Avana Hub
Web Development›Backend Development›Python

Python Development Services

Flexible. Service-Driven. Built for APIs, Automation, and Products That Need to Evolve.

A structured Python backend development system designed to help businesses build flexible, scalable, and service-oriented backend foundations — covering APIs, automation workflows, data-connected systems, integrations, and long-term product evolution.

Flexible

Service-oriented architecture

Service-Driven

APIs & system connectivity

Automation-Ready

Workflows & processing logic

Service at a Glance

Six Pillars of Flexible Python Backend Development.

Python development isn't one thing — it's a set of distinct backend capabilities that shape service flexibility, automation logic, system connectivity, and long-term product adaptability.

API & Service Development

FastAPI, Django REST Framework, and Flask-based API backends — designed around a clear service contract before implementation begins. Typed endpoints, versioning strategy, authentication, rate limiting, and consistent error structures built in from day one.

→ APIs that third-party teams and your own frontend can integrate with confidence.

Django / Flask / FastAPI Paths

Framework selection is a product architecture decision, not a habit. Django for structured, ORM-heavy, and admin-supported web applications. FastAPI for high-performance async APIs. Flask for lightweight, flexible service-oriented backends. The right framework follows the product requirements.

→ Framework choice that serves the product architecture — not the other way around.

Automation & Workflow Logic

Background task processing via Celery with Redis or RabbitMQ brokers, scheduled jobs, event-driven automation flows, retry and failure logic, and queue monitoring. Automation built as a first-class backend concern, not bolted on after launch.

→ Backend systems that run workflows reliably without blocking the request cycle.

Data-Connected Backend Systems

Backend logic built for products that process, transform, or serve structured data — SQLAlchemy or Django ORM with clean query patterns, data pipeline integration hooks, reporting and aggregation layers, and clean separation between data access and business logic.

→ Products where data is a first-class backend concern, not an afterthought.

Integrations & System Connectivity

Typed wrapper clients for every external system — payment gateways, CRMs, ERPs, communication platforms, webhooks, and third-party data sources. Built with retry logic, circuit breakers, and structured error handling. Integrations that degrade gracefully under external failure.

→ Backend systems that connect cleanly and fail predictably.

Scale, Support & Modernization

Deployment-ready Python backends configured for production: Gunicorn/Uvicorn behind Nginx, environment-based configuration, health checks, Sentry error tracking, and structured logging. Long-term support and incremental modernization of legacy Python systems alongside new development.

→ Python backends that stay maintainable across teams and product generations.

Is Python the Right Fit?

Strong Fits and Honest Mismatches.

Python wins where service flexibility, automation logic, and backend adaptability matter most. Outside those use cases, we'll point you to the stack that fits better — and explain why.

Strong Fit — Choose Python When…

Your product architecture is service-heavy — APIs, integrations, and processing workflows are central, not peripheral

Python's service-oriented design patterns (service classes, dependency injection, clean module boundaries) suit products where backend logic is composed of connected, independently evolving services rather than monolithic controllers.

You need a high-performance async API backend — especially for products with concurrent request requirements

FastAPI's ASGI-native async runtime handles concurrent workloads cleanly. Combined with Pydantic-validated schemas, OpenAPI documentation, and type-checked endpoints, FastAPI is one of the fastest and cleanest API frameworks available.

Your product requires automation workflows, background processing, or scheduled data operations

Celery with Redis or RabbitMQ is a mature, production-tested Python task queue. Scheduled tasks, retry logic, job monitoring with Flower, and event-driven automation flows are natural Python backend patterns.

Your backend must connect to and process data from multiple sources, APIs, or structured datasets

SQLAlchemy's clean query composition, Django ORM's relationship management, and Python's data processing ecosystem (Pandas, dataclasses, Pydantic) make Python a natural fit for data-connected product backends.

Your team wants a backend that can evolve cleanly — adding services, integrations, and processing layers over time

Python's explicit module structure, type annotation system, and ecosystem breadth make it well-suited for backends that need to grow across multiple product capabilities without architectural rewrites.

You need a structured, content-heavy web application with built-in admin, ORM, and security foundations

Django's batteries-included approach — ORM, admin panel, auth system, middleware, template engine, form handling — makes it the strongest choice for complex web applications where framework discipline drives delivery speed.

