Unified model routing, MCP security, and cost governance help enterprises control AI access, protect data, and manage spending
PointGuard AI announced its AI Gateway, combining high-throughput model routing with AI-native security and cost governance. Integrated with the MCP Security Gateway, the solution helps enterprises block unapproved shadow models, protect sensitive data, contain runaway costs, and scale AI workloads across applications and agents.
We built AI Gateway so enterprises can scale AI with security controls in place, maintain performance, and keep spending accountable.”
— Pravin Kothari, CEO of PointGuard AI
As agents connect to more models, tools, and enterprise resources, organizations need consistent controls over what AI can access and how it operates. In its Market Overview for AI Gateways, Gartner states, “AI gateways are essential components for managing how AI agents access both AI models and Model Context Protocol servers.”¹
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“Every new model, agent, and tool connection introduces decisions about access, data, and cost,” said Pravin Kothari, founder and CEO of PointGuard AI. “We built AI Gateway so enterprises can scale AI with security controls in place, maintain performance, and keep spending accountable.”
PointGuard AI Gateway brings together:
• Approved model routing with automatic failover
• Prompt guardrails and sensitive data protection
• Semantic caching, virtual keys, and budgets
• Identity-aware MCP tool authorization and inspection
Its Java virtual-thread runtime delivered 50 times LiteLLM’s throughput in benchmark testing with thousands of concurrent requests. Hardened software builds and build provenance strengthen supply chain security, while unified LLM and MCP controls govern north-south and east-west AI traffic. Correlated telemetry connects usage, cost, identity, and security events for operational oversight.
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