Eleven years running Python in production: the language that handles your API, trains your model, processes your data pipeline, and validates your contracts, often in the same codebase with the same team. Our Python development services know where the architecture decisions matter and where they don't — and we don't treat them the same.
Why Entalogics for Python
What kills Python in production is almost always the same short list — one sync ORM call inside an async view, Celery tasks silently failing, N+1 queries nobody noticed until traffic doubled. We've inherited all of it. We know exactly where to look when a Python development company inherits a codebase that's quietly breaking.
A synchronous ORM query inside an async def view blocks the entire event loop. We audit every layer — database driver, HTTP client, cache — before writing a single new endpoint, on AWS, GCP, or wherever the stack is deployed.
Django's admin and ORM save weeks on workflow-heavy surfaces. FastAPI belongs on high-throughput API and ML inference surfaces. Forcing one to do the other's job is where Python monoliths quietly break down.
Dead-letter queues, beat scheduling, worker concurrency tuning — Celery is powerful and genuinely complex to operate. We add it only when the job complexity actually justifies a second distributed system, not before.
API responses, environment variables, inter-service payloads — all validated at entry. When a schema changes, broken contracts show up at startup, not in a customer's request log on a Sunday night.
When Python, when not
We'll tell you on the first call if the honest answer is something else — a lighter runtime, or a different stack that fits your team's actual skills better.
PICK PYTHON WHEN
CONSIDER OTHERS WHEN
WE SAY NO WHEN
What we build with Python
The shapes of Python development we ship most often. Each slots cleanly into your existing infrastructure if you already have one.
Async ASGI services, Pydantic-validated contracts, streaming inference, OpenAPI docs generated automatically — not maintained by hand. The Python API your ML team stops routing around.
Multi-tenant architecture, bounded querysets, role-aware admin, Celery for background processing. Django used as the productivity tool it actually is — not fought against on the surfaces where it doesn't belong.
Model serving with async batching to maximise GPU utilisation, streaming token delivery, Pydantic schemas that match model input and output contracts exactly so silent type coercions never reach inference.
Async ingestion, Celery-orchestrated transformation, Pydantic-validated records at every step. Pipelines that fail loudly at the bad record, not silently three hours downstream.
The spreadsheet that became five. The Jupyter notebook three analysts share over Slack. We replace all of it with real internal tooling.
Flask to FastAPI, Django 3 to Django 5, sync views to async ASGI. One endpoint at a time — the current app keeps serving while we migrate underneath it.
The playbook
Hard-won from production — not conference talks.
P01
Async views, async ORM via SQLAlchemy 2.0 or Django's async ORM, async Redis and HTTP clients. One synchronous call anywhere in the stack undoes every async gain above it.
P02
Request bodies, response models, settings, Celery task arguments — validated at the entry point. Silent type coercions don't reach the database or the model inference layer.
P03
select_related and prefetch_related called explicitly. django-debug-toolbar and nplusone in the test suite to catch query count regressions before they reach production traffic.
P04
Every task has a retry strategy, a failure handler, and a dead-letter queue. Queue depth monitored with Flower and wired to alerting before it backs up. Silent background failures are not acceptable.
P05
Pytest with Pydantic model fixtures, async support via anyio, integration tests against real Postgres with Testcontainers. The test that catches what unit tests miss.
P06
New FastAPI service behind a reverse proxy alongside the legacy Flask or Django app. One endpoint at a time — the old app keeps serving until the new one has earned every route.
Signature case
A B2B analytics platform — 3.2s p99 on exports, Celery timing out at 12%, N+1 queries on every dashboard load. Migrated to FastAPI with streaming exports and async ORM in 11 weeks. Without a feature freeze.
Before
p99 export 3.2s · Celery timeout 12% · N+1 on every dashboard · full result set in memory
After
p99 export 0.4s · Celery timeout 0.1% · prefetched queries · streaming, flat memory
Engagement shape
One endpoint at a time. The current application never goes dark.
Two senior Python engineers go through the codebase — query profiling, async compatibility audit, Celery failure analysis, memory usage inventory. A ranked, dollarized RFC, not a list of things that feel bad.
ASGI baseline, Pydantic v2 at every boundary, one production endpoint migrated end-to-end. Real p99 numbers in your dashboards, not a benchmark on a clean dev machine.
Old and new behind a reverse proxy, side-by-side. Kill-switch on every cutover. Your roadmap keeps moving throughout.
Decommission the legacy surface. Pytest coverage on critical paths. Runbook handed to your team — or we stay on retainer.
Stack
Chosen for production reliability. Already on a stack? We slot in — but greenfield, this is where we start.
Verified feedback from Upwork, Clutch, and reference calls we're happy to set up before you sign anything.
CEO, Modestly
Entalogics has successfully delivered extension prototypes on time, and we are impressed with their project management and the finished product. The client communicates with Entalogics via virtual meetings, email, and messaging apps.
Founder, Flourish Schools
Collaborating with Entalogics was a great experience. Communication was clear, we aligned quickly, and their team delivered high-quality code on time with thoughtful technical decisions.
CTO, SentryBay
Their Chromium expertise helped us ship a hardened browser with live threat controls and enterprise rollout support. Security operations became significantly faster.
Founder, G3 Ventures LLC
Entalogics delivered our custom Electron browser in six months with isolated profile workflows and stable policy sync. Agent onboarding improved by 55% after rollout.
Project Manager, Sociall
Entalogics built our iOS Chromium-based mobile app with a smooth native feel, stable media rendering, and reliable sync. We shipped faster and improved engagement after launch.
RozmeriGPT
The web dashboard and Chrome extension now share prompts, billing, and analytics in one platform. Our content turnaround improved 4x after launch.
Project Manager, Omni Browser
Entalogics built our Omni Browser on top of Chromium with deep product-level execution. They implemented an integrated VPN and Omni Crypto Wallet directly in the browser, made strong architectural decisions, and delivered quality code on schedule with clear communication.
Project Manager, Digital Office: Aura
Entalogics built Digital Office: Aura as a custom Chromium-based authentication platform. Their team modified Chromium internals and integrated our manual custom authentication flow with reliable implementation, fast delivery, and consistent communication.
Engagement
No hourly retainer billing for "thinking time." Every path is fixed-quote or transparently rated.
One product, scoped and priced once, built entirely by senior engineers. RFC to launch in 8–14 weeks.
$15k–$30k
FIXED SCOPE
A pod of senior Python engineers embedded in your Slack, your Linear, your standups. Resize or pause with 30 days' notice.
$5k / eng / mo
PER ENGINEER
For product orgs that need more than delivery — a long-term Python consulting partner: an ASGI migration program, a pipeline architecture roadmap, hiring help.
custom
PROCUREMENT-FRIENDLY
Looking to hire embedded Python engineers instead of a managed build? See our Python developer hiring page
Senior engineers
Thirty minutes with a senior Python engineer, the relevant playbook, and a candid read on whether Python is the right tool, or your problem needs a different runtime.
