Why serving architecture affects latency and cost in MLOps - Why It Works

Introduction

Serving architecture is how your machine learning model is made available to users or applications. The way you set it up changes how fast responses come back (latency) and how much money you spend (cost).

When you want your app to respond quickly to user requests with predictions

When you need to handle many prediction requests at the same time without delays

When you want to save money by using resources efficiently during low traffic times

When you must balance between fast responses and keeping cloud costs low

When you plan to scale your model serving as your user base grows

Commands

This command deploys your model serving setup on Kubernetes. It starts the pods that will handle prediction requests.

Terminal

kubectl apply -f model-serving-deployment.yaml

Expected Output

deployment.apps/model-serving created

This command checks the status of the pods to make sure your model serving is running.

Terminal

kubectl get pods

Expected Output

NAME                                READY   STATUS    RESTARTS   AGE

model-serving-deployment-5d7f9c7d9f-abc12   1/1     Running   0          30s

This command sends a prediction request to your model server to test latency and response.

Terminal

curl -X POST http://localhost:8501/v1/models/my-model:predict -d '{"instances": [1.0, 2.0, 5.0]}'

Expected Output

{"predictions": [0.75]}

This command increases the number of pods serving your model to handle more requests and reduce latency.

Terminal

kubectl scale deployment model-serving-deployment --replicas=5

Expected Output

deployment.apps/model-serving-deployment scaled

→

--replicas - Sets the number of pod instances to run

This command shows the current number of pods and their status after scaling.

Terminal

kubectl get deployment model-serving-deployment

Expected Output

NAME                      READY   UP-TO-DATE   AVAILABLE   AGE

model-serving-deployment   5/5     5            5           2m

Key Concept

If you remember nothing else from this pattern, remember: the way you set up your model serving affects how fast predictions come back and how much you pay for computing resources.

Common Mistakes

Running only one pod for model serving under heavy load

This causes slow responses because one pod cannot handle many requests at once, increasing latency.

Scale the deployment to multiple pods to share the load and reduce latency.

Keeping many pods running even when traffic is low

This wastes resources and increases cost because you pay for unused computing power.

Use autoscaling to adjust the number of pods based on traffic automatically.

Deploying the model on a large machine without considering request volume

You might pay more than needed if the traffic is low, or get slow responses if traffic is high and resources are insufficient.

Match your serving resources to your traffic patterns and scale as needed.

Summary

Deploy your model serving using Kubernetes to make it available for predictions.

Check pod status to ensure your model is running and ready to serve.

Send test prediction requests to measure latency and correctness.

Scale the number of pods to handle more requests and reduce latency.

Balance scaling to avoid unnecessary costs during low traffic.