Kong AI Gateway

企业级AI应用统一接入层，提供集中治理、安全防护与成本控制

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https://konghq.com/products/kong-gateway

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Kong AI Gateway is a unified access layer for enterprise-level AI applications launched by Kong Inc., built on the high-performance, cloud-native Kong Gateway. It serves as the traffic management center between developers and large language models (LLM), providing enterprise-level functions such as centralized governance, security protection, data desensitization, and cost monitoring. Supports mainstream AI service providers such as OpenAI, Anthropic, Google, and Azure OpenAI, and supports the recently launched MCP (Model Context Protocol) protocol. It will be officially GA (General Availability) in June 2024, and Agent-to-Agent traffic support will be added in April 2026. It is one of the most comprehensive AI Gateway solutions currently.
Kong Inc. was founded in 2015 and initially started as the open source API gateway Kong. The project is built on Nginx and OpenResty. After years of development, it has become the world's leading API and microservice management platform. Headquartered in San Francisco, the company provides enterprise-grade API management and AI connectivity solutions globally. The origins of Kong AI Gateway can be traced back to February 2024, when Kong released six new open source AI plug-ins in Kong Gateway 3.6, turning every Kong Gateway deployment into an AI Gateway. In June 2024, with the release of Kong Gateway version 3.7, AI Gateway was officially GA, marking the product's transition from the testing phase to production-ready status. In April 2026, Kong AI Gateway added support for Agent-to-Agent traffic, becoming the most comprehensive AI Gateway solution in the agent era, capable of supporting communication and collaboration between AI Agents.
Kong AI Gateway provides the following core features: Unified access layer: As a traffic management hub between applications and large language models, developers can access multiple AI service providers through a single API endpoint without the need to integrate each model separately. Multi-model support: Supports mainstream AI service providers such as OpenAI, Google (Gemini), Azure OpenAI, Anthropic (Claude), etc., and supports MCP protocol to connect various AI tools and services. Security protection: Provide centralized management and security protection functions, including enterprise-level security capabilities such as sensitive data filtering, prompt word interception, and response auditing. Cost control: Help enterprises control AI usage costs through token consumption optimization and refined traffic management. Observability: Provides complete request logging, performance monitoring, and cost analysis capabilities to help teams understand AI usage. Current limiting and traffic management: Supports advanced current limiting functions, and can set different current limiting policies based on users, applications or API keys. Developer-friendly: Supporting OpenAI SDK, developers can quickly access AI capabilities in a zero-code or low-code manner.
Kong provides two versions: open source version and enterprise version: Kong Gateway (open source version): Basic functions are free, including core API management, current limiting, authentication and other plug-ins. It is suitable for small and medium-sized teams or individual developers. Kong Enterprise: Enterprise-grade version, providing complete security protection, compliance checks, detailed observability and technical support. For specific pricing, please contact the sales team for a quote. For AI Gateway functions, the Enterprise Edition provides more advanced security and governance capabilities, including sensitive data desensitization, advanced cost analysis, multi-tenant management, etc.
In terms of positive reviews, Kong AI Gateway has received positive feedback in enterprise-level AI application scenarios. Developers recognize that its unified access layer design can simplify multi-model management complexity and operation and maintenance pressure. The plug-in architecture makes the deployment of security policies flexible and efficient. The performance is stable and reliable, suitable for use in production environments. In terms of negative comments, some users believe that the enterprise version is priced higher and may be unaffordable for small teams. The upward curve of configuration and learning is relatively steep, and new users need some time to get familiar with it. Some advanced features require the enterprise version to be used, and the open source version has limited functions.
From an industry perspective, Kong, as an established API gateway manufacturer, has strong brand recognition and technology accumulation in the enterprise market. Kong AI Gateway has a clear positioning and is dedicated to solving pain points such as model fragmentation, complex operation and maintenance, and security risks in the implementation of enterprise AI applications. In the AI Gateway track, Kong faces competition from competing products such as cloudflare and AWS API Gateway. However, Kong’s advantage lies in its open source ecosystem and enterprise-level functional depth, which is suitable for enterprise users with higher requirements for security and compliance.
The main challenges faced by Kong AI Gateway include: small teams may not be able to afford the cost of the enterprise version and need to evaluate ROI. Compared with cloud vendor native AI services, there may be integration complexities in some scenarios. Professional operation and maintenance personnel are required for management, which increases labor costs.
Kong AI Gateway is suitable for the following scenarios: enterprises that need to access multiple AI service providers and want to uniformly manage and govern AI traffic; enterprises that have higher requirements for data and communication security; enterprises that need to finely control AI costs; enterprises that already have Kong infrastructure and want to expand their AI capabilities. For individual developers or small teams, you can try the open source version first to get familiar with the product before deciding whether to upgrade to the enterprise version.
Kong AI Gateway is a mature solution for the enterprise-level AI access layer, providing unified multi-model access, complete security protection and cost control capabilities. As part of the Kong ecosystem, it is compatible with enterprises’ existing API management strategies and is suitable for enterprises that already use or plan to use Kong. For new projects, adoption needs to be evaluated based on specific needs and budget.

