Vercel AI Gateway vs Higress: Which one is more suitable for your AI application?

In the past year, large language models (LLMs) have been transitioning from labs to the real world, rapidly penetrating various product forms. This trend has given rise to a new type of infrastructure role: the AI Gateway. The gateway is no longer a traditional network proxy; it has become a "traffic and access control hub" oriented towards LLM and MCP application scenarios, encompassing routing, model switching, access control, security authentication, token throttling, compliance auditing, and call monitoring.

Vercel's recently launched AI Gateway has garnered attention from the developer community as it is positioned as a hosted model proxy service, focusing on simplifying access and integration with other Vercel products, making it an ideal choice for lightweight AI applications. However, for development teams requiring more control, higher performance, or private deployment capabilities, Vercel's solution may not fully meet their needs.

Higress is an open-source AI Gateway developed by Alibaba Cloud, built on Envoy and Istio, specifically designed for LLM and MCP application scenarios. It possesses enterprise-level traffic governance and monitoring capabilities, including:

• Multi-model switching and fallback

• Token-based throttling and quota control

• Request-level monitoring and auditing

• API Key isolation and call statistics

• Rapid transformation and tuning capabilities for API-to-MCP

• MCP Server proxy capabilities

• MCP marketplace

This article will comprehensively compare Vercel AI Gateway and Higress from multiple dimensions such as positioning, functionality, architecture, and usage costs, and will help developers choose an AI Gateway based on typical scenarios. If you are building a truly viable large model product and MCP Server and wish to have greater control, monitoring capability, and compliance assurance over the call chain, this article will serve as a good reference for you.

1. Project Overview

In the development of large model applications, "control over the call chain" is gradually becoming a core variable in architectural design. Whether it involves multi-model switching, auditing and throttling model calls, or even predictability requirements for future costs, a suitable AI Gateway is becoming a key part of the infrastructure. Currently, Higress and Vercel AI Gateway represent two different approaches: self-built open-source and cloud-hosted. Below, we will provide a brief overview of the core positioning, target users, and technical characteristics of both.

Vercel AI Gateway: Lightweight, Hosted, Quick Start

Vercel AI Gateway is a hosted AI traffic proxy service recently launched by Vercel. It is positioned as a "developer-friendly" model access service. Through integration with the Vercel AI SDK, developers can quickly call mainstream models such as OpenAI, Anthropic, Mistral, and Cohere without having to deal with model API keys, rate limiting, or load balancing issues. Vercel's target users are mainly developers building lightweight AI features (such as chat, Q&A, generative UI), emphasizing low entry costs, quick onboarding, and no maintenance.

Features:

• Cloud-hosted, no deployment or maintenance required

• Supports 100+ mainstream models and multiple service providers (OpenAI, Anthropic, etc.)

• Provides a unified SDK interface (compatible with Fetch / LangChain)

• Defaults to built-in log tracking, rate control, and quota distribution capabilities

• Building a usage-based commercial billing system (currently free in Alpha phase)

Higress: An Open-source AI Gateway for Enterprise-grade Scenarios

Higress is an API gateway project led by Alibaba Cloud, which provides traffic governance and access control on K8s and microservices. It added management capabilities for AI applications targeted at LLM and MCP Server, especially suitable for mid to large enterprises and Web3 teams in the financial industry that require a higher level of control.

Higress supports deployment based on Kubernetes and can be deeply integrated with service discovery, API Key management, token throttling, call auditing, and more. Compared to lightweight proxy solutions, it offers stronger customization in traffic governance and user access control.

Features:

• Cloud-native architecture supporting Ingress / Gateway API

• Native support for multi-model routing, gray release, and fallback

• Supports integration with self-built large model platforms (such as MCP Server)

• Provides enterprise-level content compliance, security auditing, and call billing governance capabilities

• Open source, controllable, customizable, and easy to integrate into existing DevOps processes

2. Comparison of Functions, Deployment Experience, and Billing

Higress and Vercel AI Gateway respectively build AI Gateway capabilities from the ends of "self-hosting" and "hosted as a service". The former emphasizes controllability, depth of governance, and security control, while the latter emphasizes usability, quick onboarding, and frontend developer friendliness. We will compare Higress and Vercel AI Gateway's onboarding and costs from the following aspects:

1. Deployment and Configuration Paths

Summary: Vercel is suitable for developers in small and medium enterprises and startup teams for quick integration and launch. Higress is more suitable for mature DevOps capabilities that require deep customization for mid to large teams.

