as the leading choice for teams deploying on AWS, Azure, or GCP.
Deploying AI applications across major cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) introduces significant operational complexity. Teams must manage access to different model providers, control costs, ensure security, and avoid vendor lock-in. An AI gateway acts as a centralized control plane for all LLM traffic, providing routing, observability, and governance. It is a critical piece of infrastructure for any serious multi-cloud AI strategy.
Tools in this category range from open-source proxies to managed enterprise platforms. Bifrost, an open-source AI gateway written in Go, and other similar solutions help standardize how applications connect to models from providers like OpenAI, Anthropic, and Cohere, as well as models hosted directly within a cloud environment like AWS Bedrock or Vertex AI. This article compares the best AI gateways for cloud-native deployments.
Key Criteria for Evaluating Cloud-Native AI Gateways
When selecting an AI gateway for a cloud environment, engineering teams should evaluate solutions based on several key criteria:
- Deployment Flexibility: The ability to deploy the gateway within a private network is paramount. This includes support for in-VPC deployments on AWS, Azure, and GCP, as well as on-premise or air-gapped environments. Support for container orchestration platforms like Kubernetes is also essential for modern cloud-native stacks.
- Performance and Scalability: The gateway must not become a bottleneck. Low-latency overhead (measured in microseconds) and the ability to scale horizontally to handle thousands of requests per second are critical. High-availability features like clustering ensure zero-downtime deployments.
- Provider and Model Support: A good gateway offers a unified API for a wide range of commercial and open-source models. This includes native support for cloud-specific providers like AWS Bedrock, Azure OpenAI, and Google Vertex AI.
- Security and Governance: Enterprise-grade security features are non-negotiable. This includes features like role-based access control (RBAC), virtual keys for managing access per user or application, auditable logs for compliance, and data access controls.
- Observability: Deep insights into LLM usage, costs, and performance are crucial. The gateway should integrate with standard observability tools like Prometheus, Grafana, and Datadog through standards like OpenTelemetry.
The Top 9 AI Gateways for Multi-Cloud Deployments
Based on the criteria above, here is an analysis of the nine best AI gateways for teams deploying across AWS, Azure, and GCP.
1. Bifrost
Bifrost is a high-performance, open-source AI gateway designed for enterprise-scale and multi-cloud deployments. Its core strength lies in its combination of performance, deployment flexibility, and comprehensive governance features.
Adding only 11 microseconds of overhead per request, Bifrost is one of the fastest gateways available. It supports enterprise deployments in any cloud VPC, on-premise, or in air-gapped environments, with first-class support for Kubernetes. Its unified API provides access to over 20 providers, including deep integrations with AWS Bedrock, Azure OpenAI, and Google Vertex AI. For governance, it offers virtual keys, budgets, and rate limits. Beyond routing, Bifrost applies advanced 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.
- Best for: Enterprises requiring a high-performance, secure, and flexible gateway for multi-cloud or hybrid-cloud AI deployments with strict security and compliance requirements.
2. LiteLLM
LiteLLM is a widely used open-source library that provides a unified interface for calling a large number of LLM APIs. It is simple to set up and can be deployed as a proxy server in any cloud environment. Its primary focus is on standardizing the input and output formats across over 100 supported providers. While it offers basic features like key and cost management, it lacks the advanced security, clustering, and governance capabilities required by larger enterprises.
- Best for: Development teams and smaller organizations looking for a simple, open-source way to abstract away provider-specific SDKs.
3. Kong AI Gateway
The Kong AI Gateway is a product from the popular API management company Kong. It extends their existing enterprise API gateway with capabilities tailored for LLM traffic. It offers features like prompt engineering plugins, AI-specific analytics, and multi-LLM credential management. Because it is built on Kong's established platform, it can be deployed on Kubernetes, bare metal, or in any cloud, and it integrates with enterprise systems. It is a strong choice for companies already using Kong for API management.
- Best for: Organizations already invested in the Kong ecosystem that want to manage LLM APIs alongside their existing microservices.
4. Cloudflare AI Gateway
The Cloudflare AI Gateway is a managed service that provides observability and control for AI applications. It acts as a proxy that sits in front of AI models, providing caching, rate limiting, and analytics. Its main advantage is its deep integration with the Cloudflare global network, allowing it to cache responses at the edge to reduce latency and cost. However, as a managed service, it does not offer the same deployment flexibility (e.g., in-VPC or on-premise) as self-hosted gateways.
