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SpeakeasyvsKong

Kong governs the requests that cross its data plane, with AI plugins for LLM, MCP, and agent-to-agent traffic. Speakeasy runs inside the agent: hooks see the prompt, the tool call, and the shell command before they run. Then it governs the gateway too.

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Which should you pick, Speakeasy or Kong AI Gateway?

Pick Kong when you already run Kong, when your agents are server-side applications whose traffic reliably passes through a data plane you operate, or when turning REST APIs into MCP tools at the gateway is the main job. Pick Speakeasy when AI is rolling out across the whole company, the agents are coding agents on laptops, or the question is whose directory identity a tool call ran as. Speakeasy governs the loop and the gateway; Kong stays the API management layer for the services behind it.

Pick it when

AI is rolling out across the whole company and you want one control plane that plugs into the agents, identity provider, and tools your teams already use, and governs every step of the loop as well as the gateway.

You already run Kong, your agents are server-side applications whose traffic reliably passes through a data plane, or turning REST APIs into MCP tools is the main job.

Governance

Agents run as principals in the identity provider you already have, Okta or Entra, with the grant checked on every call.

Token claims from any OAuth 2.0 server map to a Kong consumer when the request reaches the data plane, with consumer-group ACLs per tool. A call that bypasses the gateway carries no identity.

Security

Hooks inside the agent on the prompt, the tool call, the file read, and the shell command, plus six inspection layers at the gateway, on macOS, Windows, and Linux.

Prompt Guard, Semantic Prompt Guard, AI Sanitizer, and provider guardrails on the requests that cross the data plane. The prompt inside the agent, the local call, and the shell command never produce one.

Enablement

Your agents, your identity, your APIs. MCP servers are generated from the API contract your teams already maintain, with evidence to Drata, Vanta, and your SIEM.

Everything runs as plugins on the gateway you already operate, across LLM, MCP, and agent-to-agent traffic, with REST APIs turned into MCP tools at the gateway.

Kong AI Gateway capabilities are drawn from Kong's public documentation as of September 2026. We build Speakeasy, so read us as an interested party. Kong remains the API management layer for many Speakeasy customers; this page compares what each sees of an agent. Read what AI agent hooks are.

Who is the agent, at every step?

Both decide per call whether a request proceeds. Speakeasy attaches a principal from your directory inside the loop, so the prompt, the local call, and the shell command run as someone too. Kong maps token claims to a consumer when a request reaches the data plane, and a call that bypasses it carries no identity.

Speakeasy
  1. OktaMicrosoft Entra IDOkta or Entra
  2. Directory principal
  3. Grant checked per call
  4. Call stamped with directory state

Identity lives in

Your identity provider

  • Directory identity attached inside the agent, on every step
  • People and agents share one permission model
  • A denied call opens the grant that would allow it
Kong
  1. Any OAuth 2.0 authorization server
  2. Token claims mapped to a Kong consumer
  3. Consumer-group ACL per tool
  4. Request logged at the data plane

Identity lives in

Kong consumers, at the data plane

  • Per-tool allow and deny lists on ai-mcp-proxy; deny wins
  • A call that bypasses the gateway carries no identity at all

You can only inspect what you can see.

From the data plane, Kong sees the request: prompt guards, a PII sanitizer, and provider guardrails run on the model traffic routed through it. Speakeasy's hooks apply policy to the prompt before inference, the local tool call, the file read, and the shell command, on macOS, Windows, and Linux, and the gateway scans the rest.

7 inspection layers
  • 01Hooks inside the agent: prompt, tool call, file read, shell command
  • 02Server review with evidence dossier
  • 03Prompt-injection heuristic
  • 04LLM judge
  • 05Natural-language guardrails, replayed before enforcement
  • 06CEL detection rules
  • 07Destructive-action detection
4 inspection layers
  • 01AI Prompt Guard (regex), at the data plane
  • 02AI Semantic Prompt Guard, at the data plane
  • 03AI Sanitizer (PII), at the data plane
  • 04Bedrock Guardrails, Azure Content Safety

Prompts before the model sees them

Hooks in five coding agents
Model traffic routed through the proxy via ANTHROPIC_BASE_URL

Local tool calls, file reads, shell commands

PreToolUse hooks, destructive-action detection
Never reach the gateway

Endpoint enforcement

macOS, Windows, Linux
No endpoint agent or in-agent hooks documented

Tool calls at the gateway

Inline scanners on request and response
ai-mcp-proxy allow and deny lists; AI Guardrails not supported on it

Shadow MCP discovery on endpoints

Traffic-sourced inventory, device agent

OAuth for MCP servers

OAuth 2.1 server: PKCE, DCR, consent
Token validation; DCR and PKCE not documented

Sensitive-data detection on model traffic

Secrets, financial, PII, government IDs, healthcare
AI Sanitizer, Bedrock, Azure Content Safety

Agent-to-agent traffic governance

Not documented
Agent Gateway
  • Offered
  • Partial, beta, or detect only
  • Not offered or not documented

Govern what your team already runs.

