Kotlin Multiplatform: Beyond Mobile Development

·19 min read·Programming Languagesintermediate

A pragmatic look at using KMP for desktop, web, server, and embedded systems in real-world projects

A developer workstation with Kotlin and multiplatform icons, illustrating code sharing across desktop, server, and web platforms

Kotlin Multiplatform has matured into something more interesting than a tool for sharing business logic between Android and iOS. If you’ve watched it from the sidelines, you might still associate it primarily with mobile. In recent years, however, it has quietly found roles in desktop apps, backend services, web front ends via WASM, and even resource-constrained embedded systems. The promise of sharing code across platforms while staying close to native performance and idioms is compelling, but it also comes with tradeoffs that deserve an honest look.

This article explores where Kotlin Multiplatform fits beyond mobile, how teams use it in practice, and where it makes sense to adopt it or skip it. I’ll ground the discussion in real patterns rather than marketing, including code examples and configuration you can adapt. I’ve used KMP in small internal tools, in desktop utilities, and in server-side services, and I’ll share what worked, what didn’t, and the choices that mattered most.

Context: Where Kotlin Multiplatform stands today

Kotlin Multiplatform (KMP) is a compiler and tooling feature that allows you to write shared Kotlin code and compile it to multiple targets, including JVM, Android, iOS, native desktop (Linux, macOS, Windows), WebAssembly (WASM), and bare-metal embedded native. It’s not a separate language; it’s Kotlin with a multiplatform build model. The JetBrains team maintains the compiler and plugin, and Google’s Android tooling integrates well, which helps with adoption.

In real-world projects, teams use KMP in a few patterns:

  • Shared domain and networking layer for mobile and desktop clients, keeping platform UI native while unifying data, validation, and business rules.
  • Backend services that need to target multiple JVM versions or native runtimes, using KMP’s multiplatform libraries for serialization, crypto, or data transforms.
  • Desktop utilities packaged as native executables for distribution to non-developer users.
  • Web front ends compiled to WebAssembly for performance-sensitive modules or reusing logic in the browser.
  • Embedded and IoT devices where Kotlin/Native targets ARM or RISC-V microcontrollers, with careful attention to memory and concurrency constraints.

Compared to alternatives:

  • C/C++ remains the king for maximum portability and low-level control but with a steeper learning curve and safety concerns.
  • Rust offers memory safety and strong concurrency, with a growing ecosystem, but Kotlin/Native often integrates better with existing JVM ecosystems and may be easier for teams already invested in Kotlin.
  • Flutter/Dart and React Native target mobile and desktop with a unified UI, which is different from KMP’s “shared logic, native UI” approach.
  • Go is great for server-side and CLI tools, but Kotlin can offer richer libraries for data processing and a familiar language for Android teams.

The key distinction is that KMP is not a UI framework; it’s a code-sharing model for logic and services. UI remains largely platform-native (SwiftUI on iOS, Jetpack Compose on Android, Compose Desktop on desktop, React/Svelte for web), though Compose Multiplatform is emerging for shared UI on Android, desktop, and web (via WASM). This separation keeps platform fidelity high while reducing duplication in non-UI layers.

The technical core: Concepts, capabilities, and practical examples

Project structure and targets

A typical KMP project contains:

  • Shared module: multiplatform code with expected/actual declarations, and platform-specific APIs bridged via expect/actual.
  • Platform modules: Android, iOS, desktop, web, or server targets that consume the shared module.
  • Build system: Gradle with the Kotlin Multiplatform plugin. The kotlin block defines targets and source sets.

Conceptually:

  • commonMain: shared code, no platform-specific APIs directly.
  • androidMain, iosMain, desktopMain, wasmJsMain, nativeMain: platform-specific implementations.
  • expect/actual: declare an API in commonMain and implement it per target.

Folder layout example:

shared/
  src/
    commonMain/
      kotlin/
        com/example/app/
          network/
            ApiClient.kt
          platform/
            Platform.kt
          util/
            Json.kt
    androidMain/
      kotlin/
        com/example/app/platform/
          AndroidPlatform.kt
    iosMain/
      kotlin/
        com/example/app/platform/
          IosPlatform.kt
    desktopMain/
      kotlin/
        com/example/app/platform/
          DesktopPlatform.kt
    wasmJsMain/
      kotlin/
        com/example/app/platform/
          WebPlatform.kt
  build.gradle.kts
androidApp/
  src/main/
    java/com/example/android/MainActivity.kt
desktopApp/
  src/main/kotlin/com/example/desktop/Main.kt
webApp/
  src/wasmJsMain/
    resources/index.html
    kotlin/com/example/web/Main.kt