User Reviews

FrancesWilliams040

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我们团队从去年开始用 Kong AI Gateway，最直观的感受就是性能确实猛，28K RPS 不是吹的。但说实话配置门槛挺高的，decK CLI 那一套对新手不太友好，我们折腾了一个月才把生产环境跑稳。

WPerrySr43

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太贵了。我们算了笔账，5 个 LLM 集成跨 20 个微服务，光服务费一个月就要两千多美金，小公司根本扛不住。

Christine.RuizIII

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已经在用 Kong API Gateway 的团队加 AI 插件确实是顺滑的，边际成本很低。但让我从零开始选的话我不会选 Kong，太重了，LiteLLM 几分钟就搭起来了一个 AI 代理。

iLaertesMelo_dev

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Kong AI Gateway 2.0 那个专用运行时确实是好东西，终于不用跟 API Gateway 抢发布周期了。AI 迭代速度那么快，绑在一起发布谁都难受。

Donald_Vasquez_2023

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语义缓存能省 40% 的 token 成本，听起来很香对吧？但这是 Enterprise 版才有的功能，我们小团队用开源版就只能干瞪眼。

JAree

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Lua 写插件是真的劝退。我们团队全是 Python 背景，为了搞一个自定义路由还得去学 Lua，这不是给自己找麻烦吗。人家 Portkey 直接 Python native 插件，差距太大了。

JObro

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Kong 3.13 之后原生支持 MCP 和 A2A 协议了，这对我们做 agent 治理特别重要。之前还得自己拼插件，现在终于有第一方支持了。但文档还是跟不上，好多配置要靠社区帖子猜。

trueStevenWeaver_2024

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合规性方面 Kong 确实是行业标杆，SOC2、GDPR、HIPAA 全齐，还能完全自部署保证数据不出 VPC。金融和医疗行业选它准没错。

DVasquez_99

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我在银行做 infra，从 API Gateway 一路用到 AI Gateway，Kong 的治理能力没得说。RBAC、审计日志、PII 清洗全在一个平台搞定，合规审计一次过。

HeatherMartinIII

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Kong Konnect 的定价真的让人头大，每个服务单独计费，agent 工作流一扇出就是几十次 API 调用，一个月下来账单直接起飞。真得算清楚账再上。

Angela_Phillips007

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快是快，就是太重了。光 docker 部署就要配数据库、控制面、数据面三个组件，想跑个简单的 LLM 代理还得搞懂一堆 Lua 配置，对只想快速验证想法的小团队来说太不友好了。

StakeHuq

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对比了一圈，最后选了 Portkey。不是说 Kong 不好，而是对我们只有十几人的 AI 团队来说，Kong 的运营负担太大了。光维护那套 declarative config 就够呛，而且 AI 专属功能的文档确实落后。

STurnerIII

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Kong 的 Kubernetes ingress controller 确实是行业标准，我们已有的微服务体系直接无缝对接。一个控制面管 API 和 AI 流量，运维同学表示很爽。

