When starting a new AI project, the appeal of brand-new technologies can be strong. Yet, the proven reliability of a 'boring' tech stack often provides the most practical foundation for long-term success.
Choosing established technologies can save you from the difficulties of untested and overly complex systems. This article outlines a dependable, 'boring' tech stack that is not only reliable but surprisingly well-suited for future advancements, including AI.
Why Opt for Established Technologies?
New technologies often come with heavy marketing, focusing on their strengths while omitting their limitations. This can lead teams to choose tools that are not the best fit for their project's requirements or current stage. It's important to look past the sales pitch on a landing page to understand the real-world effects of adopting new tech.
Known Quantities
Established technologies, often seen as 'boring', bring years of proven reliability, widespread community support, and extensive documentation. These factors make them a safe bet, especially for critical projects where stability is a priority. Knowing the limitations and strengths of these tools allows developers to plan better and avoid surprises during development and deployment.
The 7 Essential 'Boring' Technologies for 2025
Here is a recommended stack of technologies that balances stability with the features needed to build modern AI applications.
1. Backend: Django
Django remains a top choice for backend development because of its straightforward setup for administrative functions and its maturity. It famously supports high-traffic platforms like Instagram, demonstrating its ability to scale effectively for millions of users. This scalability is a major benefit for any application expecting significant growth.
Future-Proofing with AI
Django is increasingly prepared for the future, especially with the growing ecosystem of AI libraries that integrate well with it. Django REST Framework makes it simple to build APIs that serve machine learning models. This compatibility ensures that your applications can adopt advanced features, making Django a strategic choice.
For instance, creating an API endpoint to serve a model's prediction is direct:
# my_app/views.py
from rest_framework.views import APIView
from rest_framework.response import Response
# Assume 'my_model' is your loaded AI model
# from .ml_models import my_model
class PredictionAPIView(APIView):
def post(self, request, *args, **kwargs):
# Get data from the request
input_data = request.data.get('input')
# Perform prediction (this is a simplified example)
# result = my_model.predict([input_data])
result = {"prediction": "example_output"} # Placeholder
return Response(result)
Community and Documentation
The active community around Django produces a rich ecosystem of packages and tools, improving the frameworkâs capabilities and security. Extensive documentation and community support make Django a reliable choice for developers, helping them solve problems and stay updated with best practices.
2. Caching and Web Serving: Redis and Nginx
Redis, an in-memory data structure store, is excellent for caching and message brokering, which improves application responsiveness. Nginx, known for its efficiency as a web server and reverse proxy, pairs well with Redis by providing stability and high performance with a low memory footprint. Together, these tools form a solid infrastructure for high traffic.
A typical Nginx configuration to serve a Django application might look like this:
# /etc/nginx/sites-available/my_project
server {
listen 80;
server_name your_domain.com;
location / {
proxy_pass http://127.0.0.1:8000; # Forwards requests to Gunicorn
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
}
}
3. Database: PostgreSQL
Choosing the right database is fundamental, and PostgreSQL offers a mature, dependable solution with extensive features. While MySQL is also a strong choice, PostgreSQL's ability to handle complex queries and its extensibility make it particularly suited for AI applications.
The AI Connection: Vector Support
A standout feature is the pgvector extension, which allows you to store and query vector embeddings directly within the database. This is perfect for building recommendation systems, semantic search, or retrieval-augmented generation (RAG) systems.
Here is how you might create a table to store document embeddings:
-- First, ensure the extension is enabled
CREATE EXTENSION IF NOT EXISTS vector;
-- Create a table to store items and their vector embeddings
CREATE TABLE items (
id bigserial PRIMARY KEY,
text_content TEXT,
embedding vector(1536) -- Example dimension for OpenAI embeddings
);
This allows you to find similar items using vector distance functions right in your SQL queries, combining the reliability of Postgres with modern AI capabilities.
4. Frontend: React with Next.js
React continues to lead in frontend development with its component-based architecture, which helps in creating reusable and maintainable UI components. When combined with the Next.js framework, React gets server-side rendering and static site generation, which improves performance and SEO. This combination is powerful for creating fast and modern web applications.
5. Scripting and Operations: Bash Scripts
In an environment where complex tools like Kubernetes are common, choosing simple Bash scripts for routine automation can be a breath of fresh air. This approach streamlines the development process and reduces the cognitive load on your team. Bash scripts provide a direct and efficient way to manage deployment, system updates, and other operational tasks.
For example, a simple script to deploy code changes could be:
#!/bin/bash
# A simple script to deploy the latest code from git
echo "Pulling latest changes from main branch..."
git pull origin main
echo "Installing/updating Python dependencies..."
pip install -r requirements.txt
echo "Running database migrations..."
python manage.py migrate
echo "Restarting the application server..."
sudo systemctl restart gunicorn
This direct approach ensures your team can focus more on development and less on configuration management.
6. Architecture: The Majestic Monolith
The trend towards microservices is not the right answer for every project. Starting with a "majestic monolith" can greatly simplify development, especially for new projects that do not yet require the scalability of a microservice architecture. This style allows teams to build features within a single, unified codebase, making maintenance and testing easier.
An important principle is that if a team struggles to manage a monolithic architecture effectively, moving to microservices could make those challenges worse. Mastering a monolith requires a disciplined approach to software design, and the skills learned are essential for successfully handling the distributed nature of microservices later on.
7. Version Control: Git and GitHub
This might be the most 'boring' but most essential part of any stack. Git is the standard for version control, and platforms like GitHub or GitLab are central to modern software development. For AI projects, they are indispensable for tracking changes in code, notebooks, and even large model files with Git LFS (Large File Storage).
Using GitHub Actions, you can automate CI/CD pipelines for both your application and your machine learning models. You can create workflows that automatically test new code, build containers, and deploy to your servers, all triggered by a simple git push.
Conclusion
Choosing the right technology for your 2025 AI project is about balancing new capabilities with proven dependability. This 'boring' tech stack offers a solid foundation that meets current needs while preparing your project for future growth.
By selecting these established tools, you use technologies capable of scaling to meet high demands and integrating with AI enhancements. This approach allows your team to focus on innovation and building valuable features, not on managing overly complex infrastructure.
Ultimately, while the appeal of new technologies is strong, the importance of stability, community support, and maturity in your tech stack is difficult to overstate. This strategy supports your immediate development needs and positions your project to evolve successfully.