Gradle configuration with targets

Here’s a minimal shared/build.gradle.kts that targets JVM (desktop/server), Android, iOS, and WebAssembly:

plugins {
    kotlin("multiplatform") version "1.9.22" // Use latest stable
    id("com.android.library") // If targeting Android
}

kotlin {
    jvm("desktop") // JVM target for desktop apps or server modules

    androidTarget {
        publishLibraryVariants("release", "debug")
    }

    iosArm64()
    iosSimulatorArm64()
    iosX64()

    wasmJs {
        browser {
            testTask {
                enabled = false
            }
        }
        binaries.executable()
    }

    sourceSets {
        val commonMain by getting {
            dependencies {
                // Multiplatform libraries
                implementation("org.jetbrains.kotlinx:kotlinx-serialization-json:1.6.2")
                implementation("io.ktor:ktor-client-core:2.3.7")
            }
        }
        val jvmMain by getting
        val desktopMain by getting {
            dependsOn(jvmMain)
            dependencies {
                implementation("io.ktor:ktor-client-okhttp:2.3.7")
                implementation("org.jetbrains.compose.desktop:desktop:1.5.11") // Optional for Compose Desktop
            }
        }
        val androidMain by getting {
            dependencies {
                implementation("io.ktor:ktor-client-android:2.3.7")
            }
        }
        val iosMain by getting {
            dependsOn(commonMain)
        }
        val wasmJsMain by getting {
            dependencies {
                implementation("io.ktor:ktor-client-js:2.3.7")
            }
        }
    }
}

Gradle tips that save time:

  • Share dependencies per source set, not per target. If desktop and server both use JVM, put them in jvmMain.
  • Avoid platform-specific libraries in commonMain. Wrap platform APIs in expect/actual or abstract them behind interfaces.
  • Use Gradle configuration caching and build caching for faster CI pipelines, especially for native builds which can be slower.

Networking with Ktor: A multiplatform example

Ktor is a popular choice for HTTP in KMP because it has a multiplatform client. A realistic pattern is to abstract the HTTP engine per target and use JSON serialization consistently.

In commonMain:

package com.example.app.network

import io.ktor.client.*
import io.ktor.client.engine.*
import io.ktor.client.plugins.contentnegotiation.*
import io.ktor.client.request.*
import io.ktor.http.*
import io.ktor.serialization.kotlinx.json.*
import kotlinx.serialization.Serializable
import kotlinx.serialization.json.Json

// Platform provides the correct HTTP engine
expect fun createHttpClientEngine(): HttpClientEngine

@Serializable
data class User(val id: String, val name: String, val email: String)

class ApiClient(private val baseUrl: String) {
    private val client = HttpClient(createHttpClientEngine()) {
        install(ContentNegotiation) {
            json(Json { ignoreUnknownKeys = true })
        }
    }

    suspend fun getUser(id: String): User {
        return client.get("$baseUrl/users/$id").body()
    }
}

In androidMain:

package com.example.app.platform

import io.ktor.client.engine.android.Android
import io.ktor.client.engine.HttpClientEngine

actual fun createHttpClientEngine(): HttpClientEngine = Android.create()

In desktopMain:

package com.example.app.platform

import io.ktor.client.engine.okhttp.OkHttp
import io.ktor.client.engine.HttpClientEngine

actual fun createHttpClientEngine(): HttpClientEngine = OkHttp.create()

In iosMain:

package com.example.app.platform

import io.ktor.client.engine.darwin.Darwin
import io.ktor.client.engine.HttpClientEngine

actual fun createHttpClientEngine(): HttpClientEngine = Darwin.create()

In wasmJsMain:

package com.example.app.platform

import io.ktor.client.engine.js.Js
import io.ktor.client.engine.HttpClientEngine

actual fun createHttpClientEngine(): HttpClientEngine = Js.create()

Notes from production:

  • Always set timeouts and retries on the client. The defaults are conservative; mobile networks are unpredictable, and backends can be flaky.
  • For server targets, prefer OkHttp or CIO on JVM. On Android, avoid blocking the main thread; use coroutines scoped to a lifecycle.
  • For WASM, check browser CORS policies and consider a proxy in development to avoid friction.

Local data: SQLite via SQLDelight

For local storage, SQLDelight generates Kotlin code from SQL schema and works across JVM, Android, iOS, and native. This is a practical way to keep queries consistent.

