Hire Python Developers

Both are excellent. Choose based on your project needs.

Choose Django for:

• Full-featured applications: Built-in admin panel, ORM, authentication, forms
• Rapid development: Batteries-included philosophy gets you to market faster
• Traditional web apps: Server-rendered pages, form handling, session management
• Mature ecosystem: 19+ years of packages, patterns, community knowledge
• Monolithic applications: Everything you need in one framework

Choose FastAPI for:

• API-first applications: Modern async REST APIs, microservices
• Performance: Async/await for high-concurrency workloads
• Auto-documentation: Swagger/OpenAPI docs generated automatically
• Type safety: Pydantic models provide runtime validation and editor support
• Modern Python: Leverages Python 3.7+ features (type hints, async)

Real-world usage:

• Django: Instagram, Pinterest, Mozilla, The Washington Post, Spotify
• FastAPI: Uber, Netflix (microservices), Microsoft, many AI/ML startups

Our recommendation:

• Choose Django for full-stack web applications, admin panels, traditional SaaS
• Choose FastAPI for API-only backends, microservices, ML model serving, high-performance APIs

Many companies use both: Django for main app, FastAPI for performance-critical microservices.

Both are excellent backend choices. The decision depends on team and use case.

Choose Python for:

• Data-heavy applications: Python excels at data processing, analysis, ML
• Machine learning integration: Best ML ecosystem (PyTorch, TensorFlow, scikit-learn)
• Scientific computing: NumPy, SciPy, Pandas for numerical work
• Rapid prototyping: Clean syntax, extensive libraries
• Team expertise: If your team knows Python

Choose Node.js for:

• Real-time applications: WebSockets, chat, live updates (Node excels here)
• JavaScript full-stack: Same language for frontend and backend
• Microservices: Lightweight, fast startup times
• High I/O concurrency: Event-driven architecture handles many connections
• NPM ecosystem: Largest package registry

Performance:

• Node.js: Better for I/O-bound operations (APIs, real-time)
• Python: Better for CPU-bound operations (data processing, ML)

Hiring: Both have large talent pools; Python slightly broader (used in data science + web).

Our recommendation:

• Choose Python for data-intensive apps, ML integration, or data science teams
• Choose Node.js for real-time features, JavaScript teams, or microservices

We have senior developers in both ecosystems.

Yes. Modern Python developers are proficient in async/await patterns.

Async use cases:

• High-concurrency APIs (handling many simultaneous requests)
• I/O-bound operations (database queries, HTTP requests, file I/O)
• WebSocket servers
• Real-time data streaming
• Microservices with many external API calls

Async frameworks & tools:

• FastAPI (async by default)
• aiohttp (async HTTP client/server)
• asyncio (Python’s async library)
• asyncpg (async PostgreSQL)
• Uvicorn (async ASGI server)

When to use async:

• API serving many concurrent users
• I/O-heavy workloads (not CPU-heavy)
• Real-time features
• High-throughput requirements

When sync is fine:

• CPU-bound tasks (data processing, ML inference)
• Traditional CRUD applications
• Low-concurrency internal tools
• When simplicity matters more than performance

Our Python developers understand when async provides real benefits vs added complexity.

Absolutely. Modern Python developers bridge ML and software engineering.

What ML deployment entails:

• Model serving: Exposing models via REST API (FastAPI, Flask)
• Scalability: Handling prediction requests at scale
• Model versioning: Managing multiple model versions
• Monitoring: Tracking prediction latency, accuracy, drift
• Retraining pipelines: Automated model updates with new data
• Infrastructure: Docker, Kubernetes, cloud deployment

Common ML deployment stack:

• FastAPI for serving predictions
• Docker for containerization
• Redis for caching predictions
• PostgreSQL for logging predictions
• AWS SageMaker or GCP Vertex AI for managed ML
• MLflow for model registry and tracking

ML frameworks our developers work with:

• PyTorch (most popular for modern ML)
• TensorFlow / Keras
• scikit-learn (traditional ML)
• Hugging Face Transformers (NLP/LLMs)
• ONNX (model optimization)

Production considerations:

• Prediction latency optimization
• Batch vs real-time inference
• Model monitoring and alerts
• A/B testing different models
• Feature engineering pipelines

We match you with Python developers who understand both ML and production engineering.

