Belitsoft Reviews Python Development Trends in 2025

For 20 years, Belitsoft, a custom software development studio, has been creating Python-based products, from proof of concept to MVP design, maintenance, and modernization.

As we move further into 2025, Python continues to assert itself as a strategic asset across software development, data science, AI/ML, and enterprise automation. Python’s changing landscape presents both opportunities and requirements for tech executives, from streamlining developer workflows to coordinating technology stacks with long-term business goals.

Python is finally becoming more viable in mobile and embedded environments. Thanks to tools like Pyodide (Python in the browser) and MicroPython (Python on microcontrollers), it’s possible to write once and run across multiple platforms. This makes Python not just a back-office scripting tool but a full-stack solution. This year, Python becomes indispensable across nearly every modern tech stack.

Frameworks

Modern Web Frameworks

Flask is a lightweight microframework that emphasizes usability and flexibility, with millions of downloads per month and dozens of extensions (like Flask-RESTful for APIs and Flask-SQLAlchemy for ORM).

Django (a full-stack “batteries-included” framework) continues to be popular (roughly 8 million PyPI downloads per month).

We see that the use of FastAPI, a contemporary async-first framework, to create high-performance REST/GraphQL APIs has grown rapidly.

According to StackOverflow’s 2025 survey, FastAPI usage has increased by +5 percentage points, and PyPI statistics indicate that FastAPI (with about 9 million monthly downloads) now marginally outpaces Django.

Async Frameworks (FastAPI, Starlette)

Even though Django is still a popular choice for creating reliable, full-stack web applications, developers are increasingly turning to asynchronous (async) frameworks like FastAPI, Starlette, and Sanic. Why? The async-first methodology, which is now essential for contemporary web applications, speed, and scalability.

The popularity of FastAPI in particular has skyrocketed. Why? Its asynchronous request handling, automatic data validation, and lightning-fast performance – often on par with Node.js and Go are made possible by its foundation in Starlette and Pydantic. It is no longer limited to side projects. FastAPI is being used in production by companies like Uber, Microsoft, and Netflix to create APIs that can easily manage thousands of concurrent users.

Async is no longer a “nice-to-have.” Traditional synchronous frameworks like Flask are gradually disappearing into specialized use cases as real-time features like WebSockets, background tasks, and streaming data become commonplace. Python’s contemporary async frameworks are responding to developers’ desire for applications that scale with demand with ease.

If you’re developing a contemporary Python API or service in 2025, it’s likely that you’ll start with FastAPI rather than Django.

Libraries

AI/ML Libraries

Python’s AI/ML ecosystem is broadening. Large language model toolkits such as LangChain (for building LLM-based applications) are now central to many workflows.

High-performance numerical libraries like JAX (from Google) are gaining users for auto-differentiable, GPU/TPU-accelerated computing.

Fastai (built on PyTorch) continues to simplify deep learning experimentation. Explainable AI is a growing focus: for example, Microsoft’s InterpretML provides model interpretation (EBM, SHAP/LIME) with interactive visualizations, and TokenSHAP applies SHAP explainability to large language models.

Traditional libraries like TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, and Hugging Face Transformers remain widely used for computer vision, NLP, and tabular ML. However, new entrants, such as autoML tools like PyCaret, are lowering the barrier – PyCaret allows non-experts to build and evaluate models with minimal code.

Data Science Libraries

Core data science tools are still staples. Libraries like Pandas, NumPy, SciPy, scikit-learn, and visualization tools, such as Plotly, Matplotlib, Dash, and Seaborn, underpin most workflows. However, lightweight alternatives like Polars are challenging legacy stacks for big datasets. For statistics/analytics, new libraries – PyMC or TensorFlow Probability for Bayesian modeling – are evolving, and integration with big data/storage (SQLAlchemy, Apache Spark connectors) continues to improve.

A typical modern ML deployment might use Python end‑to‑end: collecting data via Apache Kafka + Python, training models in PyTorch/TensorFlow, serving with FastAPI, and orchestrating workflows with Airflow/Dagster. Many AI-driven services – chatbots, recommendation engines, anomaly detectors – are implemented using Python libraries such as Transformers, spaCy, or OpenCV for vision tasks.

