is kubernetes the same as aws?
I can't count how many times someone has asked me this. A founder. A VP of Engineering. A CTO who should know better. They'll say something like "we're moving to Kubernetes" and I'll ask "what cloud?" and they'll stare at me like I just asked whether they breathe air.
Let's clear this up right now: Kubernetes is not AWS. They aren't the same thing. They don't compete. You can't substitute one for the other. But the confusion is real, and it's costing people time, money, and sanity.
Here's the shortest explanation: AWS is a cloud provider — it rents you virtual servers, databases, storage, networking. Kubernetes is an orchestration system — it decides where and how your containers run. You can run Kubernetes on AWS. You can run it on Google Cloud, Azure, or a server in your closet. They solve different problems.
But the confusion tells us something deeper. People don't ask "is Python the same as Linux?" even though one runs on the other. The fact that this question keeps coming up means we've done a terrible job explaining what these tools actually do.
I've spent the last six years building data infrastructure at SIVARO. I've seen teams waste millions because they thought Kubernetes WAS their cloud. I've also seen teams waste millions because they thought they didn't need either. This guide is going to fix that confusion — for good.
What Kubernetes Actually Does (and Doesn't)
Let's start with the core question: what does kubernetes actually do?
Kubernetes is a scheduler. Full stop.
You give it a bunch of machines (physical or virtual). You tell it "I want three copies of my web server running, each with 1 CPU and 2GB of RAM, exposed on port 80." Kubernetes figures out which machines have capacity, places the containers, monitors them, restarts them if they crash, scales them up or down based on load.
That's it. That's the job.
It's not a monitoring system (though it exposes metrics). It's not a CI/CD pipeline (though it integrates with them). It's not a database (though you can run databases on it). It's a traffic cop for containers.
What exactly is kubernetes used for? In practice: running microservices. Running batch jobs. Running machine learning training workloads. Running anything that can be containerized and needs to survive machine failures.
But here's the thing most tutorials won't tell you: Kubernetes is a platform for building platforms. You don't just "install Kubernetes" and get value. You install Kubernetes, then you spend months building the abstractions on top — service mesh, ingress controllers, secret management, monitoring, logging, cost tracking, namespace policies, RBAC configurations — before your developers can deploy anything safely.
I've seen startups burn six months on this. Then they ask "why aren't we shipping features?"
AWS: The Cloud, Not The Scheduler
AWS is different. Fundamentally.
AWS is a collection of services that let you rent infrastructure. EC2 for virtual machines. RDS for databases. S3 for object storage. Lambda for serverless functions. EKS for managed Kubernetes (see the confusion?).
AWS's job is to abstract physical hardware. You don't buy servers, rack them, cable them, cool them. You click a button (or run an API call) and get a server in three minutes. That's the value.
Kubernetes doesn't do that. Kubernetes assumes you already have servers (or VMs from AWS, GCP, Azure, bare metal). Kubernetes just decides where to put things on those servers.
The Venn diagram overlap is only in one place: managed Kubernetes services. EKS (AWS), GKE (Google), AKS (Azure). These are AWS/Google/Microsoft offering to run the Kubernetes control plane for you. You still pay for the worker nodes (the VMs running your containers). You still pay for storage, networking, data transfer.
So when someone asks "is kubernetes the same as aws?", they're usually asking one of three things:
- Can I use Kubernetes without AWS? Yes.
- Is Kubernetes included with AWS? No, but EKS is.
- Should I use Kubernetes or AWS services? Depends entirely on what you're building.
The third question is the one that actually matters.
Why People Move Away from Kubernetes
I've been watching the "we're leaving Kubernetes" trend for two years now. Ona announced they were leaving Kubernetes in 2024 after running it for years. Their reason: operational complexity was eating their engineering time. They couldn't justify the overhead for their team size.
The HN thread "I Didn't Need Kubernetes, and You Probably Don't Either" from late 2024 had hundreds of comments from people sharing similar stories. Small teams. Medium-sized products. Services that could run on two machines behaving like they needed a fleet.
The hate for Kubernetes is real and not entirely wrong. The complexity tax is heavy. You need to understand networking (CNI plugins, services, ingress, network policies). You need to understand storage (PVs, PVCs, storage classes). You need to understand security (RBAC, PSPs, service accounts, secrets, pod security policies). You need to understand observability (metrics, logs, traces, dashboards, alerts).
That's a lot. For a team of five people running a CRUD app with 1000 users? That's overkill.
But here's the contrarian take: the people who claim they don't need Kubernetes are often right about their current state and wrong about their trajectory. I've consulted with a company that said "we're fine with Docker Compose" while running 47 microservices across 9 servers and doing manual rollouts at 2 AM. They needed Kubernetes. They just didn't know it yet.