Honest Mismatches — Consider Another Path When…

Your product needs a structured admin-heavy application backend with deep workflow orchestration and RBAC enforcement

Laravel's Filament admin, Eloquent ORM, Gates & Policies, and queue infrastructure are purpose-built for structured, admin-heavy PHP applications. If your backend is primarily a well-organized web application with complex admin requirements, Laravel delivers faster.

Your backend is primarily real-time — WebSockets, push events, and sub-100ms event propagation are the core product

Node.js's event loop architecture is built for sustained real-time workloads. Python's async support is solid but Node.js has deeper tooling and ecosystem maturity for real-time-first products.

Your product needs a simple, practical, web-native backend without framework complexity or broad service architecture

A lightweight PHP backend handles simple web-native products cleanly, with excellent hosting compatibility and minimal overhead. Python's ecosystem is broader than needed for purely web-native products with no automation or service requirements.

Not sure if Python fits your product?

We run a 30-minute Stack-Fit call — no commitment. We give you an honest assessment of whether Python, Laravel, Node.js, or PHP maps better to your application and team.

Service Scope

Eight Areas of Custom Python Backend Development.

Each area is a discipline with its own architecture patterns, Python-specific tooling, and business outcomes. Most engagements draw from three or more areas concurrently.

01

Custom Python Backend Development

Greenfield Python backends built on a clean architectural foundation — service classes, dependency injection, module separation, and a codebase organised for long-term maintainability from the first commit. Environment-based configuration, structured logging, and Sentry error tracking configured before the first deployment.

What it improves

Predictable codebase growth — services can be added, modified, or extracted without re-reading the entire application.

Python 3.12Service LayerDependency InjectionClean Architecturepytest
02

Django / Flask / FastAPI Architecture

Framework selection follows the product architecture — Django for structured, ORM-heavy web applications needing admin and auth foundations; FastAPI for async-first, high-performance API backends; Flask for lightweight, flexible service backends. Framework decisions documented before development begins.

What it improves

Backend performance and maintainability aligned with the product's actual service and data requirements.

Django 5FastAPIFlaskASGI / WSGIPydantic
03

Python API & Service Development

API contracts designed before implementation — endpoint schemas, authentication strategy, versioning, and error taxonomy agreed before code is written. Django REST Framework, FastAPI, or Flask-RESTX for typed responses and documentation. OpenAPI spec auto-generated.

What it improves

APIs that frontend teams and third-party consumers can integrate with confidence and a documented contract.

Django RESTFastAPIOpenAPIJWT / OAuth2Rate Limiting
04

Automation & Background Workflow Logic

Celery task queues with Redis or RabbitMQ brokers for reliable background job processing. Scheduled tasks with Celery Beat, retry and failure logic per task type, dead-letter queue strategy, and Flower for live queue monitoring. Automation built as a first-class backend concern.

What it improves

Background workflows that run reliably and fail predictably — with monitoring rather than surprise outages.

CeleryRedisRabbitMQCelery BeatFlower
05

Data-Connected Backend Systems

Backend logic designed for products where structured data is central — SQLAlchemy with clean query patterns and typed models, Django ORM with optimised relationship fetching, data aggregation and reporting layers, and pipeline integration hooks. Clean separation between data access, transformation, and business logic.

What it improves

Data-heavy products that don't accumulate N+1 queries, slow aggregations, and brittle reporting dependencies.

SQLAlchemyDjango ORMAlembicPandasQuery Optimisation
06

Integrations & System Connectivity

Typed wrapper clients for every external system — payment gateways, CRMs, ERPs, communication platforms, webhooks, and third-party data sources. Built with retry logic, exponential backoff, circuit breaker patterns, and structured error handling. Integrations that degrade gracefully under external failure.

What it improves

External system failures that surface as handled errors, not cascading outages.

httpxTyped ClientsRetry LogicWebhooksCircuit Breaker
07

Deployment, Scaling & Production Configuration

Production-grade Python deployment: Gunicorn or Uvicorn behind Nginx, Docker containerization, environment variable management, health check endpoints, structured JSON logging, Sentry DSN configuration, and deployment runbook delivery. Backends production-ready from the first deploy.

What it improves

Applications that behave the same on the first deployment as they do six months later.

DockerGunicorn / UvicornNginxSentryHealth Checks
08

Python Support, Modernization & Growth

Ongoing Python backend support — security patch management (Python release cycle, dependency vulnerability scanning), performance profiling with cProfile and py-spy, slow query detection, code quality reviews, and incremental modernization of legacy Python codebases alongside new feature delivery.

