Top Python Alternatives for Modern Programming Needs

Python dominates. But it’s not always the right choice.

Performance bottlenecks hit harder when you’re scaling production systems or processing massive datasets. You start noticing the execution speed differences. The Global Interpreter Lock becomes that thing everyone complains about in meetings.

Looking at the best Python alternatives isn’t about abandoning ship. It’s about picking the right tool for specific problems. Sometimes you need static typing for enterprise codebases. Other times, you want compiled language performance without sacrificing developer productivity.

This guide breaks down modern programming languages that solve Python’s limitations. You’ll see where Go excels in concurrent systems, why Rust matters for memory-critical applications, and when JavaScript actually makes sense for backend work.

We’re covering syntax differences, runtime performance benchmarks, learning curves, and real-world use cases across software development scenarios.

The Best Python Alternatives

JavaScript/Node.js

JavaScript with Node.js runtime brings Python-style simplicity to web development while running on Chrome’s V8 engine. The language transformed from a browser-only tool into a full-stack powerhouse that handles both client and server work.

Primary Use Cases

Real-time applications are where Node.js absolutely dominates.

Chat platforms, streaming services, and live collaboration tools use its event-driven architecture to handle thousands of connections simultaneously. Companies like Netflix and LinkedIn run their core services on Node.js specifically for this reason.

API development and microservices architectures favor Node.js because it shares code between frontend and backend. Single-page applications built with React or Vue naturally pair with Node.js backends.

Key Technical Differences

The event loop model sets Node.js apart from Python’s threading approach.

Python’s Global Interpreter Lock (GIL) limits true parallelism in multi-threaded programs. Node.js sidesteps this entirely with non-blocking I/O that handles 40-70% more requests per second for I/O-heavy tasks.

JavaScript uses dynamic typing like Python, but the V8 engine’s JIT compilation makes execution significantly faster. Benchmarks show Node.js outperforms Python by 2-4x in most web service scenarios.

Memory management differs too. Node.js uses V8’s garbage collector, which typically completes cycles in under 10 milliseconds compared to Python’s more variable pause times.

Syntax and Learning Curve

JavaScript feels familiar to Python developers, though the async/await pattern takes adjustment.

Callback hell used to plague Node.js code. Modern async/await syntax cleaned this up considerably. The learning curve is moderate if you already know JavaScript.

Python’s indentation-based syntax arguably reads cleaner than JavaScript’s curly braces. But once you’re past the basics, both languages write similarly for most tasks.

Ecosystem and Libraries

The npm registry contains over 1.3 million packages (way more than PyPI).

Popular frameworks include Express for web servers, NestJS for enterprise applications, and Socket.io for real-time features. The JavaScript ecosystem moves fast, sometimes too fast for comfort.

Testing tools like Jest and Mocha match Python’s pytest in quality. Package management with npm or yarn works smoothly, though dependency trees can get messy.

Performance Profile

Node.js crushes Python for I/O-bound operations but falls behind for CPU-intensive work.

Web API benchmarks consistently show Node.js serving 2-3x more requests per second than Django or Flask. The V8 engine optimizes hot code paths aggressively.

For data science or numerical computing? Python wins decisively thanks to NumPy and similar C-backed libraries.

Startup time is nearly instant compared to Python’s interpreter initialization overhead.

When to Choose Over Python

Pick Node.js when your project needs:

• Real-time functionality (websockets, live updates, streaming)
• Shared code between frontend and backend
• High concurrency with minimal resource usage
• Full-stack development in a single language
• Integration with JavaScript frontend frameworks

Avoid it for data science, machine learning, or heavy numerical computing where Python’s ecosystem dominates.

Java

Java remains the enterprise workhorse that Python never quite became. Released in 1995 with the “write once, run anywhere” promise, Java’s JVM architecture and strict typing make it fundamentally different from Python’s interpreted flexibility.

Primary Use Cases

Enterprise applications are Java’s bread and butter.

Banks, insurance companies, and large corporations build mission-critical systems in Java because it offers predictability and long-term stability. Android app development uses Java (or Kotlin) as the primary language.

Large-scale distributed systems, high-frequency trading platforms, and big data processing with Hadoop and Spark lean heavily on Java. The language excels when projects need to handle millions of transactions reliably.

Key Technical Differences

Compiled bytecode execution makes Java significantly faster than Python.

Java compiles to bytecode that the JVM then JIT-compiles to machine code. This two-step process delivers 5-10x faster execution than Python for most computational tasks. The JVM’s garbage collector is mature and handles large heaps efficiently.

Static typing catches errors at compile time rather than runtime. This strictness prevents entire classes of bugs that Python only catches during execution.

True multithreading works properly in Java without a GIL. Python’s threading model can’t compete for CPU-bound parallel tasks.

Syntax and Learning Curve

Java’s verbose syntax contrasts sharply with Python’s minimalism.

A simple “Hello World” requires class declarations, public static void main, and System.out.println. Python does it in one line. This verbosity adds development time but makes large codebases more explicit and maintainable.

The learning curve is steeper for beginners. Type declarations, interfaces, and abstract classes introduce concepts Python developers don’t deal with initially.

Experienced Python developers typically need 2-4 weeks to become productive in Java.

Ecosystem and Libraries

Maven Central hosts millions of libraries, though discoverability isn’t as smooth as PyPI.

Spring Framework dominates enterprise development with dependency injection, security, and database integration built in. Hibernate handles object-relational mapping better than most Python ORMs for complex schemas.

Testing frameworks like JUnit and Mockito are rock-solid. Build tools (Maven, Gradle) are powerful but complex compared to Python’s pip and setuptools.

Performance Profile

Java consistently delivers 8-10x better performance than Python in CPU-intensive benchmarks.

The JVM’s JIT compiler optimizes hot code paths during runtime. After warmup, Java performance rivals C++ for many workloads. Memory footprint is larger than Python initially but scales better under load.

Garbage collection pauses are predictable and tunable. Modern collectors like ZGC keep pause times under 10ms even with multi-gigabyte heaps.

When to Choose Over Python

Java makes sense when you need:

• Enterprise-grade stability and support
• Android application development
• High-performance backend services handling serious load
• Strong type safety for large teams and long-lived codebases
• Integration with existing Java infrastructure

Skip Java for rapid prototyping, data analysis, or quick scripts where Python’s flexibility shines.

