This content originally appeared on DEV Community and was authored by Shiva Charan

🚀 Why Kubernetes Was Created

Kubernetes was born at Google, inspired by their internal system Borg, which managed millions of containers across thousands of servers. Google needed a way to:

• Run huge numbers of services reliably
• Automatically heal failures
• Scale up and down fast
• Update applications without downtime
• Efficiently use hardware across large fleets

Google open-sourced a next-generation version of these ideas in 2014 — this became Kubernetes (K8s).

"Kubernetes" means helmsman or pilot in Greek — a fitting metaphor for a system that steers containerized applications.

🎯 The Core Problems Kubernetes Solves

Below listed are a few problems that Kubernetes solves.

1️⃣ Manual deployment of containers was painful

Before Kubernetes, teams running containers (e.g., Docker) had to:

• SSH into servers
• Start containers manually
• Restart them after a crash
• Track which app was running where

➡️ Kubernetes solves this with automated deployment & scheduling.

2️⃣ Scaling applications was not automatic

If an app was receiving more traffic, engineers had to:

• Provision new nodes
• Add more containers manually
• Update load balancers

➡️ Kubernetes provides automatic horizontal scaling based on CPU, RAM, or custom metrics.

3️⃣ Rolling updates were risky and caused downtime

Typical problems included:

• Application downtime
• Half-updated environments
• Broken rollback processes

➡️ With Kubernetes:

• Rolling updates are built-in
• Zero-downtime deployments become normal
• Instant rollbacks if something breaks

4️⃣ Machines and containers fail — constantly

Before K8s, a single node crash could take down running apps unless manually restarted.

➡️ Kubernetes automatically:

• Restarts containers
• Replaces failed containers
• Reschedules them on healthy nodes
• Ensures the “desired state” always matches reality

5️⃣ Running workloads across many servers was complex

Teams needed a global scheduler to decide:

• Which server should run which container?
• How to balance CPU/memory usage?
• What happens when nodes join/leave the cluster?

➡️ Kubernetes has a built-in cluster-wide scheduler for optimal resource utilization.

6️⃣ Service discovery was hard

Without Kubernetes, mapping “which container is running on which IP:port” is messy.

➡️ Kubernetes provides:

• Service abstraction
• Stable virtual IPs
• Load balancing across pods

This means your application always talks to a consistent endpoint.

7️⃣ Configuration management for apps was scattered

Secrets, config files, and environment variables were often stored in unsafe or inconsistent places.

➡️ Kubernetes offers:

• ConfigMaps for configuration
• Secrets for sensitive data
• Built-in versioning and updating

8️⃣ Multi-environment consistency was difficult

Running the same application on:

• Dev
• Test
• Pre-prod
• Production

…was error-prone.

➡️ Kubernetes ensures environment consistency using:

• Declarative YAML manifests
• GitOps workflows
• Immutable container images

9️⃣ Cloud vendor lock-in

Before Kubernetes, tools were tied to specific clouds (AWS ECS, Azure Service Fabric, etc.)

➡️ Kubernetes is cloud-agnostic, allowing workloads to run anywhere:

• AWS
• GCP
• Azure
• On-prem
• Hybrid

🧠 In Summary — What Problems Kubernetes Solves

🏁 Final Takeaway

Kubernetes exists because modern applications demand:

• High reliability
• Easy scaling
• Automation instead of manual work
• Portability across cloud/on-prem
• Powerful operations for containerized apps

It is the operating system for the data center, providing automated control over containerized workloads at scale.

This content originally appeared on DEV Community and was authored by Shiva Charan