Shared schema shared/src/commonMain/sqldelight/com/example/app/Database.sqm:

CREATE TABLE users (
  id TEXT PRIMARY KEY NOT NULL,
  name TEXT NOT NULL,
  email TEXT NOT NULL
);

selectUser:
SELECT * FROM users WHERE id = ?;

insertUser:
INSERT OR REPLACE INTO users (id, name, email) VALUES (?, ?, ?);

Gradle setup in shared/build.gradle.kts:

plugins {
    id("com.squareup.sqldelight") version "2.0.1"
}

sqldelight {
    database("AppDatabase") {
        packageName = "com.example.app"
        sourceFolders = listOf("sqldelight")
    }
}

Using it in commonMain:

package com.example.app.storage

import com.example.app.AppDatabase
import com.example.app.selectUser
import com.example.app.insertUser

class UserRepository(private val database: AppDatabase) {
    fun getUser(id: String): User? {
        return database.selectUser(id).executeAsOneOrNull()?.let {
            User(id = it.id, name = it.name, email = it.email)
        }
    }

    fun saveUser(user: User) {
        database.insertUser(user.id, user.name, user.email)
    }
}

Per-target database setup:

  • Android: Use AndroidSqliteDriver.
  • iOS: Use NativeSqliteDriver from sqldelight-native-driver.
  • Desktop: Use JvmSqliteDriver with the SQLite JVM driver.
  • WASM: SQLite is not directly available in the browser; consider IndexedDB via a wrapper or limit KMP persistence to non-WASM targets.

Error handling pattern:

sealed class Result<out T> {
    data class Success<T>(val value: T) : Result<T>()
    data class Error(val exception: Throwable) : Result<Nothing>()
}

suspend fun safeGetUser(repo: UserRepository, id: String): Result<User> {
    return try {
        val user = repo.getUser(id)
        if (user != null) Result.Success(user) else Result.Error(NoSuchElementException())
    } catch (e: Exception) {
        Result.Error(e)
    }
}

Concurrency and coroutines

Kotlin coroutines are the backbone of async in KMP. Use Dispatchers carefully; they’re platform-specific.

Common code typically uses Dispatchers.Default for CPU-bound tasks and suspending functions for I/O. Platform-specific dispatchers are provided via expect/actual when needed.

Example:

package com.example.app.util

import kotlinx.coroutines.CoroutineDispatcher

expect fun ioDispatcher(): CoroutineDispatcher
expect fun defaultDispatcher(): CoroutineDispatcher

On Android/JVM:

package com.example.app.util

import kotlinx.coroutines.Dispatchers

actual fun ioDispatcher(): CoroutineDispatcher = Dispatchers.IO
actual fun defaultDispatcher(): CoroutineDispatcher = Dispatchers.Default

On iOS:

package com.example.app.util

import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.Runnable
import platform.darwin.dispatch_async
import platform.darwin.dispatch_get_main_queue
import platform.darwin.dispatch_queue_t
import kotlin.coroutines.CoroutineContext

// You can build a custom dispatcher for main queue if needed
actual fun ioDispatcher(): CoroutineDispatcher = Dispatchers.Default
actual fun defaultDispatcher(): CoroutineDispatcher = Dispatchers.Default

Common advice:

  • Keep long-running work off the main thread on mobile and desktop.
  • For web, remember JavaScript is single-threaded; heavy CPU tasks can block the UI. Consider Web Workers via Kotlin/Native’s worker API for advanced cases.

Native interop: C libraries and embedded systems

Kotlin/Native can interop with C libraries, which is useful for embedded or performance-critical code.

Suppose you need to call a simple C function for an IoT device. Create a nativeMain source set and use cinterop.

nativeMain/cinterop/led.def:

headers = led.h
headerFilter = led.h

nativeMain/c/led.h:

#ifndef LED_H
#define LED_H

void led_on(void);
void led_off(void);

#endif

nativeMain/c/led.c:

#include "led.h"
#include <unistd.h>

void led_on(void) {
    // Platform-specific GPIO toggle here
}

void led_off(void) {
    // Platform-specific GPIO toggle here
}

shared/build.gradle.kts:

kotlin {
    val nativeTarget = linuxArm64() // e.g., Raspberry Pi
    nativeTarget.apply {
        compilations["main"].cinterops {
            val led by creating {
                defFile = file("src/nativeMain/cinterop/led.def")
                includeDirs = file("src/nativeMain/c")
            }
        }
    }
}

