Kubernetes vs Serverless: How to Choose the Right Platform for Your Workloads
Tags: kubernetes, serverless, cloud, cloudnative
Published: 2023-03-29 15:53:12



Kubernetes and serverless are two popular platforms for deploying and managing cloud-native applications. Both offer benefits such as scalability, reliability, and portability, but they also have different trade-offs and challenges. In this blog post, we will explore when to use kubernetes and when not and use serverless, focusing on two common problems: networking and cost optimization. I will also compare Kubernetes and serverless and explain how the application architecture can influence the choice of the underlying infrastructure and technology.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1qb24jttdkz9g6zoasbo.jpg)

## Networking

One of the main challenges of cloud-native applications is how to enable communication between different workloads that may run on different nodes, clusters, regions, or even providers. Networking is essential for ensuring performance, security, and observability of your applications.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/jeuo83oelr3fefmgzsvm.jpg)

Kubernetes provides a rich set of networking features that allow you to create virtual networks, assign IP addresses, configure DNS, load balance traffic, enforce network policies, and more. Kubernetes also supports service mesh frameworks such as [Istio ](https://istio.io/) or [Linkerd ](https://linkerd.io/) that add additional capabilities such as service discovery, routing, tracing, encryption, and fault tolerance. Kubernetes networking is flexible and powerful, but it also requires a lot of configuration and management. You need to choose the right network plugins, set up the network policies, monitor the network performance and health, troubleshoot issues, and update the network components.

Serverless platforms abstract away most of the networking details from the developers. You don't need to worry about creating or managing networks, IP addresses, DNS, load balancers, or network policies. Serverless platforms handle the networking for you and provide built-in features such as service discovery, routing, encryption, and scaling. Serverless networking is simple and convenient, but it also has some limitations. You may have less control over the network configuration and performance. You may face challenges such as cold starts, latency variability, vendor lock-in, or integration with other services.

So, when should you use kubernetes and when not and use serverless for networking? It depends on your requirements and preferences. If you need more control and flexibility over your network architecture and performance, kubernetes may be a better choice. If you prefer simplicity and convenience over your network configuration and management, serverless may be a better choice.

## Cost Optimization

Another challenge of cloud-native applications is how to optimize the cost of running them. Cloud computing is not cheap, and you need to pay attention to how much you spend on resources such as compute, storage, network, or other services.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/t3g1jspvq2hq0sutdalz.jpg)

Kubernetes allows you to optimize the cost of your workloads by using various techniques such as resource requests and limits, horizontal pod autoscaling (HPA), cluster autoscaling (CA), node affinity and taints/tolerations, spot instances, or multi-cluster federation. Kubernetes also supports FinOps practices such as tagging resources, allocating costs to namespaces or labels, monitoring usage and spending trends, or optimizing capacity planning. Kubernetes cost optimization is effective and granular, but it also requires a lot of effort and expertise. You need to understand the resource consumption patterns of your workloads, tune the scaling parameters and policies, balance the trade-offs between performance and cost efficiency, and constantly monitor and adjust your resource allocation.

Serverless platforms charge you only for the resources you actually use based on metrics such as execution time or memory consumption. You don't need to provision or manage any servers or clusters. Serverless platforms also provide features such as automatic scaling or pay-per-use pricing that help you optimize the cost of your workloads. Serverless cost optimization is simple and transparent, but it also has some drawbacks. You may have less visibility into the resource utilization of your workloads. You may face challenges such as unpredictable costs due to spikes in demand or changes in workload behavior. You may also incur additional costs due to factors such as cold starts or vendor-specific pricing models.

So when should you use kubernetes and when not and use serverless for cost optimization? It depends on your budget and goals. If you have a fixed or predictable budget and want to maximize the utilization of your resources kubernetes may be a better choice. If you have a variable or uncertain budget and want to minimize the overhead of your resources serverless may be a better choice.

## Application 

The application architecture can influence the choice of kubernetes or serverless in several ways. Some factors to consider are:

- Managed service and operation overhead: Kubernetes requires more operational effort and expertise than serverless, as you must manage the clusters, nodes, pods, services, deployments, etc. Serverless abstracts away most of the infrastructure details, allowing you to focus on the business logic and code.
- Ecosystems: Kubernetes has a rich and mature ecosystem of tools, frameworks, and integrations that can help you build, test, monitor, and troubleshoot your applications. Serverless has a smaller but growing ecosystem of tools and frameworks that can simplify the development and deployment of serverless functions.
- Workload characteristics: Kubernetes can handle any type of workload, from long-running to short-lived, from stateful to stateless, from synchronous to asynchronous. Serverless is more suited for short-lived, stateless, and asynchronous workloads that can be executed in parallel.
- Integrations: Kubernetes can integrate with any service or technology that runs on containers or exposes an API. Serverless can integrate with any service or technology that supports event-driven communication or exposes an API.
- Prototyping: Serverless can enable faster prototyping and experimentation, as you can write and deploy functions quickly without worrying about the infrastructure. Kubernetes can also enable rapid prototyping, but it may require more upfront configuration and setup.
- Application portability: Kubernetes can offer more application portability, as you can run your containers on any cloud or on-premises environment that supports Kubernetes. Serverless can offer less application portability, as you may have to adapt your functions to different cloud providers or platforms.
- Organization size and skills: Kubernetes may be more suitable for larger organizations that have dedicated teams and skills for managing the infrastructure and operations. Serverless may be more suitable for smaller organizations that have limited resources and skills for managing the infrastructure and operations.
- Cost optimizations: Serverless can offer more cost optimizations, as you only pay for what you use, and you don't have to provision or maintain any servers. Kubernetes can also offer cost optimizations, but it may require more fine-tuning and monitoring of your resource utilization and scaling policies.


## Conclusion

Kubernetes and serverless are both great platforms for deploying and managing cloud-native applications but they have different strengths and weaknesses. There is no one-size-fits-all solution for choosing between them. You need to consider your specific needs and preferences for networking and cost optimization as well as other factors such as development experience security reliability or portability. You may also use a hybrid approach that combines both platforms for different workloads or scenarios. The key is to understand the trade-offs involved in each platform and make informed decisions that suit your situation.