Yes. Python is the industry standard for data pipeline development.

Data pipeline capabilities:

• ETL workflows: Extract, Transform, Load from various sources
• Data ingestion: APIs, databases, files, streaming sources
• Data transformation: Cleaning, aggregation, enrichment
• Data warehouse integration: Loading to Snowflake, BigQuery, Redshift
• Orchestration: Airflow, Prefect for scheduling and monitoring
• Real-time streaming: Kafka, Kinesis integration

Data engineering stack:

• Pandas: Data manipulation and analysis
• Apache Spark (PySpark): Big data processing
• Airflow: Workflow orchestration
• dbt: Data transformation in warehouse
• SQL: Complex queries and analytics
• Cloud: AWS Glue, GCP Dataflow

Common use cases:

• Aggregating data from multiple APIs
• Processing log files and analytics
• Building reporting dashboards
• Data warehouse ETL
• Real-time event processing

Scale:

• Small: Pandas + PostgreSQL (GBs of data)
• Medium: Pandas + Spark + Cloud (TBs)
• Large: Spark + distributed systems (PBs)

Our Python developers architect data pipelines appropriate for your scale and complexity.

Django admin is powerful for internal tools, but custom admin panels are often needed.

Use Django admin when:

• Internal team needs CRUD operations
• Rapid development is priority
• Data model is straightforward
• Users are technical (comfortable with default UI)
• Budget/time is limited

Build custom admin when:

• Non-technical users need intuitive UX
• Complex workflows beyond CRUD
• Custom dashboards and analytics
• White-label admin for clients
• Mobile-responsive design required
• Advanced permissions beyond Django’s built-in

Hybrid approach (common):

• Use Django admin for internal team
• Build custom admin for clients/end-users
• Customize Django admin with third-party packages (django-grappelli, django-jet)

Customization options:

• Django admin customization (ModelAdmin)
• React + Django REST Framework
• Vue + DRF
• Server-rendered templates with HTMX

Our Django developers can leverage built-in admin or build custom admin panels based on requirements.

Testing and code quality are fundamental to professional Python development.

Testing practices:

• pytest: Modern testing framework (unit + integration)
• unittest: Python’s built-in testing
• Coverage: Aim for 80%+ test coverage
• Fixtures: Factory Boy, pytest fixtures
• Mocking: unittest.mock, pytest-mock
• Integration tests: Testing API endpoints, database interactions

Code quality tools:

• Type hints: mypy for static type checking
• Linting: pylint, flake8, ruff
• Formatting: black (opinionated formatter)
• Import sorting: isort
• Pre-commit hooks: Automated checks before commit

CI/CD practices:

• Automated tests on every PR
• Code coverage reporting
• Linting in CI pipeline
• Deployment automation
• Security scanning (bandit, safety)

Documentation:

• Docstrings (Google/NumPy style)
• Type hints for function signatures
• README with setup instructions
• API documentation (Swagger/OpenAPI)

Our Python developers follow these practices to deliver maintainable, reliable code.

Yes. While Python isn’t the fastest language, proper optimization makes it production-ready.

Performance optimization strategies:

Database optimization:

• Query optimization (select_related, prefetch_related in Django)
• Database indexing
• Connection pooling
• Query result caching

Caching:

• Redis for session/API caching
• Django cache framework
• CDN for static assets
• HTTP caching headers

Async patterns:

• asyncio for I/O-bound operations
• Background jobs (Celery) for heavy tasks
• Database connection async (asyncpg)

Code optimization:

• Profiling (cProfile, py-spy)
• Algorithmic improvements
• NumPy for numerical operations
• Cython for performance-critical code

Architecture:

• Horizontal scaling with load balancers
• Microservices for independent scaling
• Queue-based architecture
• Database read replicas

When Python isn’t fast enough:

• Use Rust/Go for performance-critical microservices
• Compile with Cython
• Leverage NumPy/Pandas (C-optimized)
• Profile first, optimize bottlenecks

Reality: Most “Python is slow” concerns are premature optimization. Proper architecture handles millions of requests.