Data Engineering Libraries

A new class of high-performance libraries is emerging for data processing. Polars (a Rust-backed DataFrame library) has quickly become dominant – its 1.0 release in 2024 garnered about 89 million downloads – as many teams find single-node engines (DuckDB, Polars, Apache DataFusion) sufficient for “big data” workloads.

Apache Arrow has solidified its role as the de facto in-memory columnar format, and now underpins Polars, DuckDB, Pandas, and other libraries.

Other emerging projects like Ibis and Daft offer SQL-style or distributed-DataFrame APIs in Python. On the data-quality side, frameworks like Great Expectations remain leading tools for pipeline validation.

Tooling

Tooling and MLOps

We now see a heavy focus on the ML lifecycle. MLflow (Databricks) released v3.0 with features for managing generative models (e.g., new LoggedModel entity and evaluation suites for AI).

Data pipelines and deployments are managed by platforms such as Dataiku, Domino, or the open-source Airflow, Dagster, and Prefect. For instance, with 320 million downloads in 2024, Apache Airflow (Python) continues to be the most popular orchestration engine.

In their pursuit of governance and reproducibility, organizations frequently use model registries, feature stores, and experiment tracking. To adhere to rules and maintain stakeholder trust, explainability and monitoring tools – InterpretML, TokenSHAP, or Sentry – are combined.

Infrastructure as Code

Thanks to new lightweight packaging formats like AWS Lambda SnapStart for Python and optimized runtimes, cold start times have greatly decreased.

In the meantime, developers have been powerfully embracing Python as a Infrastructure as Code (IaC) tool. One notable developer-friendly substitute for YAML-heavy tools like Terraform is Pulumi. You define infrastructure using actual Python code, complete with loops, conditions, and modules, as opposed to writing JSON or HCL.

Without ever leaving the Python ecosystem, developers can define, implement, and oversee serverless functions with the help of the Serverless Framework Python SDK. Python takes care of everything, from creating the function logic to configuring security rules and event triggers.

Scripting and Utilities

While a few “new” CLI frameworks emerged, established tools like Typer and Click are standard for command-line scripts, and Textual, such as Rich and Textual, are popular for rich console output. The advent of AI has also spurred “agentic” scripting environments (like Cursor IDE, Windsurf), though these blur the lines between tools and frameworks. In general, Python libraries for automation (such as requests, selenium, pyautogui, etc.) remain ubiquitous for scripting and DevOps tasks.

Linters & Formatters

We see today that software developers widely use automated linters (flake8, pylint, ruff) and formatters (Black, Prettier) to improve code quality. Large-scale projects are increasingly benefiting from MyPy or Pyright for type checking as type hints become more common.

More resources for developers include Git and GitHub/GitLab for version control and CI/CD pipelines, Docker/Kubernetes for deployments, Jupyter/Colab or VS Code Notebooks for exploration, and data tools like Apache Arrow, Feather for fast I/O. A recent survey shows that 84% of developers learn on StackOverflow, 67% rely on GitHub, and 61% choose YouTube.

Hardware accelerators

More teams leverage hardware accelerators. JAX and PyTorch/XLA let Python code natively target GPUs/TPUs and optimize via just‑in‑time compilation. Frameworks like PyTorch 2.x and TensorFlow 2.19 focus on performance and native Python integration. This is critical as models grow.

Generative AI & LLMs

2025 is dominated by large AI models. Python-centric frameworks for LLMs (e.g., Hugging Face Transformers) and tools like LangChain (for LLM orchestration) are widely used. Organizations are building more AI agents and assistants, although full “AI agents” are still nascent. AutoML and No-Code AI are growing: for example, PyCaret allows domain experts to run ML pipelines (such as predicting customer churn from CRM data) without deep coding.

Tooling for AI is also improving: generative AI support has been added to ML platforms (like MLflow 3.0, which introduces gen-AI tracking), and major ML libraries (PyTorch, TensorFlow) now require Python 3.12 support.

Editors and IDEs

According to a recent survey summary, “VS Code and PyCharm cover ~80% of developers”.

In 2025, new AI-enhanced IDEs emerged: for example, Cursor and Windsurf (Codeium) are VS Code-derived environments built around AI “agents” that can autocomplete or scaffold code.

Meanwhile, keyboard-driven editors like Vim/Neovim (often with LSP plugins) and lightweight text editors (Sublime) continue to have niche followings.