The question "do you actually need Kubernetes?" gets asked constantly. My rule: if you can run everything on two machines with Docker Compose and a load balancer, you probably don't need Kubernetes. If you have more than 10 services, or you need auto-scaling based on metrics, or you deploy more than once a week and can't afford downtime, you should at least evaluate it.
So What Should You Actually Use?
Let me give you the practical answer I give every client at SIVARO.
Team of 1-5 engineers, one product, simple web app: Use a platform-as-a-service. Heroku. Railway. Fly.io. Render. DigitalOcean App Platform. You don't need Kubernetes. You don't even need containers directly. You need to ship features and find product-market fit. Anything that distracts from that is wrong.
Team of 5-20 engineers, several services, growing: Use managed container services. AWS ECS with Fargate. Google Cloud Run. Azure Container Apps. These give you container orchestration without the Kubernetes control plane overhead. You still need to containerize your apps, but you don't need to manage etcd clusters or upgrade Kubernetes versions at 3 AM.
Team of 20+ engineers, complex architecture, high growth: Use Kubernetes on a managed control plane (EKS, GKE, AKS). But hire at least one person whose full-time job is Kubernetes operations before you adopt it. I've seen teams of ten backend engineers try to "learn Kubernetes on the job" and lose three months of feature development.
If you're running data infrastructure or AI systems (our specialty at SIVARO): Kubernetes is almost mandatory for production AI workloads. Training jobs need GPU scheduling. Model serving needs auto-scaling with custom metrics. Data pipelines need workflow orchestration with retry logic. Kubernetes + something like Kubeflow or Argo Workflows handles this better than any alternative.
The Netflix Question
People always ask: is netflix using kubernetes?
The answer is complicated. Netflix uses containers heavily. They use container orchestration. But they built their own system — Titus — before Kubernetes was mature. Titus runs on AWS EC2 and handles millions of containers. It's not Kubernetes, but it solves the same problem.
Netflix now supports Kubernetes internally. Some teams use it. Some don't. The lesson isn't "Netflix uses/don't use Kubernetes." The lesson is that Netflix has the resources to build whatever they need. You probably don't.
Don't copy Netflix. Copy companies that are 2-3 years ahead of you, not 10.
Why the Confusion Exists
Back to the original question. Why do people think Kubernetes and AWS are the same?
Three reasons:
1. Managed Kubernetes blurs the line. When you use EKS, you interact with AWS to provision it. You use IAM roles for auth. You use ALB for ingress. The boundary between "Kubernetes" and "AWS" gets fuzzy because they integrate deeply.
2. The abstraction stack is confusing. You run containers on Kubernetes, which runs on EC2, which runs on AWS. What's "AWS" here? The EC2? The EKS? The networking? The load balancers? It's not obvious where one ends and the other begins.
3. Bad marketing. AWS markets EKS as "Kubernetes on AWS." Kubernetes vendors market their platform as "like AWS but better." New engineers hear both and assume they're interchangeable.
Here's a concrete example to fix this in your head:
yaml
# This is Kubernetes YAML - it defines WHAT to run
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-app
spec:
replicas: 3
selector:
matchLabels:
app: my-app
template:
metadata:
labels:
app: my-app
spec:
containers:
- name: app
image: my-app:latest
ports:
- containerPort: 8080
hcl
# This is Terraform for AWS - it defines WHERE to run it
resource "aws_eks_cluster" "my_cluster" {
name = "my-cluster"
role_arn = aws_iam_role.cluster.arn
vpc_config {
subnet_ids = aws_subnet.public[*].id
}
}
resource "aws_eks_node_group" "my_nodes" {
cluster_name = aws_eks_cluster.my_cluster.name
node_role_arn = aws_iam_role.nodes.arn
instance_types = ["t3.medium"]
scaling_config {
desired_size = 3
max_size = 5
min_size = 1
}
}
The first file defines your application. The second file provisions the infrastructure that will run it. They're different tools for different layers.
When You Should Use AWS Services Instead of Kubernetes
Here's a truth that Kubernetes enthusiasts don't like: for many workloads, AWS services are better than anything running on Kubernetes.
Need a relational database? Use RDS. It handles backups, patching, replication, failover. Doing that on Kubernetes requires StatefulSets, operators (like Zalando's Postgres operator), persistent volumes, backup cronjobs, and a deep understanding of etcd behavior under network partitions. I've done both. RDS is easier.
Need a queue? Use SQS. It scales to millions of messages per second. Running RabbitMQ or Kafka on Kubernetes is an operational nightmare in comparison.
Need object storage? S3. Period. MinIO on Kubernetes is great for on-prem, but in AWS you'd be insane to not use S3.