Python 3.9 or higher is recommended for new projects in 2025.

Python version landscape:

• Python 2.7: End-of-life since 2020, never use
• Python 3.7: EOL June 2023, avoid
• Python 3.8: EOL October 2024, legacy only
• Python 3.9: Minimum recommended (dict merge, type hints improvements)
• Python 3.10: Good choice (pattern matching, better error messages)
• Python 3.11: Recommended (20-30% faster than 3.10)
• Python 3.12: Latest stable (even faster, better typing)

Key features by version:

• 3.9: Dict merge (|), type hints improvements
• 3.10: Pattern matching (match/case), better error messages
• 3.11: Major performance improvements (20-30% faster)
• 3.12: More performance gains, better typing

Our recommendation:

• New projects: Python 3.11 or 3.12
• Legacy projects: Upgrade to at least 3.9
• ML projects: Check framework compatibility (PyTorch, TensorFlow)

Upgrade considerations:

• Django 4.x supports Python 3.8+
• FastAPI supports Python 3.7+
• Most libraries support 3.9+

Our Python developers write modern Python leveraging recent language features.

Celery is the standard, but alternatives exist depending on needs.

Use Celery when:

• Complex task workflows (retries, chaining, groups)
• High-volume background processing
• Task scheduling (periodic tasks)
• Distributed task execution
• Mature, battle-tested solution needed

Alternatives to consider:

RQ (Redis Queue):

• Simpler than Celery
• Good for straightforward job queues
• Easier to debug
• Redis-only (no RabbitMQ option)

Dramatiq:

• Modern alternative to Celery
• Cleaner API
• Better for simpler use cases

APScheduler:

• Simple scheduled tasks
• No external dependencies
• Good for single-server apps

Cloud solutions:

• AWS Lambda (serverless functions)
• Google Cloud Tasks
• Azure Functions

When you don’t need background jobs:

• Lightweight async tasks (use asyncio)
• Low-volume processing
• Simple scheduled tasks (cron)

Our recommendation:

• High-volume: Celery
• Simple queue: RQ
• Scheduled only: APScheduler or cron
• Serverless: AWS Lambda

Our Python developers architect background job systems appropriate for your scale.

Yes. Python is excellent for microservices, especially with FastAPI.

Python microservices advantages:

• FastAPI: Modern, async, built for APIs
• Lightweight: Minimal resource footprint
• Docker-friendly: Easy containerization
• Async support: High concurrency
• Fast development: Rapid prototyping

Typical microservices stack:

• FastAPI for HTTP APIs
• gRPC for inter-service communication
• PostgreSQL per service (or shared)
• Redis for caching
• Docker + Kubernetes
• Message queues (RabbitMQ, Kafka)

Microservices patterns:

• API Gateway (Kong, Traefik)
• Service discovery
• Circuit breakers
• Event-driven architecture
• CQRS where appropriate

When to use microservices:

• Large teams needing independent deployment
• Different scaling requirements per service
• Polyglot architecture (mix Python, Go, etc)
• Domain-driven design benefits

When NOT to use microservices:

• Small team (< 10 developers)
• MVP or early-stage product
• Monolith is working fine
• Operational complexity outweighs benefits

Our recommendation: Start with a well-architected monolith (Django or FastAPI). Extract microservices only when clear scaling needs emerge.

Our Python developers can build both monoliths and microservices architectures.

Yes. Solo developers, pairs, or full cross-functional teams.

Option 1: Solo Python Developer

• Perfect for adding backend capacity
• Integrates with existing team

Option 2: Python + Frontend Pair

• 1-2 Python Developers
• 1 Frontend Engineer (React, Vue)

Option 3: Full Product Team

• 2-3 Python Developers
• 1-2 Frontend Engineers
• 1 UI/UX Designer
• 1 QA Engineer
• 1 Product Manager (optional)

All managed by Ideaware for HR, payroll, and retention.