Kotlin vs Scala — Choosing the Right JVM Language

In the world of JVM-languages, Kotlin and Scala are often compared: both offer modern features that go beyond Java, both support functional programming, static typing, and interoperate (to different extents) with Java. But they target somewhat different trade-offs.

This article explores the origins, strengths, weaknesses, and real-world use of both, with code examples, so you can decide which is better suited to your project.

History & Rise to Popularity

Scala

Scala (short for “Scalable Language”) was designed by Martin Odersky. Its design goal was to integrate functional programming (FP) and object-oriented programming (OOP) in a concise, expressive statically typed language on the JVM. First released around 2003-2004. Over time, Scala evolved, introducing richer type systems, implicits, pattern matching, traits, etc.

Scala saw particular adoption in big data (Spark), distributed systems (Akka), and environments demanding complex expressive power.

Kotlin

Kotlin is newer. Developed by JetBrains, the first commit was in late 2010, and the public unveiling around 2011. The design goal was to create a more ergonomic, safer, more concise “better Java” — with full Java interop, fewer ceremony, better null safety, and reasonable compile times. JetBrains explicitly cited Scala as one of the inspirations, but tried to avoid some of Scala’s pitfalls (complexity, compile times).

A big boost in Kotlin’s popularity came when Google announced official support for Kotlin on Android. That endorsement drove adoption among mobile developers.

What Popular Apps / Companies Use Kotlin vs Scala

Knowing who uses a technology in production helps assess maturity and risk.

Scala in Production

Some examples:

• Apache Spark is implemented in Scala. (Spark is a major big-data framework.)
• Twitter migrated many backend services to Scala in its early days.
• Databricks, as maintainers of Spark, heavily use Scala.
• Airbnb uses Scala in some internal pipelines, e.g. financial reporting pipelines.
• Netflix, LinkedIn, Coursera, The Guardian, SoundCloud, Morgan Stanley, and others use Scala or parts of their stack in Scala.

Kotlin in Production

Some examples:

• Android apps: Obviously many Android apps use Kotlin, especially since Google’s official adoption. Some large apps have migrated (or partially migrated) from Java to Kotlin.
• Google’s own apps: Google uses Jetpack Compose (a declarative UI toolkit) built in Kotlin. Several big apps like Instagram, the Android Settings app, Play Store app etc have parts rewritten with Compose.
• Backend / server-side usage: Companies like Amazon, Atlassian, OLX, Shazam, etc., use Kotlin for backend or shared-logic code.
• Kotlin MultiPlatform and shared codebases: Some projects share logic between Android, iOS, Web using Kotlin Multiplatform.

Use Cases: When Each Language Shines

Here are typical scenarios where one language has advantages over the other.

Use Case	Scala	Kotlin
Big Data / Data Processing	Very strong, e.g. Spark, streaming, ETL pipelines. Scala’s functional features, expressive collections, support for pattern matching make it a natural fit.	Less common; possible but not usually the default. Kotlin can interop with Java/Scala libraries though.
Distributed Systems / Reactive Architectures	Good support (Akka, actor model, streams, etc.). Scala’s power in expressing concurrency/parallelism, advanced type abstractions, makes it strong.	Kotlin has coroutines, plus async libraries; good for many use cases, but doesn’t have exactly the same landscape as Scala in FP / high-parallel models.
Web Backends / Microservices	Possible via frameworks like Play, Akka HTTP, etc. For teams comfortable with Scala, especially when requiring expressive APIs or DSLs.	Very strong. Kotlin works well with Spring Boot, Ktor, etc. Rapid development, good interop with existing Java infrastructure.
Mobile / Android	Less used. Scala can be used for Android, but has more friction (build size, tooling, compilation, also less community momentum for Android).	Excellent. Kotlin is first-class for Android. Built into Android Studio. Null safety, modern syntax, etc.
Shared / Cross-Platform Code	Possible via Scala.js or Scala Native, but less mainstream.	Kotlin Multiplatform is making strides; sharing code (business logic, data models) across platforms is more approachable.
Teams / Maintainability / Learning Curve	Best suited when you have developers with FP background, or teams willing to invest in mastering type systems, complexity.	More approachable for Java programmers; better hands-on ramp up; fewer surprises.

Code Examples & Comparisons

Here are side-by-side code examples showing typical patterns, to illustrate differences.

