Is Kotlin Used in Backend Development a Good Choice?

Daniel Gorlovetsky
September 29, 2025

Is choosing Kotlin for backend development worthy of the hype? As your trusted source for cutting-edge technology solutions, TLVTech takes a deep dive on the topic in our new blog. We demystify Kotlin, a refreshing alternative to Java and Node.js, discover its intricate frameworks, and break down its deployment and scalability. Let's explore together and simplify the complex world of backend development with Kotlin!

Are you using Kotlin in the backend? If so, that's a solid choice! Kotlin is a modern, statically typed language well-loved for its safety features, which drastically reduce common programming errors. Designed to be fully interoperable with Java, Kotlin allows you to keep the good parts of your existing Java expertise and resources.

Talking About the Main Advantages of Kotlin for Backend

So, what makes Kotlin really shine in backend development? It's all down to its expressive syntax, powerful features, and strong focus on null safety. With less boilerplate code, backend developers can write more readable and reliable server-side applications faster.

How does Kotlin fare in backend, you wonder?

First, let's look at performance. Kotlin shines. Its performance is similar to Java. Why? Kotlin runs on the Java Virtual Machine (JVM). It leverages JVM's performance optimizations.

So, if your backend development needs real-time functionality and you want easy async coding, you might want to consider Kotlin over Node.js.

Deploying Kotlin in the backend is pretty smooth. It's quite similar to Java. You can use standard tools. Examples are Maven and Gradle. Can one benefit from known deployment practices of other JVM languages?

To engage with Ktor, you start by installing the Ktor plugin. Then you create a project through IntelliJ IDEA. Next, design your application module and run your server. It's as simple as that.

For Quarkus, the process begins by setting up your project. You then code in Kotlin, compile using Maven, and run it. Their documentation gives detailed guidelines, making the usage a piece of cake.

Next up are the tools. Kotlin can run on the Java Virtual Machine (JVM). This means you can use any Integrated Development Environment (IDE) that works with Java.

Once your setup is good to go, it's time to write code. But, where to start? It's easy. Just follow Kotlin’s reference materials or a good Kotlin tutorial.

Inscription in conclusion, when it comes to backend development, let's just say–Kotlin is showing its worth against both Java and Node.js. It has easily blended into the backend environment with a strong portfolio of features that give it an edge over traditional suspects.

Navigated Kotlin backend development, highlighted its advantages, compared it with Java and Node.js, dipped into its relevant server-side software architecture, and shed light on performance and DevOps considerations. Technology doesn't have to be overwhelming. At TLVTech, we make tech accessible and useful. We aim to simplify complex software development consulting solutions. Ready to explore more about our AI development or dive into fullstack development?  Looking to develop a mobile app?  Need the expertise of a Fractional CTO? Let's embark on this tech voyage together.

Daniel Gorlovetsky
September 29, 2025
is-kotlin-used-in-backend-development-a-good-choice

Related Articles

The Impact of Data Science Consulting

The Impact of Data Science Consulting

- Data science consulting empowers businesses by equipping them with the right data tools and strategies, enhancing business performance and enabling data-driven decision making. - These services can revolutionize business strategies, such as optimizing pricing based on customer data, and impact various industries (e.g., e-commerce, healthcare, finance). - When hiring data consulting firms, consider their experience, range of services, client satisfaction rates, and transparency in their fee structure, which can be hourly or project-based. - Data science consulting is a lucrative field with an average salary of $120,000 in the US and high job opportunities due to the increasing importance of data in business decision-making. - Machine learning consulting similarly offers growth opportunities by predicting customer behavior, improving decision-making, and tailoring business solutions for efficiency and accuracy. - Best practices in data science consulting involve clean, accurate data, the right tools for the project, objective analysis, and the ethical handling of data.

Read blog post

Is Agile Methodology in Software Development Effective?

- Agile in software development is a set of methods for managing work. It divides work into smaller parts that are frequently reassessed and adapted, allowing for great flexibility with changes in customer needs. - Agile brings more value and speed to development based on four key values: prioritizing people and interactions, working software, client collaboration, and responding to change. - There are twelve principles of Agile focusing on satisfaction, rapid delivery, welcoming changing requirements, collaboration, trust, sustainable development, continual progress, technical excellence, simplicity, and reflective effectiveness. - Agile principles focus on adaptability and rapid feedback, differing from traditional methods which focus on resource allocation and long planning cycles. - The Agile software development cycle is structured into regular sprints involving planning, task division, execution, review, and revision. User stories are used to understand the software from a user perspective. - Agile methodologies include Agile Scrum, Extreme Programming, Iterative Development, and Feature-Driven Development. - Agile promotes teamwork, allows change, supports tangible results sooner, factors in real-time customer feedback, and tackles risk head-on. However, it can be overtaxing, require a proactive team, and could lead to potential long-term unforeseen issues due to its focus on the present.

Read blog post

How Startups Can Cut Cloud Costs by 30% Without Hurting Performance

Early-stage startups often waste 25–40% of their cloud budget on idle, oversized infrastructure. This article explains how intelligent, demand-based autoscaling can cut cloud costs by up to 30%—without sacrificing performance—by aligning infrastructure capacity with real usage instead of peak assumptions.

Read blog post

Contact us

Contact us today to learn more about how our automation partnership service might assist you in achieving your technology goals.

Thank you for leaving your details

Skip the line and schedule a meeting directly with our CEO
Free consultation call with our CEO
Oops! Something went wrong while submitting the form.