Jeremy_Cook_Plus12

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有个坑提醒一下大家——Kong Konnect 免费 tier 没有 DPA（数据处理协议），数据驻留和零保留策略得签企业合同才能保证。如果你们在做合规项目，这个提前跟销售确认清楚。

孔梅

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PII 清洗功能做得是真不错，20 多种敏感信息类型覆盖，prompt 和 response 双向都能处理，还能做合成替换。在金融行业这是刚需，我们合规团队看了之后非常满意，终于不用在每个应用里单独做数据脱敏了。

GSimmons369

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跟 Portkey 比，Kong 的吞吐量确实高出一大截，但 AI 专用可观测性就差远了。Portkey 开箱就能看到 token 成本明细，Kong 还得自己搭 Prometheus+Grafana，折腾。

SusanNelson_77973

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MCP Registry 的推出是好事，至少能让团队发现和管理 MCP 服务器了。但 runtime governance 还不够深，每次 tool 调用前后的策略检查还得自己写插件。对比 TrueFoundry 那种原生就支持 MCP 治理的方案，Kong 这块还是差了一截。

Pamela_Ortiz

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开源版跟企业版的差距太大了。GUI 没有，高级分析没有，token 级限流也没有。说白了你想用好就得掏钱买 Konnect，个人开发者根本玩不起。

Michelle.BakerIII

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自部署 Kong 其实挺麻烦的，Nginx + 数据库 + data plane 节点一套下来，不算 AI 插件就已经挺复杂了。如果只是为了 AI 代理，没必要上 Kong。

CA_lin

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decK 的 state file 管理需要很强的工程纪律，团队没形成习惯之前经常出现配置漂移的问题。有一次我们在 staging 改了个路由，忘记了同步，生产直接挂了半小时。

Bobby.Kim37

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prompt 安全防护做得不错，semantic prompt guard 基于语义做拦截，比简单的关键词过滤智能多了。不过要小心配置太严把正常请求也拦了，我们调了几轮才找到合适的阈值。

HashPro007

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Kong 2.0 那个 AI Gateway 独立版本确实不错，但还在 private beta 阶段，想用还得找客户经理申请，流程太长了。

LQQ858Z589

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我们做了个 benchmark，在同等资源配置下 Kong 比 LiteLLM 快了 8 倍多，这差距确实大。但对于大多数场景来说，LLM 本身的延迟才是瓶颈，网关那几毫秒差异真感觉不出来。

Christina.Martinez_72

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学习曲线是真的陡。我花了整整两周才搞清楚 Service、Route、Consumer、Plugin 之间的关系，对比之下 Cloudflare AI Gateway 简直傻瓜式操作。

田素

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LangChain 集成做得还可以，但 LlamaIndex 支持到 2026 年 3 月还是缺口，这对我们做 RAG 应用来说很致命。每次都要手动转格式，等于多了一层维护成本，希望官方尽快把这个坑填上。

DeFiSaver563

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我建议别写自定义 Lua 了，Kong 3.13 之后原生就支持了 Gemini 的 WebSocket，大部分场景用内置插件就够了。Lua 插件维护成本太高，而且懂 Lua 的人越来越少了。

Clara359

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token 级限流是刚需，但它在 Enterprise 版里，开源版只有请求级限流。一个 agent 工作流一次可能消耗几千个 token，但只算一次 HTTP 请求，这限流等于形同虚设。说白了不买 Enterprise 版就别想用真正有用的 AI 功能。

Christian_Hill520

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Kong 的定位很清晰——做大厂的基础设施，不适合小团队。我们去年从 Kong 切换到 LiteLLM，运维负担直接降到零，虽然性能差了点但够用了。

莲花524

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OpenTelemetry 集成做得不错，streaming response 也能追踪。就是在 dbless 模式下配置复杂度有点高，踩了不少坑。建议非必要别用 dbless 模式跑生产，出了问题排查起来非常痛苦。

MsRaffaelMarchand

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一句话总结：如果你是 Kong 老用户，AI Gateway 是自然延伸，值得升级；如果你是新人想搞 AI 网关，轻量级方案更香。

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