As more teams start weaving generative AI (GenAI) into their apps and workflows, Kubernetes naturally comes up as the go-to platform. It’s a tried-and-tested solution for managing containerized workloads, but AI workloads are a different beast. 

Here’s a rundown of what you should think about—and which tools can help—when running AI workloads in cloud-native environments.

  1. GenAI Workloads Need Event-Driven Infrastructure

GenAI features often hinge on user prompts, streaming data, or background jobs. That means you need infrastructure that’s reactive, scalable, and lean.

Together, they give you a nimble setup that reacts fast and keeps infra costs manageable.

  1. Things to consider when serving LLMs in Cloud-Native Environments

Cloud-native tooling provides robust building blocks to tackle the considerations below. The key is integrating scalable serving, observability, and DevOps best practices into your AI stack.

  1. MLDevOps is Growing Up: It’s Not Just About Models Anymore

With GenAI, it’s not just about models anymore. We’re now managing prompts, routing, evaluation loops—and all of it needs version control, observability, and automation.

  1. PromptOps- Prompts as Versioned Artifacts: Treat prompts like you treat code. Use Gitops tools like Argo CD to manage prompt templates. Deploy and validate with Ci/CD tools like Argo workflows and monitor using prometheus and/or grafana. You can utilize KServe to dynamically serve versioned prompts. 
  2. Shadow Deployments for GenAI: Deploy new prompts or models in the background, monitor behavior, then roll out by using Istio, Knative(handles requests route and scale) and KServe(which is built on Knative and adds model lifecycle and inference management)  that helps with Traffic routing, traffic splitting and shadow support. 
  3. Evaluation Pipelines- Automate Model and Prompt Testing: Keep quality high with continuous evaluation. Utilize MLflow/Weights and biases to log prompts and model changes. Also, Kubeflow pipelines to manage ML-native workflows.

Final Thoughts

Running AI on Kubernetes isn’t just possible, it’s powerful. With the right tools, you can treat prompts, models, and GenAI services just like any other production-grade software component. But doing so requires a mindset shift: prompts aren’t just strings, they’re assets. And evaluations aren’t just test scripts, they’re pipelines. Lean into the cloud-native ecosystem and let your AI workflows evolve alongside your infrastructure.