Python pods for the work where Python actually wins.
FastAPI, Django, Flask, plus the ML and data tooling. We pick Python when it is the right answer and tell you when it is not.
Python is three languages now. Most teams hire for one.
Python in 2026 is at least three different stacks: FastAPI for high-performance APIs, Django for established full-stack apps, and the ML and data toolchain (PyTorch, Pandas, DuckDB, Polars) for everything analytics-shaped. Most "Python developer" hires only do one of the three well. The pod we send is composed against your actual workload, not a generic Python label.
What changes when a Metafic pod is in your repo.
FastAPI for new high-throughput APIs
Async by default. Pydantic models for validation. Type-checked end-to-end. We do not reach for FastAPI when Django is the better fit.
Django for full-stack speed
When the admin matters, when forms matter, when batteries-included matters. Django ORM with select_related/prefetch_related discipline from day one.
Data pipelines that survive on-call
Prefect, Dagster, or Airflow depending on existing infra. Idempotent tasks, lineage tracked, alerts on actual incidents only.
ML in production, not in notebooks
Model registry, batch and online inference paths, drift monitoring. We ship the boring 80% so the data scientists can spend their time on the interesting 20%.
uv and ruff are not optional
We do not use pip and isort in 2026. Dependency installs measured in seconds, lints measured in milliseconds.
Who is on the pod for this work.
Pods scale up from here for Enterprise engagements.
Has owned multiple Python production stacks. Knows when async pays back and when it is overhead.
5+ years Python, depth in either the web stack (Django/FastAPI) or the data/ML stack as your workload requires.
pytest with property-based tests where it makes sense, integration tests against real Postgres in CI.
Tuned to generate Pydantic schemas, Django model migrations, and pytest scaffolds. Type-aware, repo-aware.
The bugs that bite this stack.
Django ORM N+1 storms in admin
Django admin is fast to build and terrifyingly slow to use at 100K+ records. We add the right select_related early.
FastAPI sync routes blocking the event loop
A single sync call in an async route stalls the whole worker. We audit and force the boundary.
Celery worker connection-pool exhaustion
The standard footgun for any team using Celery + Redis + Postgres without tuned connection limits per worker class.
Pandas vs Polars vs DuckDB chosen for the wrong workload
Pandas dies at the 4GB mark. We pick the tool against the dataset shape, not the team's muscle memory.
Honest about scope.
We will not ship Django for an API-only service when FastAPI is the better fit, and we will not ship FastAPI for a forms-heavy CRUD app when Django would have you live in two weeks. The framework is a means.
Common questions.
Django or FastAPI for a new project?
FastAPI for API-only services with high concurrency requirements. Django for full-stack apps with admin, forms, and authentication out of the box. Most teams over-pick FastAPI.
Do you do machine learning engineering?
Yes, with the caveat that we ship the engineering around your model, not your model. Model registry, deployment, monitoring, A/B testing infrastructure. Your data science team owns the model.
Python 3.13 / free-threading worth it yet?
For new projects with the right workload (CPU-bound parallel work), yes. For most web stacks, gunicorn with multiple workers is still the right answer.
Should we be on Poetry, uv, or pip?
uv for new projects in 2026. Poetry if your team is already on it and happy. Pip if you really need maximum compatibility, but you probably do not.
Ready to scope it?
A 25-minute call. We will tell you what we would do, what we would not, and whether a pod is the right shape.
Or stay in the loop. One engineering teardown a week.