A comparison of the top AI gateways for routing, managing, and securing traffic to Google's Vertex AI models. This review examines leading options and finds that for enterprise teams, Bifrost offers the best combination of performance, governance, and deep integration with the Vertex AI ecosystem.
As engineering teams scale their use of AI, they increasingly adopt a multi-model strategy, combining Google's powerful Gemini models on Vertex AI with other providers like Anthropic or open-source solutions. This approach, while flexible, introduces significant operational complexity in routing, authentication, and cost management. An AI gateway is a dedicated infrastructure layer that solves this by creating a unified entry point for all LLM traffic. For teams building on Google Cloud, selecting the right gateway is critical for maintaining reliability and control.
This article compares the best AI gateways for Google Vertex AI, evaluating them on performance, provider integration, governance capabilities, and enterprise readiness. While several tools offer basic proxying, a robust gateway provides intelligent routing, automatic failover, and granular security controls. Options range from comprehensive platforms like Bifrost, an open-source AI gateway from Maxim AI, to ecosystem plays from existing API management vendors.
Key Criteria for a Vertex AI Gateway
When evaluating an AI gateway for a Vertex AI-centric stack, several capabilities are essential:
- Native Vertex AI Integration: The gateway must have first-class support for Vertex AI, including authentication mechanisms (like gcloud service accounts) and compatibility with the full range of models, including the Gemini family.
- Performance and Latency: The gateway should add minimal overhead to each request. Look for published benchmarks and an architecture designed for high-throughput, low-latency inference.
- Reliability Features: Core capabilities should include automatic failover to a different model or provider if a Vertex AI endpoint fails, along with intelligent load balancing across multiple model deployments.
- Governance and Cost Control: The ability to create virtual keys with specific budgets, rate limits, and model access rules is crucial for managing usage across different teams and applications.
- Observability: The gateway must provide detailed logs and export metrics to platforms like Prometheus or OpenTelemetry for comprehensive monitoring of AI traffic.
1. Bifrost
Bifrost is an open-source, high-performance AI gateway written in Go. It is designed for enterprise-grade reliability and governance, making it a leading choice for teams running mission-critical AI workloads on Vertex AI.
Its primary strengths are its performance and comprehensive feature set. Published benchmarks show Bifrost adds only 11 microseconds of overhead at 5,000 requests per second, ensuring that the gateway is not a bottleneck.
Best for: Enterprise teams that require best-in-class performance, deep governance capabilities, and a flexible, open-source foundation for managing both Vertex AI and other LLM providers.
Key Features:
- Deep Vertex AI Support: Bifrost has a dedicated Google Vertex AI provider that supports the full model catalog, including Gemini 1.5 Flash, Pro, and Ultra. It can be configured as a drop-in replacement for existing Vertex AI SDK integrations.
- Automatic Failover and Load Balancing: Teams can configure automatic fallbacks that seamlessly reroute traffic from a failing Vertex AI model to a backup, which could be another Google model, an Anthropic model, or an open-source model hosted on Ollama.
- Granular Governance: Bifrost’s system of virtual keys allows administrators to set precise budgets, rate limits, and model access policies for each user, team, or application. This is essential for controlling costs in a multi-tenant environment.
- Unified Observability: It provides detailed telemetry and integrates with standard tools like Prometheus and OpenTelemetry, allowing teams to monitor Vertex AI usage alongside their other infrastructure.
- Enterprise Security: Beyond routing, the Bifrost AI gateway applies centralized governance and security controls. For comprehensive protection, Bifrost Edge extends those same policies to cover AI usage on employee endpoints, governing desktop apps and coding agents that connect to Vertex AI.
2. Kong AI Gateway
The Kong AI Gateway is a product from the popular API management company Kong. It extends their existing gateway infrastructure to handle LLM traffic, making it a natural choice for organizations already using Kong for their microservices.
Kong provides a reliable and scalable platform with a focus on integrating AI governance into a broader API strategy. Its AI-specific features include prompt engineering, credential management, and analytics.
