Imbibed in a learning and sharing culture, we are of the opinion that knowledge and learning should be accessed by everyone. We are dedicated to improving the tech industry and are inspired to share our skills and expertise via business, technology, and culture. In this regard, product engineering requires a deployment strategy, Serverless Vs. Kubernetes container orchestration. But the major challenge is that one-size fit for all solutions where applications can be built and scaled doesn’t exist.
Serverless computing
Serverless computing is a cloud computing execution where the cloud provider allocates machine resources on-demand and takes care of server on the behalf of customers. Serverless computing has four main principles:
- Servers are abstracted
- High availability out-of-the-box
- Scalability on demand
- Paying only for what you use
Serverless stack
The serverless stack takes care of abstracting the server. Once the applications and dependencies are made into a zip file or a container image, they are sent to the cloud provider. They will instantiate the container and execute the function present on the server. This particular server is event-driven.
If many events are triggered simultaneously, the cloud provider initiates auto-scaling thus giving you an option to pay for the executed functions. This is one part of a serverless stack, collectively regarded as function-as-a-service.
The second part of the serverless stack is backend-as-a-service. BaaS handles storage, events, orchestration, ETL, analytics, etc. Most cloud providers in the market switched to pay-for-use mode of pricing.
Serverless vs Kubernetes
Every microservice must be independently deployable, scalable, and reliable. To make these happen, you need a platform that can perform resource management by efficiently utilizing the existing infrastructure, leaving scope for deployment strategies at optimized costs, having an ecosystem for observability, and auto-scaling of the platform. By considering each of these factors, let’s compare Kubernetes and Serverless computing.
Auto-scaling
The use of Kubernetes helps your applications to scale in three dimensions – increasing the number of pods, size of the pods, and nodes in the cluster. It scales based on server metrics like CPU or memory or number of requests received per second per pod.
On the other hand, Serverless scales the application itself. First, there will be exponential scaling that happens, and then concurrent limits occur. These limits are applied to all the functions in a region, hence if not taken care of, maximum concurrent instances fall below the limits. Therefore, this will affect overall application scaling and performance.
Resource management
In Kubernetes, CPU and memory requirements for service are specified at the pod level. Whereas in serverless computing, memory requirements are mentioned in the deployment manifest which decides memory allocation.
Observability
The use of tools on top of Kubernetes clusters is very common to collect, aggregate, and visualize the logs, metrics, and traces. These tools are installed in the form of operators or demon sets in the cluster. Since logs and telemetrics grow in large volumes, they need storage services and managed at scale.
When you use serverless, the metrics vary slightly. First metrics of the function need to be measured in accordance with the cost model. As there is a concurrency limit, observation to manage the number of concurrent executions and processes along with their duration is needed. Cloud providers ensure a set of tools for complete visibility of function execution and application logging observability.
Cost considerations: Serverless vs Kubernetes
The total cost of ownership is the crucial aspect when deciding between Kubernetes and Serverless. This includes infrastructure, development, and maintenance costs. To run Kubernetes clusters, operating costs vary a lot.
Based on the operation costs model that varies from one organization to the other, applications with medium or low traffic or unpredictable patterns gain significant savings in the serverless model. Kubernetes have fewer costs for high-traffic applications with predictable load patterns.
Flexible deployment models
In contrast, we recommend organizations design the applications so that switching between deployment models between serverless and container orchestration could be economical. This can only be achieved when a strict operation is enforced when dependents on the actual target, common aspects, and independent components are determined.
Serverless vs Kubernetes: Key aspects to consider
- Serverless enables faster time-to-market, high elasticity, and low-cost models
- Utilize serverless across the stack apart from computing purpose
- Implement serverless modular code patterns to ensure flexibility to make shifts in deployment models
- Serverless can’t be applicable for batch processing and high computing applications
You can use Kubernetes-based containers as well, which are the right fit for workload, heavy traffic, streaming, and intensive computing applications. And using serverless is owning less and building more. At the initial stages of microservices deployment, most organizations go for serverless as it drives them to focus on building strong customer use cases.
Make your deployment models flexible. Build using modular code patterns. Choose the best solution based on your needs and what the application should do. Lock your goals and deploy as per your needs. Our technical architects and engineering teams make the Kubernetes-based container deployments easy for you to run most of the products you build.