C#

Microsoft’s answer to Java, C# combines enterprise robustness with modern language features. Released in 2000 as part of the .NET framework, C# has evolved into a genuinely cross-platform language that competes directly with Python in several domains.

Primary Use Cases

Game development with Unity makes C# a primary choice for 2D and 3D games.

The Unity engine powers a massive portion of mobile and indie games. C# handles game logic while Unity manages rendering and physics. This combination dominates the game development landscape outside of AAA studios.

Enterprise applications, especially on Windows, naturally use C#. ASP.NET Core builds high-performance web services. Desktop applications with WPF or WinForms target Windows environments where Python struggles.

Cross-platform mobile development uses Xamarin to share C# code between iOS and Android.

Key Technical Differences

Compiled execution with .NET runtime delivers 5-25x faster performance than Python.

C# compiles to Common Intermediate Language, which the .NET runtime JIT-compiles to native code. This approach rivals Java’s performance while feeling more modern. The runtime includes sophisticated garbage collection and memory management.

Static typing with powerful type inference catches bugs at compile-time. The type system is more expressive than Java’s, with generics that actually work well and pattern matching that rivals functional languages.

Async/await was pioneered in C# and works beautifully for concurrent operations without Python’s GIL limitations.

Syntax and Learning Curve

C# syntax sits between Java’s verbosity and Python’s brevity.

LINQ (Language Integrated Query) provides Python-like list comprehensions with type safety. Modern C# with top-level statements eliminates much of the boilerplate Java requires.

The learning curve is moderate. Python developers adjust to static typing and explicit memory awareness. Properties, delegates, and events introduce new concepts.

Visual Studio provides exceptional tooling that Python IDEs don’t quite match.

Ecosystem and Libraries

NuGet hosts hundreds of thousands of packages with strong Windows integration.

ASP.NET Core rivals Node.js for web API performance. Entity Framework Core handles ORMs competently, though it’s more complex than Python’s Django ORM.

ML.NET brings machine learning capabilities, but it can’t compete with Python’s TensorFlow and PyTorch ecosystems. For data science, Python wins decisively.

Testing frameworks (xUnit, NUnit) and mocking libraries (Moq) are mature and well-designed.

Performance Profile

C# outperforms Python by 5-10x for general-purpose computing and 20-30x for CPU-bound tasks.

The .NET runtime’s GC handles memory efficiently with configurable collection strategies. Server GC mode can sustain very high throughput with minimal pause times.

Startup time is faster than Java but slower than Python for small scripts. For long-running services, this difference becomes irrelevant.

Memory usage is reasonable and predictable compared to Python’s interpreter overhead.

When to Choose Over Python

Choose C# when building:

• Unity games or interactive 3D applications
• Windows desktop applications
• High-performance web APIs that need enterprise features
• Cross-platform mobile apps with code sharing
• Systems requiring strong type safety and performance

Avoid C# for data science, quick scripts, or projects where the Python ecosystem dominates (ML, data analysis, scientific computing).

Ruby

Ruby shares Python’s philosophy of developer happiness and readable code. Created by Yukihiro “Matz” Matsumoto in 1995, Ruby gained explosive popularity through Ruby on Rails, which revolutionized web development in the mid-2000s.

Primary Use Cases

Web applications built with Rails remain Ruby’s primary domain.

GitHub, Shopify, and Airbnb’s core platforms run on Rails. The framework’s “convention over configuration” approach accelerates development for standard web apps. Ruby excels at building MVPs and products where developer productivity matters more than raw speed.

DevOps tooling uses Ruby extensively. Chef and Puppet (configuration management) are Ruby-based. The language works well for system administration scripts and automation tasks.

Key Technical Differences

Performance characteristics are remarkably similar to Python, with Ruby typically running slightly slower.

Both are interpreted languages with similar execution speeds. Benchmarks show Python about 20-30% faster for most tasks. Ruby 3.x improved performance significantly but still can’t match compiled languages.

Ruby’s metaprogramming capabilities exceed Python’s. The language allows runtime modification of classes and methods more freely. This flexibility enables Rails’ “magic” but can make debugging tricky.

Memory management through garbage collection works similarly to Python. Both languages struggle with CPU-intensive tasks compared to compiled alternatives.

Syntax and Learning Curve

Ruby’s syntax emphasizes natural language readability, sometimes even more than Python.

Everything is an object in Ruby, truly. Even integers and nil have methods. Python makes pragmatic exceptions. Ruby’s blocks, procs, and lambdas handle closures differently than Python’s approach.

The learning curve is gentle for Python developers. Syntax differences are cosmetic rather than conceptual. Most Python devs become functional in Ruby within a week or two.

Duck typing works identically in both languages. Testing culture is stronger in Ruby, with RSpec being genuinely excellent.

Ecosystem and Libraries

RubyGems hosts fewer packages than PyPI but covers web development comprehensively.

Rails dominates web frameworks, though Sinatra offers a Flask-like lightweight alternative. The Rails ecosystem includes strong tools for testing, background jobs (Sidekiq), and deployment.

For data science and machine learning, Ruby falls far behind Python. NumPy equivalents exist but lack maturity and community support.

Bundler handles dependency management well, similar to Python’s virtual environments and requirements files.

Performance Profile

Ruby performs comparably to Python with neither being particularly fast.

Web framework benchmarks show Rails and Django delivering similar throughput. FastAPI (Python) outperforms both significantly for API-only services.

Memory consumption runs higher than Python in some scenarios. Ruby’s garbage collector has improved but can struggle under heavy load.

Concurrency remains limited by Ruby’s GIL-equivalent, making truly parallel execution difficult for CPU-bound tasks.

When to Choose Over Python

Pick Ruby when:

• Building web applications with Rails’ conventions
• Working with existing Ruby codebases or Rails shops
• Developer productivity and happiness are top priorities
• Metaprogramming flexibility outweighs performance concerns
• You prefer Ruby’s object-oriented purity

Skip Ruby for data science, machine learning, scientific computing, or high-performance systems where Python’s ecosystem and performance give it clear advantages.

Go (Golang)

Google designed Go to solve problems Python can’t: massive concurrency, fast compilation, and simple deployment. Released in 2009, Go strips away the complexity of C++ while delivering near-C performance. It’s fundamentally different from Python’s interpreted flexibility.

Primary Use Cases

Cloud infrastructure and microservices are Go’s sweet spot.

