A few years ago, if someone asked me whether running serverless on Kubernetes made sense, I probably would have laughed.
“Serverless? Isn’t that just for small apps or startups?”
I thought.
Now in 2025, almost every engineering team I work with has some form of serverless running on Kubernetes in production. The reason is simple. It solves problems that teams have been dealing with for years: unpredictable scaling, rising cloud costs, slow deployments, and complex operations.
Working on both in-house systems and client projects, I have seen how running serverless workloads on Kubernetes changes the way teams build and run applications. I’ve scaled systems, optimized workloads, and yes, sometimes learned the hard way when things went wrong. These experiences taught me why this approach is getting more popular in 2025 and for the coming years.
Top 5 Reasons to Run Serverless on Kubernetes
Here’s a detailed breakdown of the five key reasons why businesses should run serverless on Kubernetes.
1. Scaling Without Losing Sleep
The first thing you notice when you deploy serverless workloads on Kubernetes is… You stop worrying about scaling. I remember one project where we had a sudden spike in traffic from a viral marketing campaign. Before serverless, our on-call engineer would have been glued to the cluster dashboard, manually adjusting replicas and watching CPU usage spike. With serverless functions, the workload just scaled. Up. Down. Done.
This is not magic. The platform handles pod management, horizontal scaling, and even idle time, scaling to zero when no one is calling your function. As an engineering head, I can get a sound sleep knowing that my team out there is not stuck handling the traffic spikes.
2. You Only Pay for What You Use
If there is one thing finance teams love, and engineers try to achieve too, it is saving the money spent otherwise. Traditional Kubernetes clusters are like leaving the lights on in an empty office. Pods are running, resources are reserved, and bills keep adding up.
Serverless flips that model. Idle workloads do not cost you anything. I have seen teams cut costs dramatically just by migrating intermittent batch jobs and event-driven tasks. The ironic part? The developers never had to think about it. They just wrote the function, and it worked.
As a Kubernetes consulting company, we recently helped a client in the e-commerce sector move their inventory update and reporting jobs to serverless on Kubernetes. These workloads only run during specific business hours or when certain triggers fire. By moving them to serverless, we reduced their cloud compute costs by nearly 40 percent while improving reliability and removing manual scaling tasks from the engineering team.
3. Deploy Fast, Iterate Faster
One of the things I tell my Kubernetes engineers often is: do not let infrastructure slow you down. Kubernetes is great, but setting up deployments, configuring ingress, handling service accounts, all that takes time.
Serverless changes the game. You write the function, deploy it, and the platform handles the plumbing. No manifests to tweak, no manual scaling. We had a team launch a new analytics endpoint in under a day using serverless on Kubernetes, something that would have taken a week with traditional deployments. For me, that is the real win: time saved for other important tasks that require strategic thinking and innovation.
4. Observability That Actually Works
Early serverless platforms were a black box. You had almost no visibility into what was happening inside a function. You would deploy it and hope it worked.
Kubernetes changes that. Metrics, logs, and tracing work the same as with any other pod, integrated with your existing monitoring stack. I have had engineers tell me,
“It is just another pod, right?”
And, they are correct. The difference is that it behaves like a serverless function, scales automatically, and sleeps when idle, but you can still debug and monitor it like a regular service. That balance between control and automation is rare, and it is why many teams stick with this approach.
5. Flexibility Without Vendor Lock-In
Flexibility is another key reason to run serverless on Kubernetes. We run some workloads across AWS, some on GCP, and a few at the edge. The temptation with managed serverless is obvious: stick with AWS Lambda or GCP Functions, and call it a day. But then you are tied to that provider forever.
Kubernetes gives you a consistent deployment model that works everywhere. The same function behaves the same way whether it runs on-prem, in the cloud, or at the edge. You still get automatic scaling, idle management, and fast deployment without being locked into a single vendor. For our teams, this freedom makes managing workloads simpler and reduces risk.
Final Thoughts
Running serverless workloads on Kubernetes is not just a trend that I ask my clients to follow. It is a practical solution to the problems we have struggled with for years: scaling, cost, iteration speed, visibility, and operational flexibility.
If your team has not tried serverless on Kubernetes yet, I would encourage you to experiment. Start small, watch it scale, and see the difference it makes. After years in this business, I can tell you, once you see it work, you will wonder how you managed without it.
And, if you need expert help, you can consider taking the help of Bacancy’s Kubernetes managed services. Our team of experts can help you design, deploy and manage serverless workloads on Kubernetes. We help optimize scaling, reduce operational overhead, implement best practices for security and observability, and ensure your clusters run efficiently across cloud or hybrid environments.
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