GKE in 2025: Are We Over-Engineering Our Clusters?

[a_aleinikov]

Hi all,

With GKE evolving at an impressive pace, I wanted to raise a provocative question:

Are we over-engineering Kubernetes setups when simpler solutions might do the job?

A few discussion starters:

1. How do you decide when Kubernetes is the right tool versus when a simpler PaaS or even serverless service would be more efficient?

2. Have tools like autopilot mode, node auto-provisioning, and multi-cluster support made your life easier — or added new layers of complexity?

3. What’s your favorite “hack” or unconventional use of GKE that’s saved you time, money, or headaches?

Let’s share some hard-earned lessons and maybe even challenge each other’s assumptions.

Looking forward to your thoughts.

Best,
Aleksei

[reinc · Accepted solution]

Hi @a_aleinikov, 

Welcome to Google Cloud Community!

Let's get into these questions:

1. How do you decide when Kubernetes is the right tool versus when a simpler PaaS or even serverless service would be more efficient?

	GKE  (Standard/Autopilot)	PaaS (Cloud Run, App Engine)	Serverless  (Cloud Functions)
Application Complexity	High: Complex microservices, stateful applications, custom networking, specific OS dependencies.	Medium: Web apps, APIs, stateless services, common runtimes.	Low: Event-driven, single-purpose functions, simple APIs.
Control & Customization	Maximum: Node config, kernel params (GKE Standard), specific sidecars, intricate network policies, custom schedulers.	Moderate: Runtime versions, scaling params, some environment config.	Minimal: Runtime, memory, triggers.
Statefulness	Excellent support for stateful sets, persistent volumes, databases in-cluster.	Limited: Best for stateless. Can connect to external databases.	Designed for stateless. State must be externalized.
Cost Model	Pay for nodes (Standard) or Pod resources (Autopilot), control plane. Potential for idle resources.	Pay for instances running or requests served.	Pay per invocation and execution time. Can be very cost-effective for spiky workloads.
Specific Needs	GPUs, TPUs, specific machine types, long-running background tasks without HTTP triggers.	Standard web protocols, background tasks with limitations.	Short-lived, event-triggered tasks.

2. Have tools like autopilot mode, node auto-provisioning, and multi-cluster support made your life easier — or added new layers of complexity?

• GKE Autopilot - In GKE Autopilot no OS patching, node upgrades, capacity planning for nodes. For new complexity/consideration is less control, No DaemonSets for custom node agents, restricted hostPath/hostNetwork, limited machine type choice initially through simplicity.
• Node Auto-Provisioning (NAP) in GKE Standard - In Node Auto-Provisioning automatically creates and manages node pools based on requirements which are GPU, machine types, taints/tolerations. It reduces manual node pool creation for different types of workload. For new complexity/considerations to avoid Node Auto-Provisioning creating excessive, expensive node pools, set limits and understand how it decides to add new pools versus expanding existing ones.
• Multi-Cluster Support (GKE Enterprise/Fleet Management) - Features like Multi-Cluster Ingress (MCI) and Multi-Cluster Services (MCS) abstract away complex cross-cluster networking. 

3. What’s your favorite “hack” or unconventional use of GKE that’s saved you time, money, or headaches?

• initContainers - to pre-fetch large datasets/models from GCS to an emptyDir volume that's then shared with the main container. Keeps the main container image clean and focused. 
• CronJob - Centralized, version-controlled, observable, and scalable job scheduling without needing separate cron servers.
• ConfigMaps - Avoids building custom Docker images for trivial scripts. Version controlled with your K8s manifests.
• GKE Workload Identity Federation - Improves security posture by eliminating static service account keys.
  
  
  

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