Docker, Kubernetes, and countless DevOps tools run on Go. The language excels at building lightweight services that handle thousands of concurrent connections with minimal resources. Cloud providers and infrastructure companies adopt Go for networking tools and distributed systems.

API development benefits from Go’s performance and built-in HTTP server. Command-line tools compile to single binaries with no dependencies, making distribution trivial.

Data pipelines and ETL processes use Go when performance matters more than Python’s data science ecosystem.

Key Technical Differences

Compiled binaries and built-in concurrency make Go 30-40x faster than Python.

Go compiles directly to machine code. No interpreter, no VM, just native execution. Compilation is absurdly fast too, unlike C++ or Rust. This speed enables rapid development cycles despite being compiled.

Goroutines and channels provide true concurrent programming without Python’s GIL limitations. A single Go process can handle hundreds of thousands of goroutines on modest hardware. Each goroutine uses only 2KB of memory initially.

Static typing with type inference catches errors at compile time. The type system is simpler than Java’s but more rigid than Python’s dynamic typing.

Syntax and Learning Curve

Go’s syntax is deliberately minimal, sometimes frustratingly so.

No generics until Go 1.18 (and they’re still awkward). No inheritance or traditional OOP. Error handling through explicit return values rather than exceptions. These limitations force simple, readable code but can feel restrictive coming from Python.

The learning curve is surprisingly gentle. Most Python developers become productive in Go within a week. The language spec is small enough to read in an afternoon.

Formatting is enforced by gofmt. No style debates, no configuration. This uniformity helps when reading others’ code.

Ecosystem and Libraries

Go’s standard library is exceptionally complete, reducing dependency needs.

HTTP servers, JSON handling, cryptography, and testing are built-in. Third-party packages exist for most needs but the ecosystem is smaller than Python’s. Dependency management through Go modules works cleanly.

Web frameworks (Gin, Echo, Fiber) exist but aren’t as feature-complete as Django. For pure API work, the standard library often suffices.

No data science ecosystem. Machine learning tools are minimal. Go isn’t replacing Python for analytics work.

Performance Profile

Go delivers near-C performance with fraction of the complexity.

Benchmarks show Go running 10-30x faster than Python for CPU-bound tasks and handling 3-5x more requests per second for web services. Garbage collection pauses stay under 1ms for most workloads.

Memory footprint is tiny compared to Python. A Go web server uses 10-20MB versus Python’s 40-80MB baseline. This efficiency matters for containerized microservices.

Binary sizes start around 2MB but grow with dependencies. Still dramatically smaller than bundling Python with dependencies.

When to Choose Over Python

Choose Go for:

• High-performance web services and APIs
• Microservices architectures where resource efficiency matters
• Command-line tools that need simple distribution
• Systems programming without C/C++ complexity
• Concurrent processing (websockets, data pipelines, real-time systems)

Skip Go for data science, machine learning, rapid prototyping, or projects requiring extensive libraries where Python dominates.

Rust

Rust promises memory safety without garbage collection and performance matching C/C++. Mozilla started Rust in 2006 to write browser components safely. It’s now the most admired language in Stack Overflow surveys for 9 consecutive years, though its complexity makes it a very different beast from Python.

Primary Use Cases

Systems programming and performance-critical services use Rust when safety matters.

WebAssembly modules for browser applications leverage Rust’s near-native speed. Embedded systems and IoT devices run Rust for memory safety without runtime overhead. Blockchain and cryptocurrency projects adopt Rust heavily.

High-performance backends replace Python/Node.js bottlenecks with Rust microservices. Discord famously rewrote performance-critical services in Rust, reducing latency by 90%.

Command-line tools (ripgrep, bat, exa) demonstrate Rust’s speed and zero-dependency deployment.

Key Technical Differences

Rust runs 40-60x faster than Python for CPU-intensive tasks with provable memory safety.

The ownership system eliminates entire classes of bugs at compile time. No null pointers, no data races, no use-after-free. This comes at the cost of fighting the borrow checker during development.

Zero-cost abstractions mean high-level code compiles to machine code as efficient as hand-written C. No garbage collector means predictable, consistent performance.

Concurrency is fearless in Rust. The type system prevents data races at compile time, something Python’s threading can’t guarantee.

Syntax and Learning Curve

Rust’s learning curve is brutal compared to Python’s gentle slope.

Ownership, borrowing, and lifetimes introduce concepts most developers haven’t encountered. The compiler is strict but provides excellent error messages. Expect 2-4 months before feeling truly productive.

Pattern matching and functional programming features exceed Python’s capabilities. The type system is sophisticated, supporting generics, traits, and complex compile-time guarantees.

Once you “get” Rust, writing Python feels loose and unsafe by comparison.

Ecosystem and Libraries

Cargo (Rust’s package manager) and crates.io host a growing ecosystem.

Web frameworks (Actix, Axum, Rocket) exist but lack Django’s batteries-included convenience. Async programming works well with Tokio runtime. Database libraries (diesel, sqlx) are solid but not as mature as Python’s SQLAlchemy.

No real data science ecosystem. Python completely dominates ML/AI. Some tools exist for numerical computing but pale beside NumPy/SciPy.

Testing and documentation tools are excellent. Cargo handles everything integrated into the build system.

Performance Profile

Rust matches or exceeds C/C++ performance while preventing memory bugs.

Benchmarks show Rust running 50-100x faster than Python for CPU-bound code. Memory usage is minimal and predictable. No garbage collection means no pause times ever.

Compile times can be slow for large projects. The compiler does extensive checking, which takes time but catches bugs early.

Binary sizes are reasonable, typically smaller than equivalent Go programs for similar functionality.

When to Choose Over Python

Pick Rust when you need:

• Maximum performance for CPU-intensive workloads
• Memory safety guarantees for critical systems
• Embedded systems or bare-metal programming
• WebAssembly modules running in browsers
• Systems where Python’s performance is demonstrably inadequate

Avoid Rust for rapid prototyping, data science, web scraping, automation scripts, or projects where development speed trumps execution speed. The learning curve and development time rarely justify Rust unless performance is critical.

TypeScript

Microsoft’s TypeScript adds static typing to JavaScript, creating a typed superset that compiles to standard JavaScript. Released in 2012, TypeScript has become the default for large-scale frontend and Node.js projects where Python traditionally had no presence.

Primary Use Cases

Frontend web development overwhelmingly favors TypeScript over Python.