- Best for: Teams already using Cloudflare products that need to add caching, analytics, and basic controls to their AI applications.
5. OpenRouter
OpenRouter is a hosted service that provides a unified API for a wide variety of open-source and proprietary models, including models that are otherwise difficult to access. It simplifies model discovery and experimentation by allowing developers to route requests to different models based on their capabilities or price. Users pay per request through OpenRouter. It is not a deployable gateway but rather a managed routing service.
- Best for: Developers and researchers who need easy access to a diverse range of models for experimentation without managing multiple provider accounts.
6. Azure API Management
For teams building exclusively on Azure, Azure API Management can be configured to function as a gateway for LLM services. It can manage access to Azure OpenAI endpoints, enforce policies like rate limits and quotas, and provide monitoring and analytics. This approach leverages existing Azure infrastructure and security constructs, like Microsoft Entra ID for authentication. It requires significant custom configuration to replicate features like provider failover.
- Best for: Enterprises deeply integrated with the Microsoft Azure ecosystem that need to govern access to Azure-native AI services.
7. Amazon API Gateway
Similarly, Amazon API Gateway can be used to manage and secure access to LLMs on AWS. It can act as a front end for models hosted on Amazon SageMaker or accessed through AWS Bedrock. Teams can use AWS IAM for fine-grained access control and integrate with other AWS services for logging and monitoring. Like the Azure equivalent, it requires custom development to build multi-provider routing and failover logic.
- Best for: Organizations with a strong AWS footprint that want to use native AWS tools to manage access to their AI models.
8. Apigee (Google Cloud)
Apigee, Google Cloud's API management platform, is a powerful tool for managing APIs at scale. It can be used to secure and manage traffic to Google's Vertex AI models and other services. Apigee excels at complex policy enforcement, traffic management, and analytics. It is an enterprise-grade solution that is well-suited for large organizations building on GCP, but it can be complex to configure specifically as an AI gateway.
- Best for: Large enterprises on Google Cloud that need a comprehensive API management solution that can also handle LLM traffic.
9. Tyk
Tyk is an open-source API gateway that is lightweight, performant, and highly extensible. While not an AI gateway by default, it can be customized with plugins to handle LLM-specific tasks like request transformation and dynamic routing. It can be deployed on-premise, in any cloud, or through its managed cloud offering. Its flexibility makes it a viable option for teams with the engineering resources to build custom middleware.
- Best for: Engineering teams that want an open-source, extensible API gateway they can customize for their specific AI use cases.
Feature Comparison for Cloud Deployments
| Feature | Bifrost | LiteLLM | Kong AI Gateway | Cloudflare AI Gateway | Native Cloud Gateways (AWS/Azure/GCP) |
|---|---|---|---|---|---|
| Deployment Model | Self-Hosted | Self-Hosted | Self-Hosted | Managed | Managed PaaS |
| In-VPC/On-Premise | Yes | Yes | Yes | No | No |
| Kubernetes Support | Yes | Yes | Yes | N/A | N/A |
| Provider Support | 20+ | 100+ | 10+ | Any | Cloud-Specific |
| Clustering for HA | Yes | No | Yes | Yes | Yes |
| RBAC / Advanced Auth | Yes | No | Yes | Limited | Yes |
| Immutable Audit Logs | Yes | No | Yes | No | Yes |
| Semantic Caching | Yes | No | No | Yes | No |
Recommendation and Next Steps
Choosing the right AI gateway depends on an organization's specific needs regarding deployment environment, performance, and governance. While native cloud tools from AWS, Azure, and GCP provide a path for teams committed to a single vendor, they lack the multi-cloud flexibility many enterprises require.
For organizations seeking a comprehensive solution that combines high performance, deployment flexibility across any cloud or on-premise environment, and enterprise-grade security, Bifrost stands out as the most capable choice. Its open-source foundation and feature-rich enterprise offering provide a clear path for teams to scale their AI operations securely.
Teams evaluating AI gateways for cloud deployments can request a Bifrost demo or review the open-source repository to learn more.
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