Python Development Services with Django & FastAPI

Real performance in Python: async I/O and multiprocessing for CPU-bound workloads, with stable memory management in production.

Request proposal View stack
  • <100ms P95 Inference
How we build: requirements, architecture, reviewed code, reversible deployment, measured performance and evolution with your team Every metric sets the next iteration 01 · Requirements What the businessneeds 02 · Architecture Documenteddecisions 03 · Code Reviewed andtested 04 · Deployment Automated andreversible 05 · Performance Core Web Vitals inthe green 06 · Evolution Your team asks, wedeliver
  1. 01 · Requirements What the business needs
  2. 02 · Architecture Documented decisions
  3. 03 · Code Reviewed and tested
  4. 04 · Deployment Automated and reversible
  5. 05 · Performance Core Web Vitals in the green
  6. 06 · Evolution Your team asks, we deliver
  7. Every metric sets the next iteration
Scroll

Definition

Why Python for Backends?

Python dominates ML/AI and data science. FastAPI with async/await handles 10k+ req/s for I/O-bound. For CPU-bound, multiprocessing avoids the GIL. Pydantic v2 validates data 10x faster.

Why

Data Science, ML, APIs, Automation

Python for every compute-intensive use case

Python is not just "the ML language". It's the optimal runtime for data engineering (Polars, pandas), ML inference (PyTorch, ONNX), async APIs (FastAPI), and automation (scripts, ETL). The GIL is managed: async for I/O, multiprocessing for CPU-bound.

api/main.py
# FastAPI + Type Safety
@app.get("/products/{id}")
async def get_product(
id: int,
db: Session = Depends(get_db)
) -> ProductSchema:
return db.query(Product).get(id)
  • 100% Type Hints
  • Auto OpenAPI
  • ✓ Async

In numbers

Production Metrics

  • 100% Type coverage
  • >80% Test coverage
  • 0 Memory leaks

Deliverables

What We Deliver

Every Python project includes:

Included

  • Complete async FastAPI API
  • Pydantic v2 for data validation
  • SQLAlchemy 2.0 + Alembic (migrations)
  • Tests with pytest (>80% coverage)
  • mypy strict + Ruff (linting)
  • CI/CD pipeline configured
  • Docker + Kubernetes ready
  • Automatic OpenAPI documentation

Not included

  • ML model serving (ONNX/PyTorch)
  • Monthly maintenance

Summary

For Decision Makers

Python is the ML/AI language. Integrating models with APIs is direct, no bridges between languages.

FastAPI is Python's fastest framework, comparable to Node.js for I/O-bound.

Mature ecosystem: PyTorch, TensorFlow, scikit-learn, pandas/polars directly accessible.

For the CTO

For CTOs

FastAPI async with uvicorn/gunicorn workers. Pydantic v2 is 10x faster than v1.

GIL-aware: async for I/O, ProcessPoolExecutor for CPU-bound, Celery for background jobs.

ONNX Runtime for optimized inference. Model serving with Triton or custom FastAPI endpoints.

What's included

Production Stack

  • FastAPI async
  • Pydantic v2
  • SQLAlchemy 2.0
  • Celery + Redis
  • PyTorch / ONNX
  • Docker + K8s

Who it is for

Is It for You?

Who it's for

  • Teams needing ML inference in production
  • Compute-intensive backends (data processing, ETL)
  • Integrations with data science ecosystem
  • APIs consuming PyTorch/TensorFlow models
  • Projects with I/O-bound concurrency requirements

Who it's not for

  • Simple web apps where Node.js suffices
  • Mobile backends without ML component
  • Projects where <10ms latency is critical (consider Go/Rust)

Risks and how we cover them

Risk Reduction

How we manage Python-specific risks in production.

  1. 01

    GIL blocking CPU on intensive operations

    Mitigation

    multiprocessing/ProcessPoolExecutor for CPU-bound. Profiling with py-spy to find bottlenecks.

  2. 02

    Memory leaks in production

    Mitigation

    tracemalloc + objgraph in staging. Sustained load tests before release. Heap alerts in production.

  3. 03

    Slow ML model at inference

    Mitigation

    ONNX Runtime for cross-platform optimization. Batching to maximize throughput. GPU inference when applicable.

  4. 04

    Dependencies with vulnerabilities

    Mitigation

    pip-audit + Safety in CI/CD. Renovate/Dependabot for automatic updates.

How we work

Methodology

  1. 01

    API Spec

    OpenAPI spec + Pydantic models first.

  2. 02

    Core

    Business logic with tests. mypy strict.

  3. 03

    ML Integration

    Optimized model serving. ONNX when applicable.

  4. 04

    Production

    Docker, K8s, monitoring, alerts.

Use cases

Use Cases

  • ML Inference APIs

    Serve PyTorch/ONNX models in production.

    P95 < 100ms

  • ETL Pipelines

    Data processing with Polars/pandas.

    10x faster

  • Analytics Backends

    APIs for dashboards and reporting.

    Real-time insights

The proof

Data Science Credentials

Team with 10+ years of experience in production Python. From notebooks to APIs serving millions of daily inferences. FastAPI, PyTorch, ONNX Runtime. ML that scales.

  • 10+ Years with Python
  • 80+ APIs in production
  • Minimum test coverage >80%
  • Guaranteed uptime 99.9%

Technologies

Technologies

  • Python 3
  • FastAPI
  • Pydantic v2
  • SQLAlchemy 2.0
  • Celery
  • Redis
  • PyTorch
  • ONNX Runtime
  • Polars
  • pytest
  • mypy
  • Ruff

FAQ

Frequently Asked Questions

Python or Node.js for my API?

Python if you have ML/data science. Node.js for pure I/O without ML. Python with FastAPI is comparable for I/O-bound performance.

Doesn't the GIL limit performance?

For I/O-bound, async avoids the problem. For CPU-bound, multiprocessing. The GIL is manageable with correct architecture.

How do you serve ML models?

ONNX Runtime for cross-platform optimization. Custom FastAPI endpoints or Triton Inference Server for high throughput. For semantic search over your data, see enterprise RAG.

Django or FastAPI?

FastAPI for pure APIs. Django if you need admin, mature ORM and plugin ecosystem (its PHP equivalent is Laravel). FastAPI is faster and more modern.

Does it include team training?

Yes. Initial pair programming, architecture documentation, FastAPI/async workshops.

What monitoring is included?

Prometheus + Grafana. ML-specific: inference latency, drift detection, model versioning.

Hosting included?

We configure on AWS/GCP/Azure. GPU instances if needed. EU servers for GDPR.

Post-launch support?

Monthly contracts. Model retraining, optimization, security updates.

Next step

ML Model in Notebooks That Doesn't Scale?

From Jupyter to production. ML architecture serving millions of requests.

Request proposal
  • No commitment
  • Response in 24h
  • Custom proposal
Last updated: July 2026

Let's talk.

Initial technical consultation

AI, security and performance. Diagnosis with phased proposal.

  • NDA available
  • Response <24h
  • Phased proposal

Your first meeting is with a Solutions Architect, not a salesperson.

Request diagnosis