Why Serverless Inference Consistency Varies on the Same Model

Introduction

Imagine you’re selecting an LLM for your application. You do extensive research on which model will work best for your use case. You might experiment with it in a sandbox using DigitalOcean Serverless Inference, find it works well, then commit to another provider for that model to integrate into your app. After pushing to production, the model’s accuracy, time to first token (TTFT), and throughput are all worse than you’d hoped. It was the same model, so what could have happened?

The answer is that models are not all treated equally across platforms. One platform may dedicate their best GPUs to one set of models, when another platform focuses their best hardware on a different set of models. Even if the platform offers a model, it may not have the necessary resources behind the scenes to make it production-worthy. Behind every API endpoint, providers are making a series of infrastructure decisions, such as how many replicas to keep warm, what precision to serve the model at, which GPU tier to allocate, and how to prioritize request queues. These decisions are rarely documented, and they vary significantly from provider to provider and from model to model on the same provider.

This article explains what providers actually control, why model popularity shapes those decisions, and most importantly, how to measure it yourself before committing a model and provider combination to production.

The benchmark data in this article comes from internal testing we conducted to validate these patterns. The provider names are withheld, but the methodology is described in enough detail that you can reproduce the same kind of comparison yourself.