PythonDevelopmentServiceswithDjango&FastAPI

The GIL is no excuse. Async I/O, multiprocessing for CPU-bound, memory management that doesn't explode in production.

<100ms P95 Inference
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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

What We Deliver

Every Python project includes:

Incluido

  • 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

No incluido

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

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 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.

Production Stack

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

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)

Risk Reduction

How we manage Python-specific risks in production.

GIL blocking CPU on intensive operations

Mitigation:

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

Memory leaks in production

Mitigation:

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

Slow ML model at inference

Mitigation:

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

Dependencies with vulnerabilities

Mitigation:

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

Methodology

01

API Spec

OpenAPI spec + Pydantic models first.

02

Core

Business logic with tests. mypy strict.

03

ML Integration

Optimized model serving. ONNX when applicable.

04

Production

Docker, K8s, monitoring, alerts.

Use Cases

ML Inference APIs

Serve PyTorch/ONNX models in production.

ETL Pipelines

Data processing with Polars/pandas.

Analytics Backends

APIs for dashboards and reporting.

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%

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.

ML Model in Notebooks That Doesn't Scale?

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

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Last updated: July 2026

Initial technical
consultation.

AI, security and performance. Diagnosis with phased proposal.

NDA available
Response <24h
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