Code Assistance

AI-based code assistants (GitHub Copilot, TabNine, etc.) are now ubiquitous, embedded in IDEs or used via browser integration, speeding development. Many developers “prompt” AI inside their editor to generate boilerplate, which is sometimes called vibe coding. However, trust issues remain, so human oversight is key.

Environment & Packaging

One important area of productivity is environment management. Conventional tools such as Conda and pip/venv are still in use. The Conda package manager is actually explicitly bundled for dependency control in Anaconda’s new AI platform(2025).

For installing CLI tools and creating reproducible builds, tools like Poetry and pipx are becoming more and more popular.

For consistent development/test environments, containerization (Docker) and container-native Python – through official images or Distroless – are standard.

Forecasts

While there’s no confirmed release date, the Python community is already discussing what 4.0 might look like.

Predictions include full native WASM support for Python in the browser, asynchronous-first syntax across the standard library, optional static typing enforcement, and improved performance rivaling C and Go.

The good news? Any future jump to 4.0 is expected to be backward compatible, avoiding the massive disruption we saw during the transition from 2.x to 3.x.

How Big Tech and Startups Are Scaling Python

Python isn’t just thriving in academia and solo projects – it’s deeply embedded in the backbone of enterprise software development in 2025.

Python is central to Google’s internal tooling. The company’s ML Ops team uses Python for automating training workflows, optimizing models, and serving predictions through microservices. Because of TensorFlow 3.0’s tight integration with the Google Cloud Platform, Python offers a seamless transition from experimentation to production.

Netflix’s full-scale content recommendation engine, running on Spark with PySpark and pandas, processes terabytes of viewing data daily. Python scripts are also used for content tagging, sentiment analysis, and performance testing across devices.

FinTech Startups are using Python to build fraud detection engines using scikit-learn, risk modeling apps using PyMC3, and real-time dashboards using Plotly Dash and Streamlit. These startups value Python’s readability, which allows non-engineers – like analysts and domain experts – to contribute to the codebase.

Key Recommendations for CTOs

Python has matured from a general-purpose language into a critical component of enterprise software and data ecosystems. With more than 57% of developers actively using Python and its presence across nearly 1.2 million job listings globally, its ubiquity makes it a safe long-term bet for organizations building digital capabilities.

FastAPI, Polars, and LangChain: Emerging Stars

Tech executives should be aware of FastAPI’s rise to prominence as the preferred web framework for async-native, API-first applications; in terms of professional developers’ adoption, it has eclipsed Flask and Django. Its tight integration with OpenAPI, async performance, and strong developer ergonomics are drawing enterprise teams building scalable microservices.

In the data pipeline and analytics domain, Polars is replacing legacy tools like Pandas for high-performance data manipulation, powered by Rust under the hood. Meanwhile, LangChain has emerged as a cornerstone for companies operationalizing generative AI and large language models (LLMs), enabling modular integration of LLMs into real-world applications.

Strategic takeaway: Prioritize modern frameworks like FastAPI and Polars when creating new architectures. Look at LangChain for AI solutions and LLM-driven product lines that engage with consumers.

Generative AI Drives the Market

Traditional frameworks like TensorFlow and PyTorch have given way to next-generation platforms that support LLMs and AutoML (e.g. Hugging Face Transformers, PyCaret, JAX). Full lifecycle tracking for generative models is now supported by major platforms like MLflow, which is a crucial change for businesses implementing AI at scale.

With the growing enterprise adoption of tools like InterpretML, TokenSHAP, and Great Expectations, explainability, model governance, and reproducibility have also become standard considerations.

Impact on executives: AI projects are no longer just research projects. With an emphasis on tooling that facilitates traceability, compliance, and explainability, leaders should make sure ML operations are completely integrated into IT governance.

Businesses are Competing for Skilled Python Developers

Finding skilled Python developers – particularly for positions involving data science and machine learning – is still a challenge. In the US, the average salary ranges from $100K to $130,000, with senior positions – particularly in major tech hubs – paying $150K+. Businesses are benefiting from remote and offshore Python talent more and more, with a focus on nearshore engagements.

Implication for tech execs: Build hybrid hiring strategies that combine high-value local hires with distributed, cost-effective global teams. Give retention top priority by taking advantage of growth prospects in MLOps, AI, and contemporary Python stacks.