React, Angular, and Vue applications use TypeScript for type safety in complex UIs. Large-scale single-page applications benefit from catching UI bugs at compile time. Node.js backends increasingly adopt TypeScript for shared types between frontend and backend.

Full-stack development in TypeScript enables code sharing and type definitions across the entire stack, something Python can’t match for browser-based applications.

Key Technical Differences

TypeScript is JavaScript with a compile-time type system, not a fundamentally different language.

The TypeScript compiler catches type errors before runtime. Python’s type hints are optional and not enforced by the interpreter. This distinction matters for large teams maintaining complex codebases.

Performance matches JavaScript since TypeScript compiles to JavaScript. For Node.js backends, TypeScript performs identically to JavaScript (both run on V8). Python’s interpreted execution is slower but the gap narrows for I/O-bound web services.

The type system is sophisticated, supporting generics, union types, conditional types, and powerful type inference.

Syntax and Learning Curve

TypeScript feels like JavaScript with type annotations layered on top.

Python developers face a steeper learning curve than JavaScript developers. Async/await syntax is similar but the ecosystem differences are substantial. Callbacks, promises, and event-driven patterns require adjustment.

The learning curve is moderate if you already know JavaScript. Adding TypeScript to existing JavaScript projects happens gradually.

Type definitions for popular libraries make integration smooth, though poorly typed libraries can frustrate.

Ecosystem and Libraries

The npm ecosystem (1.3+ million packages) serves both JavaScript and TypeScript.

Frontend frameworks (React, Next.js, Svelte) have excellent TypeScript support. Backend frameworks (NestJS, Express with types) work well. The ecosystem moves faster than Python’s, sometimes chaotically.

Testing frameworks (Jest, Vitest) handle TypeScript natively. Build tools (Webpack, Vite, esbuild) are complex but powerful.

Data science and ML tools are essentially nonexistent. Python dominates completely for analytical work.

Performance Profile

TypeScript performance equals JavaScript performance since it compiles to JavaScript.

Node.js benchmarks show similar speeds to bare JavaScript. For web APIs, TypeScript/Node.js runs 2-3x faster than Python (Django/Flask) but slower than Go or Rust.

Browser performance is identical to JavaScript. TypeScript’s compile-time checking adds no runtime overhead.

Development workflow includes compilation time, which adds seconds to the feedback loop compared to Python’s instant execution.

When to Choose Over Python

Choose TypeScript when:

• Building frontend web applications (Python can’t run in browsers natively)
• Developing full-stack applications with shared types
• Working with existing JavaScript codebases
• Building Node.js services where type safety prevents bugs
• Your team already knows JavaScript/TypeScript

Skip TypeScript for data science, machine learning, system administration, scientific computing, or standalone scripts where Python’s ecosystem excels.

Kotlin

JetBrains created Kotlin as a better Java, and Google made it the preferred language for Android development in 2019. Kotlin runs on the JVM, compiles to JavaScript, or runs natively, giving it flexibility Python lacks while maintaining Java’s performance.

Primary Use Cases

Android app development makes Kotlin the primary choice for mobile apps.

Google’s Android team actively pushes Kotlin over Java. Modern Android frameworks (Jetpack Compose) are Kotlin-first. New Android projects overwhelmingly choose Kotlin unless maintaining legacy Java code.

Backend services use Kotlin as a more pleasant Java alternative. Spring Framework supports Kotlin fully. Companies with existing JVM infrastructure adopt Kotlin without leaving the Java ecosystem.

Cross-platform mobile development uses Kotlin Multiplatform to share business logic between Android, iOS, and web.

Key Technical Differences

JVM execution gives Kotlin Java-level performance, far exceeding Python.

Kotlin compiles to JVM bytecode like Java, delivering similar 5-10x performance advantages over Python. Null safety is enforced at compile time, eliminating NullPointerException errors. Coroutines provide lightweight concurrency better than Python’s threading.

Type inference reduces boilerplate compared to Java while maintaining static typing benefits. The language feels modern and concise despite JVM constraints.

Interoperability with Java is seamless. Kotlin code calls Java libraries without friction, accessing the entire JVM ecosystem.

Syntax and Learning Curve

Kotlin syntax is cleaner than Java but more complex than Python.

Data classes, extension functions, and smart casts reduce code verbosity. The language supports both functional and object-oriented programming naturally. Null safety through the type system (String vs String?) catches bugs Python would miss.

Python developers need 2-3 weeks to become comfortable with Kotlin’s syntax and JVM concepts. The learning curve is gentler than Java or Rust but steeper than staying in Python.

Ecosystem and Libraries

The entire Java ecosystem is accessible plus Kotlin-specific libraries.

Android development libraries (Jetpack Compose, Room, ViewModel) are Kotlin-first. Backend frameworks (Ktor for Kotlin, Spring with Kotlin support) work excellently. Build tools use Gradle with Kotlin DSL.

Data science and ML tools are minimal. Python completely dominates this space. For numerical computing or scientific work, Python wins decisively.

Testing frameworks (JUnit works fine, plus Kotlin-specific options like Kotest) are mature.

Performance Profile

Kotlin performs identically to Java since both run on the JVM.

Benchmarks show Kotlin running 8-10x faster than Python for CPU-bound tasks. Memory usage and garbage collection behave like Java. Startup time is faster than Java but slower than Python for simple scripts.

Coroutines are more efficient than Python’s async/await for high-concurrency workloads. Android apps built in Kotlin perform identically to Java apps with cleaner code.

When to Choose Over Python

Choose Kotlin when:

• Building Android applications
• Working within JVM infrastructure
• Needing Java performance with modern syntax
• Developing cross-platform mobile apps with Kotlin Multiplatform
• Your team values type safety and null safety

Skip Kotlin for data science, machine learning, quick scripts, system administration, or web scraping where Python’s ecosystem and simplicity make more sense.

Swift

Apple built Swift in 2014 to replace Objective-C for iOS development. The language compiles to native code and integrates deeply with Apple’s ecosystem. Swift is fundamentally different from Python, trading flexibility for performance and type safety.

Primary Use Cases

iOS and macOS applications are Swift’s primary domain.

The Unity game engine doesn’t use Swift, but native Apple platforms do. Apple Watch apps, tvOS apps, and iPhone applications all run Swift. Apple made it the official language for their platforms, pushing it hard since 2019.

