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.
- 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.
- Knative Serving: Great for HTTP-based GenAI services (like LLM APIs). It automatically scales up when requests come in, and scales to zero when they don’t. Perfect for saving money on GPU-bound workloads.
- KEDA: Adds event-driven autoscaling based on external sources like Kafka, RabbitMQ, or Prometheus. It complements Knative by widening the scope of what can trigger scaling.
Together, they give you a nimble setup that reacts fast and keeps infra costs manageable.
- 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.
- Autoscaling Sensitivity: Traffic to LLMs can spike unpredictably. Autoscalers must be finely tuned with custom metrics.
- KEDA: Great for event-based scaling—think queue lengths or custom metrics like token usage.
- Knative Serving: Scales based on actual HTTP traffic, and goes to zero when idle—perfect for APIs.
- VPA: Helps tune your pods’ CPU and memory requests automatically.
- KServe: Built with ML in mind, it supports scaling on things like inference throughput or request count.
- Observability & Debugging: GenAI behaviors are opaque. You need metrics, traces, and feedback to understand what’s working.
- OpenTelemetry: The go-to for collecting logs, metrics, and traces all in one.
- Prometheus: System monitoring and alerting toolset.
- Prompt and Model Drift: Just like model drift, prompts can become stale or produce inconsistent outputs.
- Evidently AI: Great for keeping tabs on prompt or input drift with real-time monitoring.
- Langfuse / PromptLayer: Track how prompts perform and evolve—like analytics for your LLM calls.
- LLMGuard: Adds guardrails around your outputs to keep them on track and safe.
- Resource Intensity: Consider smart scheduling to avoid resource waste. LLMs are GPU-hungry and memory-heavy.
- Karpenter or Cluster Autoscaler: Automatic node provisioning (like GPU-powered ones) when your workloads need them.
- Kubeflow Pipelines: Deploying scalable ML workflows using containers on kubernetes based systems.
- Kueue: It manages queues for batch jobs, especially when resources are tight. Need four GPUs for one big job? Kueue would ensure all resources are in place before the workload starts.
- Kubernetes GPU Scheduling (Device Plugins): You can fine-tune pod placement using labels and selectors. It will let you request specific specific sources using selectors and tolerations.
- 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.
- 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.
- 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.
- 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.