Data and Technology Consulting · Python

Over the next few years, we’re going to see a lot of large projects grinding over familiar struggles with python at scale. And a lot of expensive migrations onto more suitable backend core technology.

It’s easy to see why teams are tempted to go “all in” on Python. If your organization already uses Python for data pipelines, analytics, and machine learning, it feels natural to extend that ecosystem into backend development — one language, one skillset, less context switching. Frameworks like Django and FastAPI make that leap appear straightforward. But this convenience is deceptive.

Python’s Roots: Simplicity Over Scale

Created in the late 1980s, Python was an early high level language that abstracted away a lot of the “complexities” of contemporary languages like C++. At the time, it was considered elegant, readable, and incredibly productive.

This made it an ideal teaching language and a staple of comp-sci courses, particularly in the United States.

Because of it’s academic history, it boasts a number of best-in-class data science libraries that grew out of academic labs or tech giant skunkworks. NumPy, Pandas, scikit-learn, and TensorFlow to name a few. Using these as part of a pipeline makes all the sense in the world, however it’s not a foundation for the enterprise backend.

Over the past two decades, other backend ecosystems — from Go to TypeScript, Java, .NET, and Ruby on Rails — have learned from Python’s quirks and shortcomings. They’ve evolved with stronger type systems, clearer concurrency models, and more consistent tooling. The result? Languages and frameworks that are better optimized for scale, safety, and long-term maintainability. They each have relative strengths and weaknesses, but they all represent a generational step forward when it comes to building large, maintainable backend systems.

Why Enterprises Historically Chose .NET and Java

Before the AI wave, enterprise software was dominated by Java and .NET (C#) — and not by accident. These ecosystems provided exactly what large organizations needed: stability, type safety, and performance.

Static Typing and Predictability
Both Java and C# offer compile-time guarantees that reduce runtime bugs, essential for massive, multi-team projects where reliability matters.
Performance and Concurrency
The JVM and CLR deliver optimized multi-threaded execution and JIT compilation — areas where Python still struggles due to its GIL.
Ecosystem Maturity
Frameworks like Spring Boot and ASP.NET Core offer out-of-the-box support for authentication, transactions, and scalability — crucial for enterprise-grade applications.
Longevity and Backward Compatibility
Both languages have a proven record of maintaining stability across decades — a must for enterprise systems with lifespans longer than most startups.

Modern Alternatives: Go, TypeScript, Ruby on Rails, and PHP

While Java and .NET remain enterprise stalwarts, a new generation of backend ecosystems has emerged that combine developer productivity with scalability — areas where Python lags behind.

Go (Golang)

Go was designed by Google for building high-performance, concurrent systems. Its simplicity rivals Python’s, but it offers true concurrency through lightweight goroutines and channels. Go is perfect for cloud services, microservices, and infrastructure tooling, where efficiency and throughput matter most. Unlike Python, Go compiles to native code and scales effortlessly across cores.

TypeScript (Node.js)

TypeScript brings static typing to the JavaScript ecosystem, creating a unified environment for frontend and backend development. Frameworks like NestJS or Express (with TypeScript) deliver type-safe, scalable APIs that integrate cleanly into modern web stacks — something Python frameworks like Flask or Django can’t match in developer productivity at scale.

Ruby on Rails

Ruby on Rails popularized “convention over configuration”, drastically accelerating web development. Its opinionated structure and robust ecosystem make it a great choice for startups and mid-sized enterprises that need rapid iteration without compromising maintainability.

PHP (Laravel, Symfony)

Modern PHP — especially with Laravel and Symfony — has matured into a powerful, performance-conscious platform. It offers excellent tooling for web services, broad hosting support, and clear deployment pathways. For many organizations, it remains a cost-effective, scalable option with fewer operational headaches than Python-based web stacks.

Python still has a place

In a modern architecture, Python has a place.

Data ingestion, transformation, and orchestration (Pandas, Airflow, PySpark)
Machine learning and analytics (TensorFlow, PyTorch, scikit-learn)

But build your core backend with languages designed for scale, performance, and longevity.

.NET and Java for enterprise
Go for high-performance, concurrent services
TypeScript, Ruby on Rails or PHP for rapid, maintainable web development

Otherwise you risk an expensive lesson is why we invented those languages in the first place.