GKE vs Cloud Run decision in GCP - Performance Comparison

Time Complexity: GKE vs Cloud Run decision

O(n)

Understanding Time Complexity

When choosing between GKE and Cloud Run, it's important to understand how the time to deploy and scale your applications changes as your workload grows.

We want to know how the number of operations or API calls grows when we increase the number of services or containers.

Scenario Under Consideration

Analyze the time complexity of deploying multiple containerized services on GKE and Cloud Run.

// Deploy N services
for (int i = 0; i < N; i++) {
  // GKE: create deployment and service
  gke.createDeployment(serviceConfig[i]);
  gke.createService(serviceConfig[i]);

  // Cloud Run: deploy service
  cloudRun.deployService(serviceConfig[i]);
}

This sequence shows deploying N services either on GKE or Cloud Run, each requiring API calls to create or deploy resources.

Identify Repeating Operations

Identify the API calls, resource provisioning, data transfers that repeat.

• Primary operation: For each service, GKE makes two API calls (create deployment and create service), Cloud Run makes one deploy API call.
• How many times: These calls repeat once per service, so N times.

How Execution Grows With Input

As the number of services (N) increases, the total API calls grow proportionally.

Input Size (N)	Approx. API Calls/Operations
10	GKE: 20 calls, Cloud Run: 10 calls
100	GKE: 200 calls, Cloud Run: 100 calls
1000	GKE: 2000 calls, Cloud Run: 1000 calls

Pattern observation: The number of API calls grows linearly with the number of services for both platforms.

Final Time Complexity

Time Complexity: O(n)

This means the time to deploy or scale grows directly in proportion to the number of services you manage.

Common Mistake

[X] Wrong: "Deploying more services on Cloud Run or GKE takes the same fixed time regardless of how many services there are."

[OK] Correct: Each service requires separate API calls and resource setup, so more services mean more operations and longer total time.

Interview Connect

Understanding how deployment time scales with workload size helps you design efficient cloud architectures and shows you can think about system behavior as it grows.

Self-Check

"What if we batch deploy multiple services in one API call? How would the time complexity change?"