A year ago, Palo Alto Networks built dashboards to track AI spend. Today those dashboards are useless, and the team that built them thinks that's the whole story. Recorded at FinOps X in San Diego, this conversation brings together Abhinav Lad, who leads cloud and AI finance at Palo Alto Networks, and Kuntal Patel, who runs the cloud engineering function behind it. They explain what happened when agents entered the picture, and AI stopped behaving like a service anyone could forecast.

This blog sketches the AI stack as it’s emerging from real‑world practice and invites the community to refine it. The goal is to create a shared, peer‑validated blueprint for anyone building AI systems, shaped by real experiences, not marketing claims.

When enterprises build AI agent platforms, selecting the right gateway is critical for routing models, models tools (like MCP servers), and securing traffic. While LiteLLM is widely recognized for its extensive provider catalog and unified OpenAI-compatible API, it often falls short in enterprise evaluations because crucial security and platform controls—such as SSO beyond five users, secret manager integrations, scoped guardrails, and audit logging—are locked behind a proprietary enterprise license. Conversely, agentgateway (an Apache 2.0 open-source project hosted by the Linux Foundation) serves as a robust, general-purpose data plane for HTTP, gRPC, and LLM traffic, providing enterprise-grade security features without embedded license gates. Furthermore, agentgateway boasts a stronger public security record, avoiding the critical vulnerabilities that have plagued LiteLLM. For teams that need a true microservice gateway for AI services rather than just an expansive SDK, agentgateway presents a more secure, open, and scalable architectural choice.