What it improves

Applications that hold quality across teams and product iterations rather than accumulating debt silently.

Dependency AuditPerformance ProfilingCode ReviewsVersion UpgradesRefactoring

Use Cases

Five Backend Problems Python Solves Best.

These are the architecture patterns that come up in real Python development engagements — across API platforms, automation systems, data-connected products, and legacy modernization.

· SaaS APIs with partner programs

· Mobile + web on shared API

· B2B platforms needing typed contracts

· Products with tiered API access

API-Driven Backend Systems

Multi-Service API Platform with FastAPI and OAuth2

Scenario

A UAE-based B2B SaaS product needed a flexible API backend serving a web frontend, a mobile app, and a partner integration program — each with different authentication requirements, rate limits, and response shapes.

Approach

FastAPI with async endpoint handlers, Pydantic-validated request and response schemas, JWT for first-party auth, OAuth2 for partner integrations, per-tier rate limiting via Redis, and auto-generated OpenAPI documentation served at /docs.

Partner integration onboarding time from 3 weeks to 4 days. API response validation errors in frontend dropped 94%. Partner developer API adopted by 11 integrations in first quarter.

FastAPIPydanticJWT / OAuth2Redis Rate LimitingOpenAPI

· Professional services platforms

· Compliance and regulatory workflows

· Document management systems

· Operations-heavy approval flows

Automation & Workflow Platforms

Document Processing and Approval Automation System

Scenario

A professional services firm needed a backend system automating document intake, classification, multi-reviewer approval routing, deadline tracking, and client notification — replacing a manual email-and-spreadsheet workflow that took 8 days per cycle.

Approach

Celery task queue with Redis broker for async document processing, Django ORM-backed workflow state engine, Celery Beat for deadline monitoring and escalation triggers, per-stage notification system via SendGrid, and a Flower-monitored task queue dashboard.

Average document cycle reduced from 8 days to 2.2 days. Manual routing errors eliminated. Audit trail available for every document action from intake to completion.

CeleryRedisDjango ORMCelery BeatFlower

· Analytics and reporting products

· Multi-source data aggregation

· SaaS dashboards with export needs

· Property, finance, and operations data products

Data-Connected Products

Analytics and Reporting API for SaaS Dashboard

Scenario

A PropTech SaaS needed a reporting backend aggregating data from three property management systems, two payment processors, and a CRM — serving a real-time analytics dashboard with filterable views, export functionality, and scheduled report delivery.

Approach

SQLAlchemy with an async engine for concurrent data fetching, typed integration clients for each data source with retry and circuit breaker logic, a query-building service separating filter logic from ORM queries, and Celery Beat for scheduled report generation with S3 delivery.

Dashboard load time from 4.2 seconds to 380ms after query optimisation. Scheduled reports delivered reliably to 230 clients per week. Integration failure rate below 0.3% with circuit breaker protection.

SQLAlchemy asynchttpx ClientsCeleryS3 DeliveryQuery Builder

· B2B SaaS web applications

· Multi-tenant portals

· Operations management platforms

· Logistics and supply chain products

Python Web Applications

Multi-Tenant B2B Portal with Django and Custom Admin

Scenario

A UAE-based logistics SaaS needed a multi-tenant web backend managing client accounts, shipment tracking, invoicing, and a customised admin portal for operations staff — with per-tenant configuration and role-based access for three distinct user types.

Approach

Django with a tenant-aware middleware layer for per-tenant database schema resolution, Django ORM with optimised prefetch strategies, Django admin extended with custom actions and per-role queryset filtering, Celery for invoice generation, and pytest for all service-layer business logic.

Operations team admin onboarding reduced from 2 weeks to 2 days. Tenant-isolated invoicing processed without cross-tenant data access in 18 months of production.

Django 5Multi-TenancyDjango AdminCelerypytest

· Legacy Python 2/3 modernization

· Monolith service extraction

· Pre-investment technical remediation

· Teams that can't afford a full rewrite

Backend Modernization & Service Expansion

Legacy Python 2 Codebase Migrated to Modern Service Architecture

Scenario

A fintech startup had a Python 2.7 monolith with no tests, no type annotations, direct database queries in view functions, and a single-file task runner executing critical financial operations synchronously in the request cycle.

