Cloud-Native Development FAQ: Serverless vs Kubernetes Guide
You're probably here because someone in a meeting said "we should go cloud-native" and everyone nodded like they knew what that meant.
Let me save you some time: cloud-native doesn't mean anything specific. It's marketing speak that somehow encompasses serverless functions, Kubernetes clusters, service meshes, event-driven architectures, and whatever HashiCorp announced last week.
But the question you're actually asking is real: should you run your workloads on serverless (Lambda, Cloud Functions, etc.) or Kubernetes? And unlike most tech decisions, this one actually matters. Pick wrong and you'll spend the next two years either debugging networking issues at 3am or staring at AWS bills that make your CFO cry.
The Real Question Nobody Asks
Here's what you need to understand first: serverless and Kubernetes solve different problems. I know every comparison article treats them as equivalent choices, but they're not.
Serverless is "I want to run code without thinking about servers." Kubernetes is "I want total control over how my containers run, and I'm willing to become a distributed systems expert to get it."
Those are fundamentally different goals. If you're asking "which one should I use?" you're already approaching this wrong. The right question is: "what problems am I actually trying to solve?"
Let's break down the actual tradeoffs.
When Serverless Actually Works
Serverless shines when your workloads are spiky, short-lived, or event-driven. If you're processing webhook payloads, resizing images, or running scheduled jobs, Lambda is probably the right answer.
Here's why: you pay per invocation. Not per hour, not per instance—per actual execution. That S3 trigger that fires 100 times a day? Costs you basically nothing. The same workload on Kubernetes means running pods 24/7, paying for capacity you don't use.
But there's a catch everyone forgets about: cold starts.
A cold start happens when AWS has to spin up a new execution environment for your function. For Node.js, that's usually 200-500ms. For Java or .NET, it can be 3-10 seconds. If your function runs every few minutes, every invocation is a cold start.
You can mitigate this with provisioned concurrency—basically paying AWS to keep your functions warm. But now you're paying for idle capacity again, which defeats the whole point.
Here's the real killer: debugging. When your Lambda function fails, you get a log stream in CloudWatch. Maybe. If the function ran long enough to write logs before it died. Good luck correlating errors across 47 different functions with no distributed tracing.
This is where understanding your distributed architecture becomes critical. If you're running dozens of Lambda functions, API Gateway integrations, and event-driven workflows, you need to map how everything connects. Tools like Glue automatically index your entire codebase—including API routes, service boundaries, and deployment patterns—so you can actually understand what breaks when your Lambda function times out.
The Kubernetes Reality Check
Kubernetes gives you power. Lots of it. You can define exactly how your containers run, how they scale, how they communicate. You get service discovery, load balancing, rolling deployments, and about 10,000 other features.
You also get complexity that will make you question your career choices.
Here's what running Kubernetes actually means:
You need to understand pods, deployments, services, ingresses, config maps, secrets, persistent volumes, storage classes, network policies, RBAC, service accounts, and custom resource definitions. That's just to deploy a basic application.
Want proper observability? Add Prometheus, Grafana, Jaeger, and the ELK stack. Now you're managing 15 different services just to monitor your actual services.
Want CI/CD? Hope you like YAML. Your deployment pipeline will have more lines of configuration than your actual application code.
But here's the thing: for certain workloads, Kubernetes is absolutely worth it.
If you have long-running services with predictable traffic, Kubernetes is cheaper than serverless at scale. If you need stateful workloads or complex networking, Kubernetes can do things Lambda simply can't.
And if you're already running microservices, Kubernetes gives you a consistent deployment model instead of juggling Lambda functions, ECS tasks, and whatever else your team bolted together.
The Cost Breakdown Nobody Shows You
Let's talk money, because the pricing models are deliberately confusing.
Serverless costs:
Lambda: $0.20 per 1M requests + $0.0000166667 per GB-second
API Gateway: $3.50 per million requests
CloudWatch Logs: $0.50 per GB ingested
That pricing looks great until you do the math. A function with 1GB memory running for 1 second costs $0.0000166667. Seems cheap. But run that function 10 million times a month and you're at $166 just for compute, plus $35 for API Gateway, plus log costs.
Kubernetes costs:
EKS control plane: $73/month per cluster
Worker nodes: $0.0416/hour for t3.medium (3 nodes = ~$90/month)
Load balancer: $16/month
Data transfer, monitoring, etc: ~$50/month
Minimum baseline for a production EKS cluster: ~$230/month. That runs 24/7 whether you use it or not.
