Head-to-head comparisons
The pairs security and platform teams weigh against each other, on the same criteria as the main table, each ending in which one suits whom.
MCP gateway vs runtime security
What an MCP gateway enforces at access time, what runtime detection catches during tool calls, and when an AI agent deployment needs both.
Read the comparison →Build in-house vs Adopt a platform (Runlayer)Build in-house vs adopt a platform for AI agent security
Custom auth and access plumbing for AI agents compared with adopting a platform, on deployment effort, coverage of agent threats and ongoing maintenance.
Read the comparison →Shadow AI discovery vs Blanket blockingShadow AI discovery vs blocking
Detecting and remediating unsanctioned AI agent and MCP use compared with blanket restriction, including what each does to adoption.
Read the comparison →MCP gateway vs API gatewayMCP gateway vs API gateway
Why an existing API gateway does not cover MCP traffic from AI agents, and what an MCP gateway adds: tool-level policy, brokered OAuth and agent attribution.
Read the comparison →Sandboxing vs Runtime monitoringSandboxing vs runtime monitoring for AI agents
Isolating AI agents in containers or microVMs compared with monitoring their tool calls at runtime: what each stops, what each misses.
Read the comparison →Prompt guardrails vs MCP gatewayPrompt guardrails vs MCP gateway
An LLM firewall that classifies prompts and responses compared with an MCP gateway that controls tool access: different layers, different threats.
Read the comparison →Agent identity and secrets vs MCP gatewayAgent identity vs MCP gateway
Giving AI agents their own identities and short-lived credentials compared with routing their tool calls through an MCP gateway.
Read the comparison →Runlayer vs Docker MCP GatewayRunlayer vs Docker MCP Gateway
Runlayer and Docker MCP Gateway compared on visibility, authentication, credentials, policy, runtime detection, audit and deployment, from each one's public documentation.
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