Approach

Incremental modernization: Python 3 migration first, then type annotation introduction with mypy, then service class extraction replacing inline logic, then Celery task queue replacing synchronous operations, with pytest test coverage added alongside each phase.

Python 3 migration completed without service disruption. Financial operation failures from sync blocking eliminated. New developer onboarding time reduced from 3 weeks to 4 days after service extraction phase.

Python 3mypyService ExtractionCelerypytest

Find Your Fit

What Does Your Backend Actually Need?

Select the closest match to your current backend challenge — the scope on the right tells you exactly what we deliver for that category.

API & Services

Build a well-structured, documented Python API backend

  • 1API contract designed before implementation — endpoint schemas, auth strategy, versioning agreed in writing
  • 2FastAPI or Django REST Framework per the product's performance and structure requirements
  • 3Pydantic or DRF Serializers for typed request validation and consistent response transformation
  • 4JWT or OAuth2 authentication per consumer type — first-party SPA, mobile, or third-party partner
  • 5Per-tier rate limiting using Redis — different quotas for different subscription levels
  • 6Consistent error response structure with typed error codes across every endpoint
  • 7OpenAPI documentation auto-generated and served — always in sync with the implementation

Why Python Projects Underperform

Six Patterns That Turn Flexible Python Into a Liability.

Python problems are almost never about the language. They're about how the flexibility was used. These six patterns are predictable, preventable, and fixable — but compound quickly when left alone.

"Python was chosen for its flexibility — but the codebase has no structure and the services grew without clear boundaries"

Root Cause

Flexibility without architecture is just disorder with good syntax. Logic spread across view functions, utility modules, and helper files. No service layer, no dependency injection, no clear module ownership. Python's flexibility enabled rapid growth and equally rapid complexity accumulation.

Business Cost

Every new feature requires reading the entire codebase to understand side effects. Developer onboarding takes weeks because there are no predictable patterns to follow.

How We Fix It

Service class extraction, module boundary definition, dependency injection via a simple container, and a documented architecture decision record establishing what goes where and why.

"The API 'works' but every frontend update requires backend changes to handle unexpected response shapes"

Root Cause

No API contract before implementation. Direct model serialisation instead of typed response schemas. Inconsistent error formats across endpoints. No versioning strategy. The API grew organically alongside the frontend rather than being designed as a stable contract.

Business Cost

Frontend teams write defensive parsing code for every field. Third-party integrations break on minor backend changes. API updates require coordinating across three teams.

How We Fix It

Pydantic or DRF Serializer schemas for every endpoint, consistent error envelope, versioning strategy, OpenAPI documentation auto-generated, and a contract-first process for future endpoint additions.

"Background tasks run synchronously in the request cycle — the API times out under normal load"

Root Cause

Email sending, PDF generation, report building, third-party API calls, and data processing tasks execute inside Django or Flask request handlers. The backend architecture never separated async work from synchronous request handling.

Business Cost

API response times measured in seconds, not milliseconds. Timeout errors under moderate load. Operations that should complete instantly take 8–15 seconds. Users abandon.

How We Fix It

Celery task queue introduction, synchronous-to-async task migration by operation type, Redis broker configuration, retry logic per task, and a Flower monitoring dashboard for ongoing queue health visibility.

"The framework was chosen by habit, not by product fit — the backend carries framework overhead it doesn't need"

Root Cause

Django chosen for a simple API service that doesn't use its ORM, admin, or template engine. Or FastAPI chosen for a content-heavy application that needs Django's migrations and admin. Framework selection driven by familiarity, not architecture.

Business Cost

Unnecessary dependency weight, slower startup times, framework features fighting against the product's actual requirements, and developers working around the framework rather than with it.

How We Fix It

Framework assessment against the product's actual service requirements — then migration path or clean-room rebuild on the right framework, documented as an architecture decision before implementation.

"Data access logic, business logic, and API logic are all mixed together — the database is queried from everywhere"

Root Cause

SQLAlchemy sessions or Django ORM calls scattered across views, services, serializers, and background tasks. No data access layer, no query builder separation, no relationship prefetch strategy. N+1 queries accumulate undetected until production load exposes them.

Business Cost

Database query count grows linearly with list endpoint size. Production performance degrades as data volume increases. Refactoring any part of the system requires touching code in six unrelated files.