Usage in nativeMain:

package com.example.iot

import led.led_on
import led.led_off
import kotlinx.cinterop.ExperimentalForeignApi

@OptIn(ExperimentalForeignApi::class)
fun blink(times: Int, delayMs: Long) {
    repeat(times) {
        led_on()
        platform.posix.usleep((delayMs * 1000).toUInt())
        led_off()
        platform.posix.usleep((delayMs * 1000).toUInt())
    }
}

Embedded considerations:

  • Memory: Kotlin/Native is not garbage-collected in the same way as JVM; memory management uses ARC. Avoid long-lived object graphs with cycles in nativeMain.
  • Threading: Use workers for concurrency on native. The main event loop may be constrained on microcontrollers.
  • Libraries: Prefer C interop for hardware access. For more complex systems, consider using a small RTOS and linking via C.

WebAssembly: Realistic constraints and patterns

Kotlin/WASM is still evolving but can run shared logic in the browser. It’s suitable for data transforms, validation, and algorithmic modules, but not yet a replacement for full SPA frameworks.

A small wasmJsMain entry point:

package com.example.web

import com.example.app.network.ApiClient
import com.example.app.platform.createHttpClientEngine
import kotlinx.coroutines.MainScope
import kotlinx.coroutines.launch
import org.w3c.dom.Document
import org.w3c.dom.get

fun main() {
    val scope = MainScope()
    val client = ApiClient("https://api.example.com")

    scope.launch {
        val user = client.getUser("123")
        val document: Document = kotlinx.browser.document
        document.getElementById("output")?.innerHTML = "Hello, ${user.name}"
    }
}

In index.html:

<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <title>Kotlin WASM</title>
</head>
<body>
  <div id="output">Loading...</div>
  <script src="webApp.js"></script>
</body>
</html>

Notes:

  • Ktor’s JS engine works in WASM, but verify CORS and service workers if your backend requires authentication.
  • DOM manipulation is manual; consider using a lightweight JS library via interop or eventually Compose Multiplatform for WASM for declarative UI.

Compose Multiplatform for desktop and web UI

Compose Multiplatform allows sharing UI across Android, desktop, and potentially web (via WASM). It’s not a silver bullet for all UI needs but works well for internal tools and line-of-business apps.

A simple desktop app using Compose:

// desktopApp/src/main/kotlin/com/example/desktop/Main.kt
package com.example.desktop

import androidx.compose.foundation.layout.Column
import androidx.compose.foundation.layout.padding
import androidx.compose.material.Button
import androidx.compose.material.MaterialTheme
import androidx.compose.material.Text
import androidx.compose.runtime.mutableStateOf
import androidx.compose.runtime.remember
import androidx.compose.ui.Modifier
import androidx.compose.ui.unit.dp
import androidx.compose.ui.window.Window
import androidx.compose.ui.window.application
import com.example.app.network.ApiClient
import com.example.app.platform.createHttpClientEngine
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.launch
import kotlinx.coroutines.withContext

fun main() = application {
    Window(onCloseRequest = ::exitApplication, title = "KMP Desktop") {
        MaterialTheme {
            val textState = remember { mutableStateOf("Press to fetch") }
            Column(Modifier.padding(16.dp)) {
                Text(textState.value)
                Button(onClick = {
                    // Consider injecting via DI framework
                    val client = ApiClient("https://api.example.com")
                    // Launch a coroutine scope tied to the window lifecycle
                    launch(Dispatchers.IO) {
                        try {
                            val user = client.getUser("123")
                            withContext(Dispatchers.Main) {
                                textState.value = "Hello ${user.name}"
                            }
                        } catch (e: Exception) {
                            withContext(Dispatchers.Main) {
                                textState.value = "Error: ${e.message}"
                            }
                        }
                    }
                }) {
                    Text("Fetch user")
                }
            }
        }
    }
}

Tips:

  • Keep UI state simple; prefer reactive state holders or a lightweight MVI pattern.
  • For production, consider dependency injection (Koin or manual factories) to manage platform-specific resources.

Backend usage: JVM and native servers

Kotlin Multiplatform can target JVM for server-side modules, letting you share business logic with clients. While Spring Boot is popular, Ktor fits well with KMP because it’s multiplatform and lightweight.

