Technology Comparison

Cequence AI Gateway vs.
Kong: Purpose-Built Security vs. API Gateway Plugins

Kong is one of the most widely deployed API gateways in the enterprise. As agentic AI adoption accelerates, Kong has added AI and MCP capabilities through plugins on top of its existing gateway platform. Cequence AI Gateway was built from the ground up to govern the connection between AI agents and enterprise applications. Both products now route MCP traffic. They are not the same thing.

The Core Distinction

Cequence AI Gateway Kong AI Gateway
What it is Purpose-built AI gateway for governing how agents interact with enterprise apps and data via MCP. General-purpose API gateway with AI and MCP plugins added (3.12+).
Where it sits Between AI agents and your backend services. Between any client and any service. AI/MCP is one of many use cases.
Boundary Agent-to-application (security-first) API traffic management (infrastructure-first)
“Kong is an API gateway that added AI. Cequence is an AI gateway built for security.”

Which Problem Are You Actually Solving?

Scenario A: You already run Kong and want to add MCP routing. Kong’s MCP Proxy plugin handles this. The question is whether routing is enough, or whether you also need agent-scoped governance, behavioral detection, sensitive data inspection, and a trusted MCP server registry that your security team controls.
Scenario B: You are deploying agentic AI and need governed access to enterprise applications. You need purpose-built governance, not plugins on a general-purpose gateway. This is Cequence.
Scenario C: Both. Kong manages your API traffic. Cequence governs your MCP traffic. They coexist.

When to Use Each Technology

Use Cequence AI Gateway When:

Your AI agents need governed access to enterprise applications and data, and you need to control what they do at the tool-call level.
 
Scenario 1: Making Private APIs and SaaS Apps Agent-Ready
Three paths: convert from OpenAPI spec, import from the application protection platform, or import remote official MCP servers into the governed registry. Prebuilt tools for common enterprise SaaS. Kong can only generate MCP from Kong-managed REST APIs. No prebuilt SaaS connectors.
 
Scenario 2: Agent Job Descriptions (Personas)
Cequence Agent Personas scope each agent to the intersection of user permissions and allowed tools. The other tools are invisible. Kong has no concept of agent-level tool scoping.
 
Scenario 3: Behavioral Forensics on Agent Sessions
Cequence reconstructs the full sequential trail of tool calls, identifies behavioral patterns, and produces targeted recommendations. Kong provides MCP traffic metrics, not behavioral forensics.
 
Scenario 4: Sensitive Data Detection and DLP (Beta)
Real-time inspection of MCP tool call payloads with compliance-mapped detection. Native to Cequence, no separate module. Kong’s PII plugins are designed for LLM prompts, not MCP payloads.

Use Kong AI Gateway When:

Your platform engineering team needs a unified control plane for API and AI traffic.
 
Scenario 1: Extending Your Existing Kong Deployment
You already run Kong Enterprise or Konnect. Kong’s MCP Proxy plugin adds MCP routing within your existing infrastructure.
 
Scenario 2: LLM Routing and Cost Management
Multi-provider LLM routing with fallback, load balancing, and token spend tracking via AI Proxy plugin.
Cequence AI Gateway governs LLM calls too, through LLM Registry: credential brokering, model allowlisting, spend/rate limits, and Prompt Guard, unified with the same Agent Persona and audit trail that already governs MCP and API traffic.
 
Scenario 3: Unified API and AI Traffic Management
A single gateway for both traditional API traffic and AI/MCP traffic. 300+ plugin ecosystem.

When You Need Both

Kong manages your API infrastructure. Cequence governs what AI agents do with your enterprise applications. One is infrastructure. The other is security governance. They are complementary.