Here's a pattern I've seen work well at multiple companies:
AWS services for stateful, high-availability infrastructure:
- RDS for databases (Postgres, MySQL)
- ElastiCache for Redis
- SQS for queues
- S3 for object storage
Kubernetes for stateless, elastic application workloads:
- Web servers
- API services
- Background workers
- Batch processing
- ML inference
This hybrid approach gives you the operational simplicity of managed services where it matters most, plus the flexibility of Kubernetes where you need custom scaling or complex deployment patterns.
The Cost Mistake Everyone Makes
At SIVARO, we've analyzed dozens of cloud bills. The single biggest mistake I see: companies move to Kubernetes thinking it will save money, then spend more on unnecessary resources.
Why? Kubernetes makes it easy to deploy things. Too easy. Developers spin up staging environments with 10 replicas. Devs deploy CPU-intensive jobs without resource limits. Teams run databases on Kubernetes with triple replication when a single RDS instance would suffice.
Here's a real example from a client:
yaml
# Bad - no resource limits, developer didn't specify constraints
apiVersion: apps/v1
kind: Deployment
metadata:
name: analytics-worker
spec:
replicas: 5
template:
spec:
containers:
- name: worker
image: analytics-worker:latest
# No resources specified - defaults to cluster max
This ran on p3.16xlarge instances (GPU instances, $24/hour each) because the developer didn't specify resource requests and the scheduler placed it on the only nodes with available capacity. That deployment cost $2,880 in a weekend before anyone noticed.
Compare with proper resource management:
yaml
# Good - explicit resource requests and limits
apiVersion: apps/v1
kind: Deployment
metadata:
name: analytics-worker
spec:
replicas: 2
template:
spec:
containers:
- name: worker
image: analytics-worker:latest
resources:
requests:
cpu: "1"
memory: "2Gi"
limits:
cpu: "2"
memory: "4Gi"
The fix: implement resource quotas per namespace, enforce limit ranges, and use cost monitoring tools (Kubecost, Vantage, or even simple labels + custom dashboards). Without these safeguards, Kubernetes is a money printer — in the wrong direction.
FAQ: The Questions I Actually Get Asked
Q: Is Kubernetes the same as AWS?
No. Kubernetes is container orchestration. AWS is a cloud provider. You can run Kubernetes on AWS (via EKS), on Google Cloud (GKE), on Azure (AKS), or on your own servers. They serve different purposes and are not interchangeable.
Q: Do I need to learn Kubernetes to use AWS?
No. Many AWS customers never touch Kubernetes. They use EC2 directly, or Lambda for serverless, or Elastic Beanstalk for platform-as-a-service. Kubernetes is an advanced tool for specific use cases.
Q: Is Netflix using Kubernetes?
Partially. Netflix built their own container orchestration system called Titus, which runs on AWS EC2. Some internal teams now use Kubernetes, but Netflix's core streaming infrastructure still runs on Titus. Their lesson: build what you need, not what's popular.
Q: What does Kubernetes actually do?
Schedules containers across machines. Ensures desired state matches actual state. Handles scaling, rolling updates, service discovery, and self-healing. It's a platform for running distributed systems reliably.
Q: Why are people moving away from Kubernetes?
Operational complexity. Small teams find that managing Kubernetes takes more time than the benefits it provides. Ona's post and the HN discussion both highlight this. If you have fewer than 20 servers and deploy weekly, Kubernetes probably isn't worth it.
Q: Can I run Kubernetes without AWS?
Absolutely. You can run Kubernetes on-premises using tools like kubeadm, Rancher, or OpenShift. You can run it on bare metal, on VMware, or on any cloud provider. Kubernetes is cloud-agnostic by design.
Q: What exactly is Kubernetes used for in production?
Mostly microservices — deploying and managing hundreds of small services that communicate via APIs or message queues. Also batch processing, ML training pipelines, CI/CD runners, and any workload that benefits from automated scaling and self-healing.
Q: Should my startup use Kubernetes?
Probably not. If you're pre-product-market fit, use a platform-as-a-service. You don't need to worry about pod scheduling while you're trying to figure out if anyone wants your product.
The Real Answer
Is Kubernetes the same as AWS? No. And if you're asking the question, you're probably not ready for either in the way you think.
The right question isn't "should I use Kubernetes or AWS?" The right question is "what problem am I trying to solve?" If it's "I need to run my app somewhere," use a platform service. If it's "I need to orchestrate 50 microservices across 100 machines," use Kubernetes. If it's "I need to build an AI system that processes 200K events per second," call SIVARO and let's talk — we'll use both, and a dozen other tools, and it'll still be cheaper than what you'd build yourself.
Kubernetes and AWS are tools. Tools are not identities. Picking the right one for the job is the only thing that matters.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.