How We Fix It

Data access layer extraction, ORM query centralisation, prefetch strategy for relationship-heavy endpoints, query profiling with Django Debug Toolbar or SQLAlchemy logging, and N+1 elimination before production.

"The Python backend works at 100 users — it fails silently at 1,000"

Root Cause

No connection pooling, no async concurrency strategy, synchronous tasks blocking worker processes, no rate limiting on external API calls, no health check endpoints, and no structured logging. The backend was designed for development load, not production traffic.

Business Cost

Cascading failures under real load. External API rate limit breaches triggering blocking retries. Worker processes exhausted by slow database queries. Errors silently swallowed with no Sentry visibility.

How We Fix It

Connection pool configuration, async endpoint migration for I/O-bound operations, external API client retry logic with circuit breakers, structured logging, Sentry DSN setup, and load testing before production deployment.

How We Build

The Avana Hub Python Development Framework.

Five phases applied to every Python engagement. Each phase has a concrete output — not an internal milestone. Every architectural decision is documented, not just implemented.

Define

Product Logic Before Framework Choice

Connect

APIs and Integrations Built to Contract

Automate

Workflow and Processing Logic Off the Request Cycle

Scale

Deployment and Performance Built for Production

Evolve

Backends Built to Grow Across Product Generations

Every Python engagement starts with a product architecture review — service requirements, API design, automation needs, data sources, integration dependencies, and team context — before any framework or tooling decision is made.

→ Product architecture brief. Service map. Framework recommendation with rationale. Agreed technology stack before a line of code.

Architecture Decision Record · Service Map · Stack Justification

API contracts designed before endpoint implementation — schemas, authentication strategy, error taxonomy, and versioning agreed before development begins. Integration clients typed and tested. External systems connected with retry logic and circuit breakers.

→ API contract specification. OpenAPI schema. Typed integration client map. Authentication model documentation.

FastAPI · Django REST · Pydantic · httpx · OAuth2

Background task architecture designed to match the product's automation requirements — task queue setup, retry strategy, scheduling, queue monitoring, and failure handling. Automation built as a first-class backend concern from day one.

→ Task queue configuration. Automation flow diagram. Celery Beat schedule. Flower monitoring setup.

Celery · Redis · RabbitMQ · Celery Beat · Flower

Production-grade deployment configuration: Gunicorn or Uvicorn worker setup, Docker containerization, connection pooling, structured logging, Sentry error tracking, and health checks. Load testing before go-live, not after the first outage.

→ Deployment configuration. Docker setup. Load test results. Sentry DSN and alert routing. Runbook.

Docker · Gunicorn / Uvicorn · Nginx · Sentry · pytest

Test coverage, structured logging, dependency audit, and architectural documentation that makes the backend maintainable beyond the initial team. Version upgrade paths planned. Service extraction strategy for future evolution documented at delivery.

→ pytest suite. Dependency audit report. Architecture evolution guide. Python upgrade path documentation.

pytest · mypy · Dependency Audit · Type Annotations · Docs

Delivery Process

Five Steps. Clear Architecture. No Framework Surprises.

Framework and architecture are agreed before development starts. Every step produces a written output you can review. No verbal scope changes, no undocumented decisions, no "we'll figure it out during build."

Step 01

Review Product Logic, Service Needs & Backend Goals

Days 1–4

Deep-dive into the product scope, service requirements, automation complexity, API consumer types, data sources, and integration dependencies. Output: a written requirements brief and framework recommendation — agreed and signed off before a line of implementation code.

Deliverables

Product and service requirements brief
API consumer map and auth strategy
Automation and workflow complexity assessment
Integration dependency map
Framework recommendation (Django / Flask / FastAPI) with rationale
Step 02

Choose the Right Python Path, Framework & Architecture

Days 4–8

Architecture decision record: service layer design, module separation strategy, ORM and data access patterns, API contract specification, task queue architecture, and deployment approach. All architectural decisions written down before implementation begins.

Deliverables

Architecture Decision Record (ADR)
Service and module map
API contract specification with schema definitions
Task queue and automation architecture plan
Database schema and ORM relationship design
Project scaffolding with correct structure from day one
Step 03

Build APIs, Services, Workflows & Integrations

Days 8–24

Iterative development in service slices — each slice delivered with service logic, data access layer, API endpoints, integration clients, and pytest test coverage. Weekly review sessions to validate progress against requirements and adjust scope.