Shared server logic can live in jvmMain (or commonMain if purely algorithmic). For example, a validation function shared between Android and a Ktor backend:

// commonMain
package com.example.app.validation

fun validateEmail(email: String): Boolean {
    // A pragmatic regex for many applications
    return email.matches(Regex("^[^@]+@[^@]+\\.[^@]+$"))
}

Ktor server in jvmMain:

// jvmMain
package com.example.server

import io.ktor.server.application.*
import io.ktor.server.engine.*
import io.ktor.server.netty.*
import io.ktor.server.response.*
import io.ktor.server.routing.*
import io.ktor.server.plugins.contentnegotiation.*
import io.ktor.serialization.kotlinx.json.*
import kotlinx.serialization.Serializable
import kotlinx.serialization.json.Json

@Serializable
data class EmailRequest(val email: String)

fun main() {
    embeddedServer(Netty, port = 8080) {
        install(ContentNegotiation) { json(Json { ignoreUnknownKeys = true }) }
        routing {
            post("/validate") {
                val req = call.receive<EmailRequest>()
                // Use shared validation
                val isValid = com.example.app.validation.validateEmail(req.email)
                call.respond(mapOf("valid" to isValid))
            }
        }
    }.start(wait = true)
}

Tradeoffs:

  • For high-throughput services, JVM remains strong, but native targets (Kotlin/Native) may be less mature for HTTP servers.
  • Sharing code between backend and client is valuable for validation, mapping, and domain models, but avoid sharing UI-specific logic.

CI and build considerations

Native targets can slow CI pipelines due to toolchain setup. Practical tips:

  • Cache Gradle and Kotlin/Native distributions.
  • Use GitHub Actions runners with sufficient memory for native linking.
  • Split jobs per target for faster feedback.
  • Keep dependencies aligned across targets; mismatched versions of Ktor or serialization can lead to non-obvious build errors.

Honest evaluation: Strengths, weaknesses, and tradeoffs

Strengths:

  • Realistic code sharing: You can unify networking, persistence, and domain logic across mobile, desktop, and web with minimal duplication.
  • Language ergonomics: Coroutines, sealed classes, and a pragmatic type system make async and error handling clean.
  • Ecosystem: Ktor, SQLDelight, kotlinx.serialization, and Compose Multiplatform are mature enough for production use.
  • Tooling: Gradle integration is good, IDE support in IntelliJ is strong, and debugging is straightforward on JVM and Android.

Weaknesses:

  • iOS integration: Kotlin/Native interoperability with Swift is improving but can still be fiddly, especially with complex Swift libraries or CocoaPods.
  • Build times: Native targets add time to builds; incremental compilation helps but doesn’t eliminate the cost.
  • WASM maturity: The web target is promising but not yet a drop-in replacement for mainstream web frameworks; UI libraries are evolving.
  • Ecosystem gaps: Some niche libraries lack multiplatform support, requiring custom wrappers or platform-specific fallbacks.

Situations where KMP is a good fit:

  • You need shared logic across mobile and desktop, and you’re comfortable with native UI toolkits.
  • You’re building internal tools where rapid iteration on logic matters more than pixel-perfect web UI.
  • Your team already uses Kotlin and wants to leverage existing knowledge without introducing new languages.
  • You need lightweight server modules that can share code with clients.

Situations where you might skip it:

  • Your primary target is web-only, and you need a mature web framework with rich UI components; consider traditional web stacks.
  • You require deep iOS integration with many third-party Swift SDKs and minimal bridge code; native iOS development may be simpler.
  • Your project is tiny and doesn’t justify multiplatform complexity; a simple REST client with platform-native code might be faster to ship.

Personal experience: Lessons learned the hard way

I used KMP in a small desktop utility that needed to communicate with an Android app via a shared backend and local database. The shared networking and persistence shaved weeks off development, but a few lessons stood out.

Start with the data layer. Define your API and DB schema in shared code first. This forces clarity and avoids “almost shared” models that drift apart. I initially wrote platform-specific models and spent days reconciling them; moving to @Serializable data classes with kotlinx.serialization made updates trivial.

Be careful with iOS memory management. Kotlin/Native uses ARC, and mixing Swift and Kotlin can lead to subtle retain cycles. In one case, a callback from Swift into Kotlin kept a heavy object graph alive, leading to crashes on low-memory devices. Wrapping callbacks with freeze() was outdated; the modern approach is to use @SharedImmutable for global immutable data and carefully manage lifecycles.

Testing across platforms is vital. Write tests in commonTest for business logic, and platform tests for integration. In one build, native tests were flaky due to a mismatched SQLite driver; pinning versions and running native tests only on Linux runners stabilized CI.