Server-side Swift exists but remains niche. Some teams use it for backend services to share code with iOS apps. The Vapor framework enables web development, though adoption is limited outside Apple-focused companies.

Key Technical Differences

Compiled execution makes Swift 5-8x faster than Python according to Apple’s claims.

Swift compiles to native machine code using LLVM. This eliminates interpreter overhead entirely. Automatic Reference Counting (ARC) manages memory without garbage collection pauses, giving predictable performance.

Static typing with powerful type inference catches errors at compile time. The type system supports optionals natively, forcing developers to handle nil values explicitly. This prevents null pointer crashes that plague other languages.

Playgrounds provide interactive coding environments similar to Jupyter notebooks but with compiled performance.

Syntax and Learning Curve

Swift’s syntax borrows from multiple languages, creating something familiar yet distinct.

Type inference reduces boilerplate compared to Java while maintaining compile-time safety. Guard statements and pattern matching make code more expressive than Python’s equivalent constructs.

The learning curve is moderate for Python developers. Concepts like optionals, protocols, and value semantics require adjustment. Xcode integration helps but locks you into Apple’s development environment.

Ecosystem and Libraries

Swift’s ecosystem is Apple-centric with limited breadth outside iOS/macOS development.

SwiftUI builds modern interfaces. Core Data handles persistence. The standard library is solid but nowhere near Python’s breadth. Third-party packages exist through Swift Package Manager but the selection is narrow.

For data science or machine learning, Swift is essentially unusable. Python dominates completely. Web frameworks exist (Vapor, Kitura) but lack maturity and adoption.

Performance Profile

Swift delivers native performance on Apple hardware, significantly faster than Python.

Benchmarks show Swift running 8-10x faster than Python for computational tasks. Memory usage is efficient thanks to ARC. Startup time is near-instant for compiled binaries.

One Cultured Code case study showed 4x performance gains and 1/3 the cost migrating from Python to Swift on servers. Build times can be slow (10 minutes for 30,000 lines), which frustrates rapid iteration.

When to Choose Over Python

Pick Swift when:

• Building iOS, macOS, watchOS, or tvOS applications
• Performance matters and you’re targeting Apple platforms
• You want type safety for mobile development
• Sharing code between iOS app and backend services
• Working exclusively within Apple’s ecosystem

Skip Swift for data analysis, machine learning, web scraping, cross-platform development, or any project not targeting Apple devices. Python’s ecosystem dominates everywhere else.

PHP

PHP powers roughly 77% of websites with known server-side languages. Created in 1994 for web development, PHP remains deeply embedded in the internet’s infrastructure. WordPress, Drupal, and countless custom systems run on PHP, making it fundamentally different from Python’s general-purpose design.

Primary Use Cases

Web development is PHP’s sole reason for existence.

Content management systems like WordPress (43% of all websites) run PHP. E-commerce platforms (Magento, WooCommerce) use PHP extensively. Facebook’s backend originally ran on PHP before they created HipHop VM.

Traditional server-rendered websites favor PHP’s simplicity. The language embeds directly into HTML, making dynamic web pages straightforward. Shared hosting environments support PHP universally.

Key Technical Differences

PHP 8.x performance now rivals or exceeds Python for web-specific tasks.

PHP 7 and 8 brought massive speed improvements through the Zend Engine 3.0 and JIT compilation. Modern PHP runs 2-3x faster than Python for typical web requests. The language processes and generates HTML extremely efficiently.

Dynamic typing matches Python’s approach but with web-specific quirks. PHP’s type system has improved dramatically with type declarations and return types. Error handling through exceptions works similarly to Python.

PHP-FPM (FastCGI Process Manager) enables efficient request handling on web servers.

Syntax and Learning Curve

PHP syntax is straightforward but inconsistent in ways Python developers find jarring.

Function naming conventions are notoriously inconsistent (str_replace vs. substr). Variable names require $ prefixes. Arrays work as both lists and dictionaries, creating confusion.

The learning curve is gentle for basic web pages. Python developers find PHP familiar enough but miss Python’s consistency. Modern PHP with strict typing feels more structured than legacy codebases.

Ecosystem and Libraries

Composer manages PHP packages with millions of downloads.

Laravel and Symfony dominate modern PHP web frameworks, offering structure and features comparable to Django. WordPress plugin ecosystem is massive but quality varies wildly.

For machine learning, data science, or scientific computing, PHP is essentially absent. Python wins decisively for non-web tasks.

Testing frameworks (PHPUnit) and debugging tools are mature for web development.

Performance Profile

PHP 8.x delivers excellent web performance, often faster than Python frameworks.

Web framework benchmarks show PHP handling more requests per second than Django or Flask in many scenarios. The JIT compiler in PHP 8 significantly improves computational performance.

Memory usage is reasonable for typical web applications. PHP’s shared-nothing architecture (each request starts fresh) simplifies scaling but wastes some resources.

When to Choose Over Python

Choose PHP for:

• Traditional web development with server-rendered pages
• WordPress site development or customization
• Projects requiring cheap shared hosting
• Legacy PHP systems needing maintenance
• Teams already fluent in PHP

Avoid PHP for data science, machine learning, automation scripts, desktop applications, or anything beyond web development where Python’s versatility shines.

R

R started in 1993 as a statistical computing language for academics and researchers. It’s fundamentally different from Python’s general-purpose design, focusing exclusively on data analysis and statistical modeling. While Python invaded R’s territory with pandas and scikit-learn, R remains entrenched in specific niches.

Primary Use Cases

Statistical analysis and academic research are R’s core strengths.

Biostatistics, pharmaceutical research, and academic papers heavily use R. The language excels at statistical tests, hypothesis testing, and generating publication-quality plots. Many statistics professors teach R because it’s designed for statistical thinking.

Data visualization with ggplot2 is genuinely excellent. The grammar of graphics approach creates beautiful, reproducible plots more naturally than matplotlib.

Bioinformatics packages through Bioconductor are comprehensive and well-maintained.

Key Technical Differences

R is interpreted and generally slower than Python for general computing.

Performance is adequate for typical statistical analyses on small to medium datasets. For production systems or large-scale data processing, Python with NumPy typically wins. R was designed for interactive analysis, not production pipelines.

Vectorized operations perform well, similar to NumPy. But R’s data frame operations can be inefficient compared to pandas for very large datasets.

The language uses call-by-value semantics, which can create unexpected memory copies. This makes R less suitable for memory-constrained environments.