Deliverables

Service-layered Python codebase
API endpoints with typed schemas and OpenAPI documentation
Authentication system per agreed consumer model
Celery task queue and automation workflows
Typed integration clients for external systems
pytest coverage per delivered service slice
Step 04

Validate Scalability, Deployment Readiness & Code Quality

Days 22–30

End-to-end functional testing, performance profiling with cProfile or py-spy, N+1 query detection and ORM optimisation, connection pool configuration, load testing under target traffic, security review, and dependency vulnerability audit.

Deliverables

Functional test results across all service flows
Performance profiling report
N+1 detection and ORM query optimisation
Load test results at target traffic levels
Security review and dependency vulnerability audit
Step 05

Deploy & Support the Backend as the System Evolves

Days 28–32+

Production deployment, Celery worker and queue configuration, Sentry error tracking with alert routing, structured logging, environment documentation, runbook delivery, and 30-day post-launch support window included in every engagement.

Deliverables

Production deployment and smoke test
Celery + broker production configuration
Sentry error tracking with alert routing
Environment documentation and deployment runbook
30-day post-launch support window
Handoff session recorded for async onboarding

What Gets Measured

Delivery Standards, Not Capability Claims.

Outcomes are defined before development starts. These numbers come from real Python development and modernisation engagements.

94%

drop in API response validation errors

Pydantic-typed FastAPI rebuild replacing inconsistent Django REST serializers without schemas

380ms

dashboard load time

Down from 4.2s — SQLAlchemy async engine with N+1 elimination and prefetch strategy

2.2 days

average document processing cycle

Down from 8 days — Celery-backed automation workflow replacing manual routing

0

cross-tenant data access incidents

18 months of production on multi-tenant Django platform with row-level tenant isolation

11

partner integrations onboarded in Q1

FastAPI OAuth2 partner API with OpenAPI documentation — integration time from 3 weeks to 4 days

Day 1

Sentry + structured logging configured

Observability at scaffolding — never retrofit in a production incident

Non-Negotiable Delivery Standards

Architecture Decision Record agreed before framework selection — no habit-driven stack choices

API contract specification before endpoint implementation — typed schemas agreed in writing

pytest coverage on all service-layer business logic — CI runs on every pull request

Celery task queues for all background work — no synchronous side-effects in request cycles

Typed integration clients with retry logic and circuit breakers — every external system

Sentry error tracking and structured JSON logging configured at scaffolding — not after the first outage

What You Receive

Everything Needed to Run, Operate, and Grow.

A Python engagement doesn't end at a working application. Every engagement delivers six output categories — codebase, API layer, automation system, integrations, documentation, and post-launch support — everything needed to own the backend.

Python Backend Codebase

  • Service-layered codebase: routes → views → service classes → data access layer
  • Clean module separation with no circular dependencies between service boundaries
  • ORM models (Django or SQLAlchemy) with typed relationships, constraints, and clean query patterns
  • Database migrations version-controlled and deterministic — no schema drift between environments
  • Environment-based configuration with pydantic-settings or python-decouple — no secrets in code
  • All background work through Celery task queue — no synchronous side-effects in request cycles

API & Service Layer

  • API contract specification agreed before implementation — schemas, auth, versioning, error taxonomy
  • Pydantic or DRF Serializer schemas for every endpoint — typed requests and responses
  • Authentication system per agreed consumer model — JWT, OAuth2, session, or API key
  • Consistent error response structure with typed error codes across every endpoint
  • OpenAPI documentation auto-generated and served at /docs — always in sync with implementation
  • Postman collection and integration test suite for API validation

Automation & Workflow System

  • Celery task queue with Redis or RabbitMQ broker configured for the product's job types
  • Retry logic with exponential backoff and max retry limit per task type
  • Celery Beat for scheduled automation — cron and interval-based task scheduling
  • Dead-letter queue strategy for failed tasks — failure visibility before cascade
  • Flower monitoring dashboard for live queue health, task status, and worker metrics
  • Task idempotency design — automation that retries safely without duplicate effects

Data Access & Integrations

Where data or integrations are in scope

  • Data access layer separating ORM queries from business logic
  • N+1 query elimination with prefetch and select_related strategies
  • Typed httpx clients for every external system — retry logic, timeout, circuit breaker
  • Webhook receiver endpoints with signature verification and idempotent event processing
  • Integration error handling — external failures surface as structured errors, not silent failures
  • Integration test suite with mock servers — tests that don't require live credentials

