Python Development Services
Backend, APIs & AI Engineering
Backend systems, APIs, and AI engineering built by the team behind procedure.tech, Treebo, Last9, and KredX.
Trusted by engineering teams at
Python Development Services
Backend systems, AI/ML, and the data infrastructure your product depends on.
Backend & API Development
REST and GraphQL APIs built with Django, FastAPI, or Flask. We pick the framework based on your workload: Django for full-stack apps with admin panels and auth, FastAPI for high-concurrency APIs and AI-serving endpoints, Flask for lightweight services and serverless functions. Not a one-size-fits-all decision.
AI & ML Engineering
Production ML pipelines, LLM integrations, and AI-powered features. We build with PyTorch, scikit-learn, and LangChain, then deploy on AWS SageMaker or self-hosted infrastructure. Model training is one thing. Getting it into production with monitoring, versioning, and rollback is what most teams struggle with.
Data Engineering & Pipelines
ETL pipelines, data warehousing, and real-time processing with Apache Airflow, Celery, and Pandas. We build the data infrastructure that feeds your dashboards, ML models, and reporting. Clean data in, usable insights out. No 'data lake' that nobody can query.
Automation & Scripting
Process automation, workflow orchestration, and system integration. Python scripts that replace manual work your team does every week. We have automated reporting pipelines, compliance checks, and multi-system data syncs that saved teams 20+ hours per week.
Legacy Migration to Python
Migrating PHP, Ruby, or Java backends to modern Python. We do this incrementally - running old and new systems in parallel, migrating service by service. No big-bang rewrites. Typical migration: 3-6 months depending on system complexity.
Full-Stack Teams (Python + Frontend)
Python backend paired with React, Next.js, or Angular frontend. One team, shared TypeScript types, unified deployment. Most Python projects need a frontend. We staff both sides instead of making you coordinate two vendors.
Is Python Right for Your Backend?
The right tool for the job. Not the trendy one.
AI/ML features are core to your product
Python owns the AI ecosystem. PyTorch, TensorFlow, scikit-learn, LangChain - the models, libraries, and talent pool all live here. No other language comes close for ML.
Data-heavy applications (analytics, ETL, reporting)
Pandas, NumPy, and Airflow make data manipulation fast. Python handles everything from quick scripts to petabyte-scale processing pipelines.
APIs serving high concurrency (with FastAPI)
FastAPI handles 20,000+ requests/second on Uvicorn. Native async, automatic OpenAPI docs, Pydantic validation. Built for the workloads Django wasn’t designed for.
Full-stack web apps with complex business logic
Django gives you ORM, auth, admin panel, and security middleware out of the box. Instagram, Spotify, and Dropbox run on it. Proven at scale.
Need raw API throughput without the Python ecosystem? Node.js with NestJS or Fastify handles I/O-bound workloads with a lighter runtime. Building a frontend-heavy application where the backend is thin? Next.js API routes might be all you need. CPU-intensive work like video encoding or real-time game servers? Go or Rust will outperform Python. Not sure? That's what our architecture consultation is for.
Django vs FastAPI vs Flask: Which One?
We use all three. Here's how we decide.
Django
Best for
Full-stack web apps, admin-heavy platforms, SaaS
Why
ORM, auth, admin panel, security middleware built in. Batteries-included means faster development for complex apps. 20 years of battle-tested stability.
We use it when
Your app needs user management, complex data models, and an admin interface. Most enterprise Python projects start here.
FastAPI
Best for
High-performance APIs, AI/ML serving, microservices
Why
Native async, automatic OpenAPI docs, Pydantic validation, 4-5x faster than Django for API workloads. The default choice for AI backends in 2026.
We use it when
You're building APIs that serve ML models, need high concurrency, or want auto-generated documentation.
Flask
Best for
Lightweight services, serverless functions, prototypes
Why
Minimal footprint, fast startup, zero opinions about architecture. Perfect when you need just enough framework to get out of the way.
We use it when
The service is small and focused, you're deploying to Lambda/serverless, or you need maximum control over the stack.
Most projects aren't purely one framework. We've built systems where Django handles the main app, FastAPI serves the ML inference endpoints, and Flask runs lightweight webhook handlers. The framework follows the workload.
How We Deliver Python Projects
Working software every sprint, not just progress updates.
Architecture & Discovery (1-2 weeks)
We map your requirements, data flows, and integration points. You get a technical proposal covering framework selection (Django vs FastAPI vs Flask), database design, API architecture, hosting recommendation, and CI/CD setup. No code until the architecture makes sense.
API Design & Data Modeling (1-2 weeks)
API contracts documented in OpenAPI spec before a single endpoint is built. Database schema designed, relationships mapped, migration strategy defined. If there is an ML component, we define the model serving architecture here.