Syntax and Learning Curve

R’s syntax reflects its statistical heritage and feels alien to Python developers.

The <- assignment operator, formula syntax (y ~ x + z), and dataframe indexing (df[df$column > 5,]) are distinctive. Functions often return lists with named elements rather than objects.

The learning curve is steep for programmers but gentle for statisticians. Python developers find R quirky and inconsistent. Statisticians find it intuitive.

Ecosystem and Libraries

CRAN hosts over 19,000 packages focused on statistical methods.

ggplot2, dplyr, and the tidyverse ecosystem provide powerful data manipulation tools. Statistical packages cover virtually every technique imaginable. Specialized domains like survival analysis or time series have excellent R packages.

For web development, system administration, or general programming, R is essentially useless. Python dominates outside statistics and data analysis.

RStudio IDE is excellent for data analysis workflows.

Performance Profile

R is slower than Python for most computational tasks but adequate for typical statistics.

Benchmarks show Python 20-30% faster than R for general computing. For specific statistical functions, R’s optimized C implementations can be competitive.

Memory usage can be high since R copies data more readily than Python. The language wasn’t designed for big data (though packages like data.table help).

When to Choose Over Python

Pick R when:

• Doing serious statistical analysis or research
• Working in academic or pharmaceutical environments where R is standard
• You need specific statistical methods only available in R
• Publication-quality data visualization is critical
• You’re a statistician first, programmer second

Skip R for web development, general programming, production systems, or if you’re primarily building ML models where Python’s ecosystem dominates.

Julia

Julia launched in 2012 to solve the “two-language problem” where researchers prototype in Python/MATLAB then rewrite in C for performance. The language promises Python-like syntax with C-like speed through JIT compilation. It’s fundamentally different from Python’s interpreted approach.

Primary Use Cases

Scientific computing and numerical analysis are Julia’s target audience.

High-performance computing, computational physics, and quantitative finance use Julia when performance matters. The language excels at mathematical operations and algorithmic trading systems.

Differential equations, optimization problems, and simulation heavy workloads benefit from Julia’s speed. Climate modeling and complex systems research adopt Julia for computational efficiency.

Data analysis is growing in Julia but Python’s pandas still dominates in industry.

Key Technical Differences

JIT compilation delivers near-C performance with high-level syntax.

Julia compiles to LLVM machine code at runtime. First execution is slow (compilation overhead) but subsequent runs are blazingly fast. This makes Julia 10-100x faster than Python for numerical code.

Multiple dispatch (not just methods on objects) enables flexible, composable code. This paradigm differs from Python’s object-oriented approach and takes mental adjustment.

Type system is sophisticated with optional type annotations that improve performance. The compiler uses type information for optimization.

Syntax and Learning Curve

Julia syntax feels Python-like but with mathematical conveniences.

Array indexing starts at 1 (like MATLAB), which frustrates programmers. Math notation works naturally (x^2 instead of x**2). Unicode symbols work in code (α = 0.05).

The learning curve is moderate for Python users but the ecosystem is much smaller. Multiple dispatch concepts require rethinking code organization.

Debugging JIT compilation issues can be tricky compared to Python’s straightforward interpreter.

Ecosystem and Libraries

Julia’s package ecosystem is growing but nowhere near Python’s breadth.

DifferentialEquations.jl is genuinely world-class. Plots.jl provides decent visualization. DataFrames.jl attempts pandas-like functionality but with less maturity.

For machine learning, Julia lags far behind Python’s TensorFlow/PyTorch ecosystem. Web frameworks exist but are immature. System scripting is awkward compared to Python.

Package management through Pkg works well but dependency resolution can be slow.

Performance Profile

Julia delivers exceptional numerical computing performance, vastly exceeding Python.

Benchmarks show Julia running 50-100x faster than Python for numerical code, approaching C speeds. Memory usage is efficient. The JIT compilation overhead means short scripts don’t benefit much.

Startup time is slow (seconds) compared to Python’s near-instant execution. This makes Julia unsuitable for quick scripts.

When to Choose Over Python

Choose Julia for:

• Computationally intensive scientific simulations
• High-performance numerical computing where Python is too slow
• Projects where C/Fortran performance is needed with high-level syntax
• Algorithmic trading or quantitative finance
• Research code that needs to be both readable and fast

Skip Julia for web development, data analysis pipelines, machine learning projects, automation scripts, or anything where Python’s ecosystem advantage matters more than raw performance.

Scala

Scala combines functional and object-oriented programming on the JVM. Created in 2003, Scala positions itself as a better Java for complex systems. It’s fundamentally different from Python’s dynamic simplicity, offering type safety and JVM performance.

Primary Use Cases

Big data processing with Apache Spark makes Scala relevant.

Spark is written in Scala, and Scala provides the most direct API access. Data engineering pipelines processing terabytes of data often use Scala for performance. LinkedIn, Twitter, and Netflix use Scala for backend services.

Distributed systems and concurrent applications benefit from Scala’s actor model (Akka framework). Microservices handling high throughput adopt Scala for JVM stability.

Financial services use Scala for trading systems and risk analysis where correctness and performance matter.

Key Technical Differences

JVM compilation gives Scala Java-level performance, far exceeding Python.

Scala compiles to JVM bytecode like Java, delivering 5-10x better performance than Python. The type system is sophisticated with type inference reducing boilerplate. Pattern matching and algebraic data types exceed Python’s capabilities.

Immutable data structures and functional programming paradigms differ sharply from Python’s approach. Scala encourages immutability while Python defaults to mutable objects.

Integration with Java libraries is seamless. The entire Java ecosystem is accessible.

Syntax and Learning Curve

Scala’s learning curve is notoriously steep, especially for Python developers.

The language supports multiple programming paradigms (object-oriented, functional, imperative). This flexibility creates complexity. Type classes, implicits, and higher-kinded types require serious study.

Python developers face culture shock. Scala’s academic computer science influences (category theory, type theory) contrast with Python’s pragmatic simplicity.

Compilation times can be frustratingly slow for large projects.

Ecosystem and Libraries

Scala accesses the entire Java ecosystem plus Scala-specific libraries.

Spark dominates big data processing. Akka provides actor-based concurrency. Play Framework handles web development. Slick offers database access.

For data science and machine learning outside big data contexts, Python completely dominates. Scala doesn’t compete with pandas/NumPy for typical data analysis.