Documentation

  • Architecture overview: service map, module responsibilities, framework rationale, key decisions
  • API documentation generated with OpenAPI — endpoint contracts, schemas, auth model
  • Task queue documentation: task inventory, retry strategy, monitoring guide
  • Integration map: every external system, client configuration, failure behaviour
  • Deployment runbook: environment setup, deployment procedure, Celery worker management
  • Python version upgrade path with dependency compatibility notes

Post-Launch Support

  • 30-day post-launch support window included in all engagements
  • Sentry error tracking configured with DSN and alert routing per error category
  • Production incident response within 4 business hours
  • One performance review at 30 days — profiling data, slow query analysis, queue health
  • Handoff session recorded for team onboarding
  • Python version and dependency security advisory for 90 days post-launch

How to Engage

Five Engagement Models. One Right Fit.

Every engagement starts with a discovery call — we map the model to your backend's actual requirements, not a default package.

Python API & Service Development

15–25 days

Structured Python API backend from contract design to production deployment — FastAPI or Django REST Framework, Pydantic-typed schemas, authentication system, rate limiting, versioning strategy, OpenAPI documentation, pytest suite, and 30-day post-launch support.

Best For

  • SaaS products needing a typed, documented API backend
  • Backends serving both a web SPA and mobile application
  • Products building a partner or third-party developer API

Includes

  • API contract specification before implementation
  • FastAPI or Django REST Framework per product requirements
  • Pydantic or DRF Serializers for typed request/response contracts
  • JWT or OAuth2 authentication per consumer type
  • Rate limiting and quota management per tier
  • OpenAPI documentation auto-generated from implementation
  • pytest integration test suite and Postman collection

Pricing Plans

Python Development Pricing

Every engagement starts with a discovery call to confirm Python is the right fit, map the service architecture, and define the scope — before any commitment. Pricing below reflects the three most common engagement shapes.

Python Backend Assessment

A structured review of your existing Python backend or a scoping session for a new build: architecture quality, framework fit, service boundaries, performance posture, security review, and a prioritised action plan.

AED 2,650/mo
  • Codebase review or new project scoping session
  • Architecture and service boundary assessment
  • Framework fit review (Django / Flask / FastAPI)
  • Security posture review (injection, input validation, auth)
  • Python version and dependency audit
  • Performance and N+1 query risk identification
  • Prioritised action plan with quick wins and structural recommendations
  • Delivered in 5–7 business days
Most Popular

Python Backend Build

Full Python backend from architecture design to production deployment — service-layered codebase, API layer, automation and task queue system, integrations, pytest test suite, deployment configuration, and 30-day post-launch support.

From AED 13,200/mo
  • Everything in Python Backend Assessment
  • Architecture Decision Record (agreed pre-build)
  • Service-layered Python codebase (Django / Flask / FastAPI)
  • API layer with typed schemas and OpenAPI documentation
  • Authentication system per agreed consumer model
  • Celery task queue and automation workflows
  • pytest test suite with CI pipeline
  • Production deployment and environment documentation
  • 30-day post-launch support included

Ongoing Python Support

Monthly Python backend support — security patch management, dependency auditing, performance profiling, N+1 detection, and on-call advisory without an in-house Python developer.

Custom Pricing

Tailored to your needs

  • Everything in Python Backend Build
  • Monthly security patch review and dependency update cycle
  • pip-audit / safety vulnerability scanning
  • Performance profiling with cProfile or py-spy
  • N+1 detection and ORM query optimisation
  • Sentry error triage and monthly health report
  • On-call advisory: 4-hour business-hours response SLA
  • Quarterly Python version and upgrade path review
No setup fees Cancel anytime Free consultation

Common Questions

Python Development FAQ.

Other Backend Paths

Python Isn't the Only Backend Path.

If the product fit points elsewhere, these are the other backend stacks we build on — each with their own dedicated engagement model and delivery standards.

Related Services

Connected Development Services

Start a Python Development Engagement

Build Python Backends That Are More Flexible, Better Connected, and Built to Grow.

Every engagement starts with a discovery call — we map your backend requirements to the right Python path, agree the architecture, and define the scope before any commitment.

Architecture and framework agreed before any implementation begins
All background work through Celery — no sync side-effects in requests
30-day post-launch support included in every engagement