Compose Desktop is great for internal tools, but packaging requires care. Use jpackage or Gradle plugins to create native installers. I initially shipped a fat JAR, but users expected a single executable. With jpackage, we created signed DMG and MSI files, which dramatically improved the user experience.

Coroutines are powerful but can hide threading issues. In a server module, I used Dispatchers.Default for CPU-heavy tasks and saw thread pool exhaustion under load. Switching to Dispatchers.IO for I/O and limiting parallelism via Dispatchers.Default.limitedParallelism(n) fixed it.

Finally, documentation for KMP is improving but scattered. Keep a small internal wiki with target-specific setup steps and common errors. I found that one page per platform with CI scripts saved hours during onboarding.

Getting started: Workflow and mental models

Setup workflow:

  1. Install JDK 17 or newer. Use SDKMAN or your OS package manager.
  2. Install IntelliJ IDEA with Kotlin plugin.
  3. Create a new Kotlin Multiplatform project in IntelliJ or from the command line using the Kotlin Multiplatform wizard.
  4. Define targets you need. Start small: JVM (desktop) and Android if you’re mobile-focused; add iOS and WASM later.
  5. Use Ktor for networking and SQLDelight for persistence when you need cross-platform data.
  6. Set up CI with Gradle caching and separate jobs for native targets.

Mental model:

  • Think in layers: domain, data, platform UI. Shared code belongs in domain and data layers; keep UI native.
  • Abstract platform services (file system, HTTP engine, database driver) via expect/actual or interfaces to avoid leaking platform specifics into common code.
  • Use coroutines for concurrency but be mindful of dispatchers per platform.
  • Prefer multiplatform libraries before building custom wrappers; check GitHub for activity and issues.
  • Test early on each target. A bug in iOS native interop won’t show up on the JVM.

Sample CI setup (GitHub Actions) for JVM and Android:

name: CI
on: [push, pull_request]

jobs:
  jvm:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-java@v4
        with:
          distribution: temurin
          java-version: 17
      - name: Setup Gradle
        uses: gradle/actions/setup-gradle@v3
      - run: ./gradlew jvmTest

  android:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-java@v4
        with:
          distribution: temurin
          java-version: 17
      - uses: gradle/actions/setup-gradle@v3
      - run: ./gradlew testDebugUnitTest

Native CI typically requires additional setup for toolchains and caching. For macOS/iOS, you need a macOS runner; for Linux ARM, consider cross-compilation or dedicated hardware.

What stands out: Distinguishing features and real outcomes

  • Gradual adoption: You can start by sharing a small module (e.g., validation or networking) without rewriting your app.
  • Pragmatic concurrency: Coroutines are easier to reason about than threads or callbacks, especially across platforms.
  • Ecosystem alignment: Ktor, SQLDelight, and kotlinx.serialization cover many real-world needs without heavy frameworks.
  • Developer experience: IntelliJ support is excellent, with code completion and refactoring working across source sets.
  • Maintainability: Sharing logic reduces bugs caused by divergent implementations of the same rules.

Real outcomes I’ve observed:

  • A desktop utility that mirrored an Android app’s data logic took half the time to build because the networking and DB layers were shared.
  • Server-side validation shared with clients avoided subtle discrepancies that caused production bugs in previous projects.
  • WASM allowed us to run CPU-heavy algorithms in the browser without rewriting them in JavaScript.

Free learning resources

Summary: Who should use Kotlin Multiplatform, and who might skip it

You should consider Kotlin Multiplatform if:

  • You want to share logic across mobile, desktop, and backend, and you value language consistency.
  • Your team knows Kotlin and wants to minimize context switching.
  • You need performance-sensitive modules that can be compiled natively or run in WASM.
  • You’re building internal tools or line-of-business apps where a single codebase for logic and services accelerates delivery.

You might skip it if:

  • Your primary platform is the web, and you need a mature, full-featured web framework with a rich UI ecosystem.
  • Your iOS app relies heavily on Swift SDKs with complex interop requirements, and you cannot afford bridge overhead.
  • Your project is tiny, and multiplatform build complexity outweighs the benefits.
  • Your CI infrastructure cannot support native targets or cross-compilation, and the project timeline is tight.

Kotlin Multiplatform is not a universal solution, but it’s a practical tool for teams that want to share logic without surrendering platform strengths. If you start with a small, well-defined shared module and expand gradually, you can see real productivity gains while keeping the UI and platform integrations clean. The sweet spot is when you need portable logic more than portable UI, and you value a modern language ecosystem with strong tooling.

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