2. Cost Structure and Billing Strategy

Summary: Vercel offers "effortless" early-stage benefits but has uncontrollable costs, especially when token consumption is unclear. Higress may require a higher up-front investment (deployment and tuning needed) but offers strong cost compression potential and model governance capabilities in the later stages.

3. Functionality Comparison

Summary: AI applications are not just code calls; they are a full-chain project of data, computing power, and access strategies. In the context of rapidly rising token costs, early-stage investment in deployment and governance tools may be key to reducing long-term operational costs.

3. Situational Analysis

When choosing an AI Gateway solution, a core judgment criterion is: does your team prefer "rapid application construction" or "building enterprise-level capabilities"?

This determines whether you are more suited to use a hosted product like Vercel or a solution like Higress that emphasizes governance, security, and self-deployment capabilities. Below, we compare the suitability of both from several typical scenarios and outline corresponding user profiles.

Prototype Validation and Lightweight AI Applications — Vercel is More Suitable

User Profile

• Startup projects / Hackathon teams

• Small and medium enterprise engineering teams

• Fast product iteration pace, prioritizing idea validation

• Not focusing on cost structure and model provider details

Typical Scenarios

• Building a GPT-driven smart customer service demo

• Embedding an AI assistant module into an existing web application

• Low-barrier integration of mainstream models like OpenAI, Anthropic, Mistral

Advantages
Vercel offers hosted services, SDK, rate control, and fallback, significantly lowering the threshold for "from 0 to 1" calls to large model APIs. Especially for frontend developers, there is no need to understand complex network proxies and gateway configurations, enabling quick integration.

Enterprise Embedded Large Model Capability — Higress is More Suitable

User Profile

• Medium to large internet/AI platforms

• Technical organizations with backend/platform teams

• High demands for model quotas, call security, and data auditing

• Want to build multi-model dynamic scheduling or operational platform capabilities

Typical Scenarios

• Building a unified LLM access platform supporting OpenAI + self-built large models (e.g., DeepSeek, Qwen)

• Need to throttle tokens and log audits based on tenant/user dimensions

• Operations teams need monitoring, alerting, and cost tracking for large model calls

Advantages
Higress emphasizes “controllable,” “observable,” and “secure,” supporting users in inserting logic plugins (such as gray models, cost analysis, token review, etc.) into the access chain, providing an ideal foundation for enterprises to build custom LLM service platforms.

High Security & High Compliance Scenarios — Higress is Clearly Better than Hosted Solutions

User Profile

• Web3, finance, government teams with extremely high compliance requirements

• Strict regulatory needs regarding data transfer paths, model sources, and call chain logs

• Prefer/must use open-source or self-built large models

Typical Scenarios

• Building an inference engine + LLM (e.g., Ollama, LMDeploy, MCP Server) through private deployment

• Conducting chain audits, signature validation, and exception interception on model requests

• Having strict requirements for sensitive data or user privacy control

Real Case: Blockscout Using Higress to Build** ****mcp-server-plugin**
Blockscout is an important blockchain explorer platform in the Web3 field. In order to support self-built AI capabilities, it chose to create a custom MCP Server plugin based on Higress. This plugin enables it to:

• Receive model requests from multiple frontend modules

• Control tokens and model fallback at the tenant/address level

• Establish trusted data call paths between on-chain and off-chain

The “out-of-the-box + enterprise-level observability + strong plugin capabilities” provided by Higress has allowed Blockscout to quickly launch a highly trusted on-chain smart Q&A capability without relying on external platforms.

Summary: It’s Not About Who is Stronger, But Who is More Suitable

4. Beyond Tools, It’s a Comprehensive Reflection of Architectural Decisions

The choice of AI Gateway has never been just a matter of "tool selection"; it is a comprehensive decision regarding your entire AI application system's access control capability, cost governance capability, traffic control capability, and platform construction.

No matter what stage you are currently in, we recommend that you think about these questions in advance. Because the sooner you lay a stable foundation, the faster and further you can run on the track of future AI capability explosions.