This content originally appeared on DEV Community and was authored by Juan Manuel Barea Martínez
While most companies focus on building better models, adding new features, and providing new services, many seem to overlook one crucial step: deploying those models through a proper inference service.
Doing so not only addresses key security concerns but also gives better control over the data your models use.
In my second post about deploying models in production environments, I explain how to deploy an LLM on Kubernetes using vLLM and discuss the main challenges and how to overcome them.
If you’d like to dig deeper, check it out here: https://levelup.gitconnected.com/deploying-an-llm-using-vllm-on-production-with-kubernetes-90e0bf225448
This content originally appeared on DEV Community and was authored by Juan Manuel Barea Martínez
Juan Manuel Barea Martínez | Sciencx (2025-11-05T08:01:49+00:00) Deploying an LLM using vLLM on Production with Kubernetes. Retrieved from https://www.scien.cx/2025/11/05/deploying-an-llm-using-vllm-on-production-with-kubernetes-2/
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