


TrueFoundry AI Gateway is a production-ready control plane designed to help teams experiment with, monitor, and govern AI agents at scale. It provides a unified interface for connecting all agent components—including models, MCPs, guardrails, prompts, and agents—within a single playground environment. The platform delivers complete visibility into agent responses through traces and health metrics, while enabling governance through configurable rules on request volumes, costs, and response content. Already powering thousands of agents in production across multiple Fortune 100 companies, TrueFoundry AI Gateway is built for enterprise-grade reliability and control.
Connect and test all agent components—models, MCPs, guardrails, prompts, and agents—in a single interactive environment. This allows teams to iterate quickly on agent designs before moving to production.
Maintain full visibility into every agent response with detailed traces and health metrics. Track performance, debug issues, and understand exactly how your agents are behaving in real time.
Set rules and limits on request volumes, cost thresholds, and response content through guardrails. This ensures agents operate within defined boundaries for compliance, security, and budget control.
Built to handle thousands of agents simultaneously, with proven deployment at Fortune 100 companies. The platform is designed for high availability and low latency in demanding enterprise environments.
"The only control plane that lets you experiment, monitor, and govern agents from a single interface—already proven at Fortune 100 scale."
TrueFoundry AI Gateway differentiates itself by combining experimentation, observability, and governance into one cohesive platform. Most solutions force teams to piece together separate tools for prototyping, monitoring, and policy enforcement. This unified approach reduces complexity and accelerates the path from agent concept to production deployment, all while maintaining the control that enterprise environments demand.
You're managing multiple AI agents across your organization and need a centralized way to experiment with configurations, monitor performance, and enforce governance policies. It's especially valuable for teams scaling from pilot projects to production deployments, or for organizations that require strict cost controls, content filtering, and audit capabilities for their AI systems.
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