Top Tools for Serving Open-Source Models on Kubernetes
Serving open-source models on Kubernetes requires much more than one tool. Ha! It’s more like a full-fledged orchestra. You need a model runner, model deployment tools, cluster management, and a few other components to get everything working together on your GPU.
The best choice for each of these jobs depends on what you’re serving. Is it one large model? Or many small ones?
In this quick-to-digest guide (a shoutout to our short attention spans 😁), I will walk you through each job, the main open-source tools for it, and the trade-offs you should consider.
If you only need a ranking of inference servers, I’ve already covered that in Top Kubernetes-Native Inference Servers Ranked (2026).
5 Layers of a Kubernetes Serving Stack
Most rankings put all the tools on one list, but that makes them hard to compare because they do different jobs:
What it does
Tools in this guide
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