The breakeven point? Depends on your workload, but typically if you're processing more than 5-10 million requests per month with moderate compute needs, Kubernetes becomes cheaper.
But remember: that $230/month doesn't include your time. Managing Kubernetes is a full-time job. If you're a team of three engineers, spending 20% of your time on Kubernetes ops costs you more than the actual infrastructure.
What About Both?
Here's the pattern that actually works for most teams: use both, strategically.
Run your core services on Kubernetes. They're always running anyway, they need consistent performance, and you want tight control over networking and observability.
Use Lambda for the edges: webhooks, async jobs, file processing, scheduled tasks. Anything that's genuinely event-driven or runs infrequently.
This gives you cost efficiency where it matters and operational simplicity where Kubernetes would be overkill.
But now you have a new problem: understanding how these systems interact. Your Lambda functions call your Kubernetes services. Your Kubernetes services trigger Lambda functions via SNS. You have API Gateway routing to both.
When something breaks—and it will—you need to trace requests across these boundaries. Traditional monitoring tools show you metrics per service, but they don't map the actual architecture. This is exactly the problem that makes distributed systems hard: the complexity isn't in any single component, it's in how everything connects.
This is where Glue becomes useful for cloud-native teams. It maps your entire architecture—service boundaries, API routes, Lambda functions, deployment patterns—by analyzing your actual code. When your team asks "which services call this Lambda?" or "what happens if this API endpoint goes down?", you have answers instead of guesses.
The Questions You Should Actually Ask
Forget "serverless vs Kubernetes" for a minute. Here's what you need to figure out:
What's your traffic pattern? Constant load favors Kubernetes. Spiky or unpredictable load favors serverless. If you don't know, start with serverless—it's easier to migrate from Lambda to Kubernetes than the reverse.
How much control do you need? If you're running standard web services, serverless is probably fine. If you need custom networking, GPU workloads, or stateful services, you need Kubernetes.
What's your team's expertise? Kubernetes has a brutal learning curve. If you don't have someone who understands distributed systems, you're going to hurt yourself. Serverless has its own complexity, but the blast radius is smaller when you screw up.
How much code are you shipping? If you're iterating fast and deploying multiple times per day, serverless gives you faster feedback loops. If you're shipping large, complex services, Kubernetes gives you better deployment control.
What breaks at 3am? This is the real test. With Lambda, you're debugging weird timeout issues and permission problems. With Kubernetes, you're debugging networking, pod scheduling, and resource exhaustion. Pick your poison.
The Migration Path Nobody Talks About
Let's say you're on serverless now and outgrowing it. How do you migrate to Kubernetes without nuking your product?
Start with the most expensive Lambda functions. Move them to containers running on ECS (AWS's simpler container service). Get comfortable with containers first, then consider Kubernetes.
Or going the other way: you have a monolith on Kubernetes and want to break off pieces to serverless. Start with scheduled jobs and background tasks. Move them to Lambda. Leave the core service on Kubernetes.
The worst thing you can do is a "big bang" migration where you rewrite everything at once. I've seen teams spend 18 months migrating from Lambda to Kubernetes only to realize serverless was actually fine for their use case.
What This Actually Means For Your Team
Cloud-native architecture—whether serverless, Kubernetes, or both—creates distributed complexity that traditional dev tools don't handle well.
Your IDE can't show you how Lambda functions connect to your Kubernetes services. Your monitoring can't map which team owns each component. Your documentation (if you have any) is outdated the moment you deploy.
You need tooling that understands distributed systems as systems, not just individual services. That means mapping code to infrastructure, tracking API boundaries, identifying what breaks when you change a schema.
Glue does this by indexing your entire codebase—not just files and functions, but service boundaries, API contracts, and deployment patterns. It's built for teams running complex cloud-native architectures who need to understand the full system, not just individual components.
The Honest Answer
Most teams should start with serverless and migrate to Kubernetes only when they have specific reasons to do so: cost at scale, technical requirements serverless can't handle, or operational maturity to actually run Kubernetes properly.
But if you're already on Kubernetes and it's working? Don't migrate to serverless just because it's trendy. The grass isn't greener—it's just different grass with different problems.
The real answer isn't about technology. It's about understanding your workload, your team, and your constraints. Then picking the tool that fits instead of the one with the best marketing.
And once you've made that choice, invest in tooling that helps you understand what you've built. Because the hard part isn't deploying to Lambda or Kubernetes—it's maintaining and evolving the distributed system you've created.