Detailed Capability Comparison

Capability Cequence AI Gateway Kong AI Gateway
Primary function Governed connection between agents and enterprise apps/data via MCP. General-purpose API gateway with AI/MCP plugins.
Architecture Purpose-built for agentic AI security. MCP-native. Plugin-based. MCP added via AI MCP Proxy plugin (3.12+).
API to MCP conversion No-code from OpenAPI spec. Import specs from Cequence application protection platform. Prebuilt tools for common enterprise apps. Generate MCP from Kong-managed REST APIs via plugin. No prebuilt connectors for third-party SaaS.
Remote MCP server import Import remote official MCP servers into governed registry. Centrally managed, governed, monitored. No. Proxies MCP servers but does not import into a governed registry.
Enterprise MCP registry Centralized trusted registry. No shadow MCP. No agent-discovered endpoints. No centralized registry. MCP servers managed as Kong services/routes.
API registry Brokers API credentials so agents never hold them directly. Every call runs through the same policy-enforced identity as MCP and LLM traffic. Partial. Kong manages API keys and OAuth via plugins, without persona-bound credential brokering.
Skills registry Curated, vetted, reusable agent capabilities that security and platform teams approve once and reuse across every agent. No equivalent. Capabilities are assembled per use case from the plugin ecosystem.
AI/LLM/shadow discovery AI Discovery surfaces every agent, LLM provider, and MCP server running across the enterprise, pulled from existing SIEM logs, whether or not it went through an official process. No built-in discovery of shadow agents or shadow LLM usage. Services/routes must be known and configured.
Agent Personas Per-user, per-tool, and per-model scoping. Job descriptions that bind each agent's tools, APIs, and approved model to one policy-enforced identity — not separate plugin configuration per capability. No. No agent-level tool scoping or purpose-based governance.
Sensitive data / DLP Native. Real-time inspection across MCP tool calls, API requests, and LLM prompts and responses, including base64 and evasion-character detection — built in, not a separate plugin to configure. PII sanitization via Prompt Guard/Response Guard plugins. Designed for LLM prompts, not MCP payloads.
Prompt injection protection Prompt Guard detects prompt injection, jailbreak attempts, and system-prompt extraction on every LLM prompt and response. Partial. Guard/Response Guard plugins detect injection in LLM prompts, assembled per use case.
Behavioral detection Sequential tool call forensics. Full agent behavioral trail. 10+ years of API attack data. MCP traffic metrics (Prometheus). Usage monitoring, not forensics.
LLM routing Partial. Brokers provider credentials, allowlists approved models per Agent Persona, and enforces spend and rate limits with Prompt Guard and DLP on every call. Yes. AI Proxy plugin. Multi-provider routing, fallback, load balancing.
Enterprise IdP OAuth 2.1. Okta, Entra ID, Google. Two-layer credential isolation. OIDC, JWT, ACL plugins. OAuth 2.0. Broad auth plugin ecosystem.
Deployment SaaS or self-hosted (Kubernetes). Self-hosted Enterprise, managed SaaS (Konnect), or cloud marketplace.
Plugin ecosystem Purpose-built. No plugin assembly required. 300+ plugins. AI capabilities assembled from multiple plugins.

Security Considerations

Standards Authorship

Cequence co-authors CIS Controls companion guides for AI Agent and MCP environments with the Center for Internet Security, and co-chairs TM Forum’s Agentic Interaction Security initiative across 800+ member organizations. Three consecutive Verizon DBIRs (2023-2025). Kong has no comparable standards authorship in agentic AI security.

Plugin-Composed Security vs. Purpose-Built Security

Kong’s AI security is assembled from plugins: AI MCP Proxy, AI MCP OAuth2, Prompt Guard, Response Guard, PII sanitization. Each configured independently. Security posture depends on correct assembly. Cequence’s security is integrated by design. Personas constrain visibility, behavioral detection watches actions, sensitive data inspection catches extraction. Not a plugin chain. A security architecture.
Skill Registry applies the same purpose-built model to reusable agent capabilities: a curated, vetted set of instructions that security and platform teams approve once and reuse across every agent, rather than assembling and maintaining a chain of plugins per use case.

Agent Identity vs. Agent Purpose

Kong authenticates agents via OIDC/JWT. It answers “is this agent authorized to connect.” Cequence Personas answer “what is this agent’s job, and is it doing only that job right now.” A coerced agent that can only see two read-only tools cannot exfiltrate from the other 16.

Prompt Injection Containment

The “Agents of Chaos” study compromised all six test agents via social engineering. DeepMind achieved 86% attack success rates. Kong’s Prompt Guard detects injection in LLM prompts. Cequence ensures a coerced agent can only see the tools its Persona permits. When detection fails, containment prevents material harm.

Shadow AI and Agentic Discovery

AI Discovery surfaces every agent, LLM provider, and MCP server running across the enterprise from existing SIEM logs, whether or not it went through an official process — visibility that does not depend on services and routes being known and configured in Kong first.

Case Study: When an Agent Goes Rogue to Get the Job Done

Environment: Fortune 50 enterprise. Autonomous AI coding agent. 47 continuous hours. 2,575 tool calls. Entirely unsupervised.
What actually happened: The agent guessed 162 filenames. None existed. It hallucinated commit hashes over 71-second loops. It re-probed wrong paths across 27 hours with no memory between sessions.
This is not a malicious agent. It is a determined one.
What Cequence did: Reconstructed the full behavioral trail. Identified six error clusters. Projected error reduction from 212 to under 20 per 48-hour window.
Kong would show MCP traffic metrics: request counts, latency, error rates. It would not reconstruct the sequential behavioral trail.

Summary

Kong is the most widely deployed API gateway in the enterprise. It excels at API traffic management, LLM routing, rate limiting, and multi-protocol support. For platform teams that already run Kong, the AI MCP Proxy plugin is a reasonable extension.
 
But MCP routing is table stakes. The hard part is the security controls: Agent Personas that scope each agent to its specific job, behavioral forensics that reconstruct what agents actually did, sensitive data detection across MCP, API, and LLM traffic, and a trusted registry that eliminates rogue MCP servers — none of it assembled from a plugin. Kong manages your API infrastructure. Cequence governs your agentic AI security. They can coexist.