# The model is the easy part: Building the LLM Platform at Whatnot

DevFeed: [The model is the easy part: Building the LLM Platform at Whatnot](<https://devfeed.tech/articles/the-model-is-the-easy-part-building-the-llm-platform-at-whatnot-23714.md>)

Original publisher: [Read original article](<https://medium.com/whatnot-engineering/the-model-is-the-easy-part-building-the-llm-platform-at-whatnot-ec8730fa9bdf?source=rss----162aeca881b0---4>)

Author: Whatnot Engineering

Published: 2026-04-14T15:01:05Z

Content type: article

Language: en

Sources: [Whatnot Engineering](<https://devfeed.tech/sources/whatnot-engineering.md>)

Topics: [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Tooling](<https://devfeed.tech/topics/tooling.md>)

Tags: [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [building](<https://devfeed.tech/tags/building.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-evaluation](<https://devfeed.tech/tags/llm-evaluation.md>), [llms](<https://devfeed.tech/tags/llms.md>), [platform](<https://devfeed.tech/tags/platform.md>), [platform-engineering](<https://devfeed.tech/tags/platform-engineering.md>), [production](<https://devfeed.tech/tags/production.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [quality](<https://devfeed.tech/tags/quality.md>), [teams](<https://devfeed.tech/tags/teams.md>), [trust](<https://devfeed.tech/tags/trust.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

## AI overview

This article explains that building an LLM platform involves much more than calling a model. At Whatnot, the platform is organized around reliability, velocity, and trust, supported by existing data, logging, analytics, integration, and internal tooling foundations.

## Source excerpt

Stas Sajin, Faithful Alabi, Peiyun Zhang, Peicheng Yu | AI Platform Introduction A decade ago, one of the more useful ways to explain machine learning systems was with the diagram below: the ML model itself was a tiny box in the middle, surrounded by everything else you actually had to build to make it work in production. Figure 1: Complexity surfaced by ML systems. Source. The same thing is happening with LLMs now. Making the API call is the small box, maybe even smaller than the one in that original diagram. Calling the model is the easy part. The hard part is everything around it: there is less stable ground truth, inputs are harder to constrain, outputs are non-deterministic, and the system is much easier for users to push in unintended directions. The hard part is giving teams the ability to iterate fast, trust that the system is working, and know that it is getting better. From our perspective, this is what an LLM platform actually has to solve for. It has to be reliable enough to support real product and operational workflows. It has to enable velocity, because many of the highest-leverage improvements are small changes that need to move quickly. And it has to create trust, so teams can understand output quality, catch regressions, and ship with confidence. These pillars reinforce each other. Reliability makes teams willing to depend on the platform in production. That production usage creates the data and feedback loops needed to build trust. And trust, in turn, makes it much easier for teams to move with velocity, because they can tell whether a change actually helped. The rest of this post is about those three strategic pillars and the concrete actions behind each of them that allowed us to build the LLM Platform at Whatnot. Figure 2: The LLM platform is organized around three self-reinforcing pillars: velocity, reliability, and trust, each supported by a distinct set of platform enablers.We built on foundations that were already there A big reason we were