Best for: Organizations already invested in the Kong ecosystem for API management who want to extend the same control plane to cover their Vertex AI and other LLM endpoints.
Key Features:
- Multiple Provider Support: Kong supports a variety of LLM providers, including Google Vertex AI.
- AI-Specific Plugins: It offers plugins for prompt validation, transformation, and security, allowing teams to enforce policies at the gateway level.
- Unified Analytics: Teams can monitor and analyze AI traffic alongside their other API traffic within the Kong control plane.
- Enterprise Integrations: As an established enterprise product, it integrates with a wide range of identity providers and security tools.
3. Cloudflare AI Gateway
Cloudflare AI Gateway is a managed service that provides caching, rate limiting, and analytics for AI applications. It leverages Cloudflare's massive global network to improve the performance and reliability of connections to LLM providers like Google Vertex AI.
Its main value proposition is its simplicity and integration with the rest of the Cloudflare ecosystem. For teams already using Cloudflare for DNS, CDN, or security, adding the AI Gateway is a straightforward process.
Best for: Teams already using the Cloudflare platform who need a simple, managed solution for caching, basic rate limiting, and visibility into their Vertex AI API usage.
Key Features:
- Global Caching: Cloudflare can cache responses from Vertex AI at its edge locations, reducing latency for repeated queries.
- Analytics and Logging: It provides a dashboard for viewing requests, tracking errors, and monitoring costs across different models.
- Rate Limiting: Basic rate limiting helps protect applications from abuse and control costs.
- Easy Setup: As a fully managed service, setup requires minimal configuration.
4. LiteLLM
LiteLLM is a popular open-source library that provides a unified interface for calling over 100 LLM providers, including Google Vertex AI. While primarily a library, it can also be deployed as a standalone proxy server, functioning as a lightweight AI gateway.
Its key strength is its breadth of model support and active community. It is an excellent choice for development environments, research projects, and applications that need to switch between many different models with minimal code changes.
Best for: Developers and small teams looking for a highly flexible, open-source solution to standardize API calls across a vast number of LLM providers, including Vertex AI.
Key Features:
- Broad Model Compatibility: LiteLLM provides a consistent input/output format for hundreds of models, simplifying development.
- Callback Functions: It supports callbacks for logging, cost tracking, and sending data to platforms like Langfuse or Helicone.
- Key and Timeout Management: The proxy can manage API keys and set consistent timeouts across all providers.
- Active Community Support: Being a widely used open-source project, it benefits from a large community of contributors and users.
How the Gateways Compare for Vertex AI Workloads
| Feature | Bifrost | Kong AI Gateway | Cloudflare AI Gateway | LiteLLM |
|---|---|---|---|---|
| Primary Use Case | Enterprise Governance & Performance | Unified API Management | Edge Caching & Analytics | Unified API Library |
| Vertex AI Integration | Native Provider | Supported | Supported | Supported |
| Performance | <1ms Overhead | High | High (with Caching) | Variable |
| Failover/Routing | Automatic & Advanced | Policy-based | Basic | Basic |
| Governance | Virtual Keys, Budgets, RBAC | Plugins, Policies | Rate Limiting | Basic |
| Deployment Model | Self-hosted (OSS), Managed | Self-hosted, Managed | Managed Service | Self-hosted |
| Open Source | Yes | Core is OS, AI is Enterprise | No | Yes |
Recommendation
For production applications built on Google Vertex AI, the choice of an AI gateway has a direct impact on reliability, security, and cost. While managed services like Cloudflare offer simplicity and LiteLLM provides unmatched flexibility, they often lack the deep governance and performance characteristics required for enterprise scale.
For most teams running serious workloads, Bifrost stands out as the most complete solution. Its combination of extremely low latency, advanced reliability features like automatic failover, and granular governance through virtual keys makes it uniquely suited for managing complex, multi-model AI applications that rely on Google Vertex AI.
Teams evaluating AI gateways for their Google Cloud environment can request a Bifrost demo or explore the project's open-source repository to test its capabilities directly.
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