aiqbench analysis
This presentation details DoorDash's strategic evolution in building an internal Generative AI platform, covering critical architectural decisions such as transitioning from proprietary vendor models to open-weight options and managing LLM agent gateways. The discussion highlights practical trade-offs between accuracy, latency, and cost for a large-scale enterprise deployment involving thousands of users.
Presentation: Building GenAI Platform at DoorDash

Swaroop Chitlur and Sidd Kodwani share DoorDash’s journey building an internal GenAI platform. They discuss core architectural bets, transitioning from vendor-first setups to open-weights models, navigating LLM and agent gateways, and balancing accuracy, latency, and cost for over 5,000 internal users. By Siddharth Kodwani, Swaroop Chitlur
Swaroop Chitlur: My name is Swaroop. This is Sidd. We started this team called GenAI Platform at DoorDash, and this is sharing our story about all the things that we did to make a successful platform. We talk about the principles, the bets, the pivots, and what do we mean by success. How many of you are shipping GenAI-based projects at work? How many of you are in a platform type of function where you are supporting other teams be successful at this? Hopefully some of the things we talk about will resonate with you all. Our journey actually starts in roughly April '23. As you know, November '22 was the ChatGPT moment. Then a few months later, we were like, we need to start using OpenAI. I was tasked with signing this contract, and I remember I was sweating, like, are we really going to spend this much money? Of course, in hindsight, that number seems minor now. The feeling was very real because at that moment, I was like, if this thing takes off, how do we support this in such a large company? How do we make this productive? How do we make this accountable? That set…
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