Development & Iteration (6-16 weeks)
Sprint-based delivery with working endpoints shipped every two weeks. Automated tests written alongside features. Your team gets staging access from week one. For AI/ML projects, model training runs in parallel with API development.
Load Testing & Hardening (1-2 weeks)
Production traffic simulation, bottleneck identification, query optimization, caching layers, rate limiting. For ML endpoints: latency benchmarking, model warm-up strategies, and fallback handling. Nothing ships until it handles your expected load.
Handoff & Support
Complete documentation, architecture decision records, and runbooks. For ML projects: model retraining guides and monitoring dashboards. Your team owns the system. Optional support retainer for ongoing work, but no lock-in.
Our Python Stack
Every tool earns its place. Here’s what we ship with and why.
| Layer | Tools | Why |
|---|---|---|
| Language | Python 3.12+ (strict typing) | Performance improvements, pattern matching, type hints as contracts |
| Web Framework | Django 5.x, FastAPI, Flask | Django for apps, FastAPI for APIs, Flask for lightweight services |
| ORM / Query | Django ORM, SQLAlchemy, Prisma | Django ORM for Django apps, SQLAlchemy for FastAPI/Flask, Prisma for type safety |
| Database | PostgreSQL, MongoDB, Redis | Postgres for relational, MongoDB for documents, Redis for cache and queues |
| AI/ML | PyTorch, scikit-learn, LangChain, Hugging Face | PyTorch for custom models, scikit-learn for classical ML, LangChain for LLM apps |
| Data | Pandas, NumPy, Apache Airflow, Celery | Data manipulation, orchestration, and async task processing |
| API | REST (DRF, FastAPI), GraphQL (Strawberry) | DRF for Django REST APIs, FastAPI for async, Strawberry for type-safe GraphQL |
| Auth | Django auth, OAuth2, JWT | Built-in auth for Django, token-based for APIs |
| Testing | pytest, Hypothesis, Locust | pytest for unit/integration, Hypothesis for property-based, Locust for load testing |
| Hosting | AWS (ECS, Lambda, SageMaker), GCP, Vercel | Containers for control, serverless for cost efficiency, SageMaker for ML deployment |
| CI/CD | GitHub Actions, Docker | Automated tests, container builds, and deployments on every merge |
| Monitoring | Datadog, Sentry, Prometheus + Grafana | APM, error tracking, and custom dashboards |
We pick the framework based on your constraints. Django if your team needs structure and a built-in admin. FastAPI if throughput and async matter. Flask if simplicity and a small footprint are the priority. There is no single right answer.
Discuss Your Python Project
Whether it’s APIs, AI engineering, or a full backend migration, we’re happy to talk through your situation.
Schedule a CallNo sales pitch. Just an honest conversation.
What you get
- Engineers with 3+ years building production Python systems in Django, FastAPI, or Flask
- Full-stack capability: Python backend + React, Next.js, or Angular frontend
- AI/ML experience available: PyTorch, scikit-learn, LangChain, data pipelines
- Same timezone overlap (India-based team, flexible to US working hours)
- No recruiting overhead - engineers are vetted, onboarded, and managed
Hire Python Developers
Experienced Python engineers who plug into your team and ship from week one.
Dedicated Developer
Engineers with 3+ years building production Python systems in Django, FastAPI, or Flask. Full backend capability spanning API design, data engineering, and cloud infrastructure. AI/ML experience available.
Ongoing backend or ML development, 3-month minimum engagement
Python Pod (2-4 Engineers + Lead)
A self-contained team that owns full API or service delivery. Architecture, implementation, testing, and deployment covered. Mixed team of leads and mid-level engineers staffed based on project needs.
Full API or service ownership, 6-month minimum engagement
Project-Based Delivery
Fixed-scope engagement with clear deliverables, timeline, and budget. Defined API contracts, agreed milestones, and transparent pricing.
Defined scope with clear deliverables, scope-dependent
Starting at $3,500/month per developer for full-time dedicated engagement.
Talk to Us About Your TeamReady to Discuss Your
Python Development Services Project?
Tell us about your Python project. Whether it’s backend APIs, AI engineering, or a full system migration, we’ll discuss architecture and give honest next steps.
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Python Development FAQ
It depends on scope. An API backend for a web or mobile app with 10-20 endpoints typically runs $15,000 to $40,000. A data platform with ETL pipelines and dashboards sits in the $40,000 to $120,000 range. AI/ML systems with custom model training, serving infrastructure, and monitoring can run $80,000 to $250,000 or more. Our architecture consultation is free and scopes your specific project.