3. What is Unity AI Gateway | How to Control AI Spending

Welcome to the Agentic AI Explained series. This is video 3 of 10, covering the key building blocks behind modern AI agent systems. This video covers Databricks Unity AI Gateway, a centralized governance layer for the runtime interactions between models, agents, MCP servers, and tools. It gives developers flexibility to use different AI models and services while giving platform teams one place to control access, traffic, cost, and behavior. 🔗 Learn more about Unity AI Gateway: https://docs.databricks.com/aws/en/ai-gateway/ 🔗 GitHub repo for this series: https://github.com/viktoriasemaan/agentic-ai-explained-labs The video covers 4 key areas: ▪️ Access and routing: control which models and AI services teams can use, and route requests across different model providers and destinations. ▪️ Cost controls: apply rate limits, track usage, set budgets, and enforce spend thresholds and hard caps. ▪️ Policies and guardrails: apply service policies to requests and responses based on who is calling and what the interaction contains. ▪️ Monitoring and auditing: track requests, token usage, latency, and cost, while logging requests and responses for monitoring and debugging. The result is a consistent governance layer across AI traffic, so teams can use different models and tools without creating a separate set of controls for every provider or application.