Null Safety / Option types

Scala:

// Scala: using Option to avoid nulls
def findUser(id: String): Option[User] = {
  // suppose some DB lookup
  if (id.nonEmpty) Some(User(id, s"Name-$id")) else None
}

val maybeUser: Option[User] = findUser("123")
maybeUser match {
  case Some(user) => println(s"Got user: ${user.name}")
  case None       => println("User not found")
}

Kotlin:

data class User(val id: String, val name: String)

fun findUser(id: String): User? {
    return if (id.isNotEmpty()) User(id, "Name-$id") else null
}

val maybeUser: User? = findUser("123")
if (maybeUser != null) {
    println("Got user: ${maybeUser.name}")
} else {
    println("User not found")
}

// Or using safe calls + let
maybeUser?.let { println("Got user: ${it.name}") } ?: println("User not found")

Pattern Matching / Algebraic Data Types

Scala has more built-in power here.

Scala:

sealed trait Expr
case class Num(n: Int) extends Expr
case class Sum(a: Expr, b: Expr) extends Expr
case class Prod(a: Expr, b: Expr) extends Expr

def eval(e: Expr): Int = e match {
  case Num(n)        => n
  case Sum(a, b)     => eval(a) + eval(b)
  case Prod(a, b)    => eval(a) * eval(b)
}

val expression: Expr = Sum(Num(2), Prod(Num(3), Num(4)))  // 2 + (3 * 4)
println(eval(expression))  // prints 14

Kotlin (approximation, using when):

sealed class Expr
data class Num(val n: Int) : Expr()
data class Sum(val a: Expr, val b: Expr) : Expr()
data class Prod(val a: Expr, val b: Expr) : Expr()

fun eval(e: Expr): Int = when(e) {
    is Num -> e.n
    is Sum -> eval(e.a) + eval(e.b)
    is Prod -> eval(e.a) * eval(e.b)
}
   
val expression: Expr = Sum(Num(2), Prod(Num(3), Num(4)))
println(eval(expression))  // prints 14

So Kotlin can do sealed classes + when nicely. Scala often has more concise pattern matching, and more built-in support for more complex matching (e.g. extractors, unapply, pattern matching on types, etc.)

Higher-Order Functions & Collections

Scala:

val numbers = List(1,2,3,4,5)
val doubled = numbers.map(_ * 2)
val evens = numbers.filter(_ % 2 == 0)
val sum = numbers.foldLeft(0)(_ + _)
println(doubled) // List(2,4,6,8,10)
println(evens)   // List(2,4)
println(sum)     // 15

Kotlin:

val numbers = listOf(1,2,3,4,5)
val doubled = numbers.map { it * 2 }
val evens = numbers.filter { it % 2 == 0 }
val sum = numbers.fold(0) { acc, v -> acc + v }
println(doubled)  // [2,4,6,8,10]
println(evens)    // [2,4]
println(sum)      // 15

They are similar here; Kotlin’s standard library is expressive in this area.

Pros & Cons

Here are some trade-offs to consider, comparing Kotlin and Scala.