Build tools (sbt) are powerful but complex compared to Python’s simplicity.

Performance Profile

Scala delivers JVM performance comparable to Java, vastly faster than Python.

Benchmarks show Scala running 8-12x faster than Python for computational work. Memory usage is JVM-typical. Garbage collection behavior matches Java.

Startup time is slow (JVM warmup) but irrelevant for long-running services.

When to Choose Over Python

Pick Scala when:

• Building Apache Spark data pipelines
• Developing high-throughput concurrent systems
• You need JVM ecosystem access with functional programming
• Type safety matters for complex business logic
• Working on large-scale distributed systems

Avoid Scala for rapid prototyping, data analysis, machine learning, web scraping, or projects where Python’s simplicity and ecosystem dominate.

Dart

Google created Dart in 2011 initially as a JavaScript replacement. Flutter’s success in 2018 gave Dart new life for mobile development. The language compiles to native code or JavaScript, positioned differently from Python’s interpreted flexibility.

Primary Use Cases

Flutter mobile app development is Dart’s primary use case.

Cross-platform mobile apps for iOS and Android use Flutter/Dart. Companies wanting single codebase for mobile platforms adopt Flutter. The framework renders natively, avoiding hybrid app performance issues.

Google Ads, Alibaba’s Xianyu, and numerous startups build production apps in Flutter. The framework’s hot reload feature accelerates development significantly.

Web development through Dart-to-JavaScript compilation exists but remains niche. Angular originally supported Dart but dropped it.

Key Technical Differences

AOT compilation for mobile delivers native performance exceeding Python.

Dart compiles ahead-of-time to ARM/x64 machine code for mobile. This makes Flutter apps perform comparably to native Swift/Kotlin apps. JIT compilation during development enables hot reload.

Sound null safety (introduced in Dart 2.12) prevents null reference errors at compile time. This type safety advantage over Python prevents entire bug classes.

Isolates (not threads) handle concurrency, similar to Erlang. This model differs from Python’s threading approach and avoids shared memory issues.

Syntax and Learning Curve

Dart syntax resembles Java/JavaScript, feeling familiar to many developers.

Python developers find Dart relatively straightforward. Type annotations are optional but encouraged. Async/await works similarly to Python but with more type safety.

The learning curve is gentle compared to Rust or Scala. Flutter-specific concepts (widgets, state management) add complexity beyond the language itself.

Ecosystem and Libraries

Pub.dev hosts packages but the ecosystem is narrower than Python’s.

Flutter provides comprehensive mobile development widgets. HTTP clients, state management (Provider, Riverpod), and database packages (Hive, Drift) exist.

For data science, machine learning, or general scripting, Dart is essentially absent. Python dominates completely outside mobile development.

Flutter’s widget ecosystem is maturing but still growing compared to native development.

Performance Profile

Dart delivers solid performance for mobile apps, comparable to native development.

Flutter apps run at 60fps consistently, matching native performance. AOT compilation eliminates interpretation overhead. Memory usage is reasonable for mobile constraints.

Startup time is fast for mobile apps. Package size is larger than native apps but acceptable.

When to Choose Over Python

Choose Dart when:

• Building cross-platform mobile applications
• You want single codebase for iOS and Android
• Hot reload rapid iteration is valuable
• Working on mobile application development exclusively
• Team wants to avoid Swift and Kotlin complexity

Skip Dart for web backends, data science, machine learning, desktop applications, or server-side work where Python’s ecosystem excels.

C/C++

C (1972) and C++ (1985) are the foundational systems programming languages. They offer maximum control and performance at the cost of complexity and safety. These are fundamentally different from Python’s safety-first, high-level approach.

Primary Use Cases

Systems programming, operating systems, and performance-critical code use C/C++.

Game engines (Unreal, Unity’s core), embedded systems, device drivers, and operating systems require C/C++’s low-level control. Python itself is implemented in C (CPython).

High-frequency trading, real-time systems, and graphics rendering demand C/C++ performance. Browsers, databases, and compilers are written in these languages.

Python libraries like NumPy, TensorFlow, and pandas implement performance-critical code in C/C++ and expose Python interfaces.

Key Technical Differences

Compiled to machine code with zero abstraction overhead, C/C++ runs 50-100x faster than Python.

Manual memory management gives total control but requires careful programming. No garbage collection means predictable, deterministic performance. Pointer arithmetic and direct hardware access enable optimization impossible in Python.

C++ adds objects, templates, and modern features while maintaining C’s performance. The language is enormously complex with decades of accumulated features.

Undefined behavior and segmentation faults plague C/C++ development. Python’s safety nets don’t exist.

Syntax and Learning Curve

C/C++ syntax is verbose and complex compared to Python’s elegance.

Header files, makefiles, linkers, and compilation steps add complexity. Memory management (malloc/free, new/delete, smart pointers) requires constant attention. Template metaprogramming in C++ reaches academic complexity.

The learning curve is steep. Python developers face months of learning systems programming concepts. Debugging segfaults and memory leaks differs completely from Python development.

Ecosystem and Libraries

C/C++ ecosystems are vast but fragmented across platforms and build systems.

Boost, STL, and platform-specific libraries provide functionality. Package management is poor compared to pip (vcpkg and Conan try to help). Build systems (CMake, Make, Bazel) are complex.

For rapid development, web services, or data analysis, C/C++ is impractical. Python’s productivity advantage is massive for these domains.

Performance Profile

C/C++ delivers maximum performance, orders of magnitude faster than Python.

Benchmarks show 50-200x speed advantages over Python depending on the task. Memory usage is minimal and completely controlled. No runtime overhead exists.

Compilation times can be long for large C++ projects. Startup time is effectively zero.

When to Choose Over Python

Pick C/C++ when:

• Building operating systems, compilers, or low-level infrastructure
• Performance is absolutely critical (gaming, HFT, real-time systems)
• Working with embedded systems or hardware
• Writing Python extension modules for performance-critical code
• Maximum control over hardware is required

Avoid C/C++ for web development, data analysis, rapid prototyping, or any project where Python’s productivity and safety matter more than raw performance.

Mojo

Mojo launched in 2023 as a superset of Python that compiles to machine code. Created by Modular (founded by Swift/LLVM creators), Mojo promises Python syntax with C/Rust performance. It’s fundamentally different from Python while maintaining compatibility.

Primary Use Cases

AI/ML infrastructure and high-performance Python workloads are Mojo’s targets.

