How to Reduce LLM Costs with an AI Gateway

AI gateways can control growing production LLM costs by centralizing routing, caching, token limits, fallback handling, and cost attribution between applications and model providers. They route simple tasks to lower-cost models while reserving more capable models for complex work, use semantic caching to serve equivalent repeated queries without new model calls, and impose token-based budgets and rate limits to prevent excessive usage from users, applications, or autonomous agents. Gateways also provide fallback chains that reduce costly retries during provider failures and tag requests by team, application, model, and time period to make spending measurable and actionable. The text argues that these infrastructure-level controls are more effective than application-specific monitoring, particularly as organizations deploy multiple models, services, and AI agents with MCP tool calls. It presents NeuralTrust’s open-source TrustGate as an example of a self-hosted gateway that offers these controls without requiring application code changes.

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