Aspect	Scala — Pros	Scala — Cons	Kotlin — Pros	Kotlin — Cons
Expressivity / Power	Very expressive type system; powerful functional programming constructs; pattern matching; implicit conversions; ability to build DSLs; mix OOP + FP seamlessly.	Complexity; steeper learning curve; compilation times can lag (especially for large codebases); implicit/“magic” features (e.g. implicits in Scala) can lead to confusion; sometimes harder to debug.	More modestly expressive but well balanced; simpler syntax; smoother learning curve; features like null safety, extension functions, coroutines.	Less expressive in the most advanced FP constructs (higher-kind types are more verbose or limited; fewer built-in macros or implicit magic); some cases of “you have to pull in libraries” to get comparable FP features; compile times still nontrivial in big projects.
Interoperability with Java ecosystem	Good — Scala runs on the JVM, can call Java libraries. However, sometimes Scala idioms don’t map so cleanly back to Java. Also, binary compatibility or versioning issues can arise.	The flip side is that Java code calling Scala (especially Scala features like traits, implicits) can be cumbersome. Also, IDE/tooling for Scala is more complex.	Excellent. Kotlin was designed for seamless Java interop. Also tooling in Android/IntelliJ/etc tends to be very good. Migrations from Java to Kotlin are easier.	Some marginal cost for mixed Java/Kotlin codebases; some Java libraries may not fit cleanly with idiomatic Kotlin; also, in very performance-sensitive code, some overhead from interop or features might be nonzero.
Compilation & Tooling / Build Times	For complex Scala code, compile times can be long. Incremental compilation support is good but has limitations. Tooling (IDE autocompletion, debugging) is sometimes more fragile or slower.	Complexity of the language increases the chance of obscure compiler errors; binary compatibility between major Scala versions can be an issue.	Generally good; Kotlin has been optimized for fast compilation; tooling support (IntelliJ, Android Studio) is strong; good incremental builds.	For very large Kotlin codebases or where many annotation processors or kapt are used, build times can still become painful; also Kotlin Multiplatform is still maturing.
Learning Curve & Developer Productivity	Once mastered, Scala allows very concise, expressive, powerful code; great for domain-specific languages, complex data pipelines, reactive / concurrent systems.	Barrier to entry is higher; newbies may struggle with advanced type errors, implicits, implicit conversions, type inference nuance; less common expertise in the market (fewer Scala developers).	Easier for Java developers to pick up; faster to get productive; less cognitive overhead; safer defaults (nullability, etc.).	Because it’s simpler in some respects, some of the “sharp edges” or “super expressive” FP techniques are less straightforward than in Scala; sometimes you trade off “less magic” for more boilerplate when you push beyond typical use.
Library / Ecosystem / Community	Mature libraries in big data (Spark), reactive systems (Akka), etc. Strong ecosystem for functional programming, DSLs.	The ecosystem is powerful, but fragmentation can happen; some libraries are Scala-specific and have steep learning curves; community smaller than e.g. Java or mainstream Android dev.	Growing ecosystem; very good support from JetBrains; lots of libraries for Android, web, backend; community is large and growing.	Some libraries or patterns simply have more maturity in Scala; for very advanced FP / type-level programming, might need third-party libraries. Also tooling maturity in certain fringe areas (Scala.js, Multiplatform) still catching up.

When (or Why) to Choose One Over the Other

Here are guidelines / “decision tips”:

• If you are working heavily in Android / mobile development, Kotlin is almost always the safer and more productive choice.
• If your project involves large-scale data processing, streaming, real-time pipelines, or you need powerful abstractions / DSLs / functional programming at core, Scala is attractive, especially if you have experienced developers.
• If your team’s background is mostly Java, and you want a smoother learning curve, fewer surprises, faster ramp-up, then Kotlin might reduce risk.
• Consider maintenance and onboarding: Scala’s advanced features are powerful but pay a cost in comprehension and long-term maintainability.
• Consider tooling, build times, support, library ecosystem relevant to your domain. If you need libraries / frameworks that are well supported in one ecosystem and not in the other, that may tip the balance.

Performance, Compilation & Runtime Overheads

Some more technical trade-offs are often relevant:

• Runtime performance: Because both compile to JVM bytecode, for many tasks their performance is comparable. But Scala’s richer abstractions (e.g. implicits, higher-kinded types) can sometimes introduce overhead, or force more careful tuning.
• Binary / compatibility concerns: Scala’s major version jumps (Scala 2 → Scala 3) brought in breaking changes; binary compatibility issues can surface. Kotlin has more stability in its versioning (for many projects) though still needs attention.
• Memory / code size overhead: Scala’s standard library and abstractions may induce larger binaries or more class files; for constrained environments (e.g. mobile) that can matter. Kotlin tends to do better in that regard, especially when targeting Android.
• Compile times: As noted above, Scala compile times for complex projects can be higher. Incremental compilation helps but is not a magic fix. Kotlin generally has more optimized build toolchains in many popular use scenarios.

Kotlin vs Scala Cheat Sheet for Developers

Conclusion

There is no one-size-fits-all answer. The choice between Kotlin vs Scala depends heavily on your project requirements, team skill sets, performance constraints, and what you are optimizing for (developer productivity, maintainability, expressive power, or domain-specific needs).

• If you’re building Android apps, or need to ramp quickly with good safety and maintainability, Kotlin is often the better choice.
• If you’re building large scale data pipelines, reactive/distributed systems, or need the highest expressive power, Scala is worth the investment—provided your team is willing to take on the learning and complexity.

Further Reading: The Most Popular Programming Languages to Learn in 2025