Speakeasy governs the agents your teams already run, the directory you already have, and the APIs your teams already maintain, from the inside. Kong governs what you route through its data plane, and turns your APIs into MCP tools there.

Speakeasy
Your agents
Claude CodeClaude CoworkCursorCodexOpenCode
Your identity
OktaMicrosoft Entra ID
Your stack
GitHubSlackLinearNotionSnowflakeJamf
  • MCP servers generated from the API contract your teams maintain
  • Catalog drawn from the official MCP Registry
  • Coverage evidence to Drata and Vanta, events to your SIEM
Kong
Traffic
LLM (ai-proxy)MCP (ai-mcp-proxy)A2A (Agent Gateway)
Tools
OpenAPI to MCP toolsMCP Registry (tech preview)
Control plane
Konnect AI ManagerSelf-hosted data planes
  • Everything runs as plugins on the gateway you already operate
  • Coding agents are governed by routing their model traffic through the proxy
  • No endpoint agent or in-agent hooks documented

The MCP server we built using Speakeasy just works. It made becoming AI-native much simpler than we expected.

Constantine Nathanson

Staff Software Engineer, Cloudinary

Cloudinary

Questions

Can an API gateway govern AI agents?

It governs the part of the agent it can see. A gateway sits on the network path and applies policy to the HTTP requests that pass through it, which for a coding agent means model calls routed through the proxy and calls to remote MCP servers fronted by it. The agentic loop is different: the agent reads files, runs shell commands, edits code, and calls local stdio tools, and none of that produces a request the gateway can inspect. Governing the loop needs a hooks intercept inside the agent, which is what Speakeasy's agent hooks do, with the gateway as the second enforcement point.

Does Kong support MCP?

Yes. Kong's AI MCP Proxy plugin runs in four modes, proxying upstream MCP servers or converting REST APIs into MCP tools, with per-tool allow and deny lists by consumer and consumer group. The AI MCP OAuth2 plugin validates tokens by introspection or JWKS and maps claims to consumers. Kong's Agent Gateway extends this to agent-to-agent traffic, and Konnect adds an MCP Registry in tech preview. Kong's documentation notes that AI Guardrails are not supported on the MCP Proxy plugin, and none of it runs on developer endpoints.

Does Speakeasy replace Kong?

No. Kong stays the API management layer for the services behind it. Speakeasy governs the agent: identity from your directory on every session, hooks inside Claude Code, Cursor, Codex, Cowork, and OpenCode, shadow MCP discovery and enforcement on endpoints, and an MCP gateway that authenticates, authorizes, scans, and logs every tool call. Governed MCP servers generated from your API contracts can sit behind Kong like any other upstream.

When is Kong a better choice than Speakeasy?

Pick Kong when your agents are server-side applications whose traffic reliably crosses a data plane you already operate, and the job is applying the same consumers, rate limits, prompt guards, and observability to LLM, MCP, and agent-to-agent traffic. If the agents are on laptops, or the question is whose directory identity a tool call ran as, the control plane is the right layer.

Why do hooks matter for governing AI agents?

A coding agent reads files, runs shell commands, edits code, and calls local tools, and none of that produces a request a proxy can inspect. Hooks are lifecycle handlers the agent itself invokes on every prompt and tool call, with the full session context, before execution proceeds. Speakeasy runs hooks in Claude Code, Cursor, Codex, Cowork, and OpenCode, which is how it sees and governs the loop rather than only the traffic that leaves it.

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Both run hooks inside the agent. They differ on whose identity the loop runs as and who authors detection.

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Speakeasy vs LiteLLM

A proxy sees the model call. Hooks see the agent think. The two run together, with LiteLLM kept as the model proxy.

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Speakeasy vs Microsoft

Microsoft governs agents on its path. Hooks govern the agents your teams already run, on every OS, as the same Entra principal.

Read the comparison

AI everywhere.

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