Machine learning researchers wanting Python convenience with compiled performance might adopt Mojo. Data pipelines processing massive datasets could benefit from Mojo’s speed.

The language is extremely young with limited production use. Most adoption is experimental from early adopters curious about Python-compatible compilation.

Key Technical Differences

MLIR-based compilation delivers claimed 35,000x speedups over Python for specific benchmarks.

Mojo compiles to machine code through MLIR (Multi-Level Intermediate Representation). This enables low-level optimization while maintaining high-level syntax. Parallelization and vectorization happen automatically.

Python compatibility means existing Python code should run in Mojo (eventually). But Mojo adds struct types, unsafe memory access, and compile-time features for performance.

No garbage collection overhead. Memory management is explicit when needed, implicit when safe.

Syntax and Learning Curve

Mojo extends Python syntax with performance-oriented features.

Python code is valid Mojo (the goal). But performance requires using Mojo-specific features like struct, fn (compiled functions), and type annotations. This creates two tiers: Python-compatible slow code and Mojo-optimized fast code.

The learning curve is gentle for Python developers for basic usage. Mastering performance optimization requires systems programming knowledge.

The language specification is still evolving rapidly. Breaking changes happen frequently.

Ecosystem and Libraries

The ecosystem is essentially nonexistent. Mojo is too new.

Python standard library compatibility is partial and improving. NumPy-like functionality exists but isn’t mature. ML framework integration is promised but unproven.

Tooling is minimal. IDE support is basic. The compiler itself is new and buggy.

For production work, Mojo isn’t ready. Python’s ecosystem dominates completely.

Performance Profile

Claimed performance is exceptional but real-world evidence is limited.

Benchmarks from Modular show dramatic speedups (10-35,000x) over Python. Independent verification is sparse. Compilation overhead means short scripts don’t benefit.

Memory usage is efficient with manual control available. Startup time is slower than Python but faster than Julia.

When to Choose Over Python

Mojo is too immature to choose for production work in 2025.

Experimenters curious about Python’s future might explore Mojo. Researchers wanting cutting-edge performance with familiar syntax could trial it. Most projects should wait for stability and ecosystem maturity.

If Mojo delivers on its promises, it could eventually replace Python for performance-critical ML workloads. But that future is years away. For now, stick with Python or use Rust/C++ for performance when needed.

FAQ on The Best Python Alternatives

What language is faster than Python for data processing?

Go and Rust both outperform Python significantly in raw execution speed. Julia targets scientific computing specifically and compiles to machine code.

C++ remains the performance king but requires more development time. Java offers decent speed with better memory management than Python for large datasets.

Is JavaScript a viable Python alternative for backend development?

Node.js handles concurrent connections efficiently through its event-driven architecture. TypeScript adds static typing that Python lacks natively.

The JavaScript ecosystem provides extensive packages for back-end development tasks. Performance sits between Python and compiled languages like Go.

Which Python alternative has the easiest learning curve?

Ruby’s syntax feels natural and reads almost like English. JavaScript transfers easily if you already know front-end development basics.

Go deliberately keeps its feature set minimal. The entire language specification fits in your head within weeks, not months.

Can Rust replace Python for system programming?

Rust excels at memory-critical applications without garbage collection overhead. Systems programming becomes safer through ownership rules that prevent common bugs.

The compiler catches errors Python would only reveal at runtime. Performance matches C++ while eliminating entire classes of security vulnerabilities.

What’s the best Python alternative for web applications?

JavaScript dominates full-stack web apps through frameworks like Next.js and Express. Go powers high-traffic services at Google, Uber, and Dropbox.

Ruby on Rails still ships features faster than most alternatives. TypeScript adds enterprise-grade type safety to JavaScript codebases.

Does Go handle concurrent programming better than Python?

Goroutines make concurrency simple compared to Python’s threading limitations. The Global Interpreter Lock doesn’t exist in Go’s runtime.

Channel-based communication prevents common race conditions. You can spawn thousands of concurrent operations without the overhead Python’s threads carry.

Which language offers better mobile application development than Python?

Kotlin dominates Android development as Google’s preferred language. Swift powers iOS development with better performance than Python alternatives.

Dart enables cross-platform app development through Flutter. JavaScript works through React Native for both platforms simultaneously.

Is TypeScript worth learning as a Python alternative?

Static typing catches errors before deployment that Python’s dynamic system misses. Large codebases become easier to maintain and refactor safely.

The JavaScript ecosystem transfers directly. You gain type safety without abandoning npm packages or existing JavaScript knowledge.

What Python alternative works best for machine learning?

Julia targets numerical computing with syntax data scientists understand quickly. R specializes in statistical analysis but lacks general-purpose appeal.

C++ underlies TensorFlow and PyTorch for performance-critical operations. Scala integrates with Apache Spark for distributed machine learning workflows.

Should I learn multiple Python alternatives or master one?

Master one alternative deeply before exploring others. Language features matter less than understanding software development principles that transfer between languages.

Pick based on your target industry. Fintech favors Java, game development needs C++, and startups often choose Go or TypeScript.

Conclusion

Choosing among the best Python alternatives depends on your specific project requirements and performance goals.

Languages like Go and Rust deliver exceptional execution speed for system-level tasks. JavaScript dominates web development with its massive ecosystem. Ruby maintains strong community support for rapid prototyping.

Consider type safety needs when evaluating options. Statically typed languages catch errors during compilation rather than runtime.

Performance benchmarks matter less than developer productivity for many projects. Pick a language your team knows well rather than chasing theoretical speed gains.

Modern software development demands practical trade-offs. The right choice balances learning curve, available frameworks, and long-term maintainability.

Test alternatives on small projects before committing to full codebase migrations.

If you liked this article about Python alternatives, you should check out this article about Angular alternatives.

There are also similar articles discussing PHP alternatives, Ruby alternatives, Redux alternatives, and Express alternatives.

And let’s not forget about articles on Spring alternatives, Flask alternatives, TypeScript alternatives, and Laravel alternatives.

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Bogdan Sandu

Bogdan Sandu specializes in web design, focusing on creating user-friendly websites, and innovative UI kits.

Many of his resources are available on various design marketplaces and for free on Codepen.

Over the years, he's worked with a range of clients and contributed to design publications like Design Your Way, Designmodo, WebDesignerDepot, WPDean, and Speckyboy, and Slider Revolution among others.

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