MCP vs API Gateways: Why AI Agents Need Governance Beyond APIs
API gateways like Kong, Apigee, and Azure APIM are excellent at what they were built for: securing and managing perimeter APIs. They enforce authentication, rate limits, and policies for north–south traffic — external clients calling services through REST endpoints.
But with the rise of AI agents and the Model Context Protocol (MCP), enterprises face a new challenge: agent-to-tool governance. This is not something API gateways were designed to cover. MCP introduces new flows, new risks, and new governance requirements that demand a dedicated MCP governance layer. That's where Palma.ai comes in.
MCP Protocol vs API Gateways: MCP Governance Layer vs Edge Security
MCP isn't "just another API." It uses JSON-RPC instead of REST, supports bidirectional and sessionful flows, and exposes tools dynamically via list_tools. API gateways, in contrast, were built for north–south HTTP traffic at the edge. MCP is about east–west traffic inside the enterprise — where AI agents call tools and services continuously. Governing this requires a protocol-true, MCP-native approach.
Per-Tool Policy Enforcement for AI Agents (Agent-to-Tool Governance)
With MCP, governance happens at the function and parameter level, not just the endpoint. Enterprises need per-tool policy enforcement: defaults like read-only, staged writes, and exceptions tied to departments or roles. This level of AI agent security is essential in regulated environments. Classic API gateways simply don't understand these agent-to-tool semantics.
Human-in-the-Loop Governance Beyond API Gateways (AI Approval Workflows)
Sensitive actions often require human oversight. Palma.ai supports human-in-the-loop approval workflows where an agent can be paused, routed to the right approver, and resumed once approved. This ensures enterprise AI governance with a clear audit trail. Traditional API gateways only allow or deny requests — they lack this pause/resume model that's critical for safe AI agent control.
Identity Mediation Across MCP Servers (Trust Brokering for AI Agents)
Enterprises rarely run a single MCP server. They need to connect many tools, each with its own identity system. Palma.ai provides identity mediation, mapping agent JWTs to OAuth tokens and per-tool scopes across heterogeneous MCP servers. API gateways validate tokens, but they don't broker trust across multiple domains. This MCP governance layer closes that gap.
Cost Guardrails for AI and LLMs (LLM Cost Governance)
When AI agents use large language models, cost governance becomes critical. Palma.ai enforces LLM cost guardrails: token budgets, caching/deduplication, anomaly detection, and intelligent model routing (small → large only when confidence is low). API gateways rely on rate limits, but they don't govern AI usage costs or detect anomalies that can escalate spend.
Why API Gateways Feel "Good Enough" (API Gateway Limitations for AI)
It's common to hear: "MCP is just another API. Our gateway covers that." This feels true in the early days: maybe one MCP server, mostly read-only calls, minimal cost exposure. But once organizations scale to fleets of agents, sensitive writes, and rising token spend, the limits of API gateways for AI become clear. At that point, AI agent governance is no longer optional.
API gateways stop at the HTTP layer. Palma.ai adds per-tool policy, HITL approvals, identity mediation, and cost guardrails for complete AI agent governance
How Palma Complements (Not Replaces) API Gateways (Enterprise AI Governance)
Palma.ai doesn't replace your gateway — it complements it.
- Keep your API gateway at the perimeter for north–south API security.
- Deploy Palma inside your VPC or on-prem for east–west AI agent traffic.
- Reuse your stack: IdP for agent JWTs, secrets in Vault, SIEM for logs, and Teams/Slack for approval workflows.
The result: API gateways secure your edge. Palma secures your agents. Together, they deliver a future-proof MCP orchestration strategy.
Next Step: A Low-Risk MCP Pilot (Audit Logs and Approvals)
The simplest way to see the difference is a two-week pilot:
- Connect two MCP servers
- Apply a read-only baseline
- Allow one write behind approval
- Deliver a unified approval trail and audit logs with cost guardrails
If your existing API gateway can already do this, great. If not, the governance gap will be clear.
Bottom Line
API gateways solve the perimeter problem.
MCP introduces a new one: governing AI agents calling tools inside the enterprise.
Palma.ai is the MCP governance layer built for this reality. It enforces per-tool policies, human-in-the-loop approvals, identity mediation, and LLM cost controls — all while remaining protocol-true to MCP.
👉 Ready to explore? Start with a low-risk pilot and see why enterprises are adopting Palma as their AI agent governance platform.
