Python development cost depends heavily on what you're building: a Django web app, a FastAPI backend, or a production ML system are three different budgets. This guide breaks down realistic 2026 ranges by project type, what moves the number, and how to get a fixed quote without surprise overruns.
Quick estimate
Use our free Python Project Cost Calculator for a directional budget, timeline, and stack recommendation based on your module scope, framework, and whether AI/ML is involved — then book a call for a fixed milestone quote.
Python development cost ranges by project type
- Codebase audit / AI feasibility PoC: $2,500 – $12,000 (1–3 weeks)
- Scoped MVP (Django or FastAPI): $15,000 – $35,000 (6–10 weeks)
- Django SaaS platform (billing, admin, multi-tenant): $45,000 – $120,000+ (12–24 weeks)
- FastAPI / DRF API backend: $20,000 – $55,000 (8–14 weeks)
- Production AI/ML build (LLM/RAG or prediction): $20,000 – $60,000 (6–12 weeks)
- Dedicated Python engineer (monthly): $5,000 – $15,000 per senior FTE
What drives Python project cost the most
1. Whether AI/ML is in scope
A CRUD Django app is predictable. Adding a model — LLM/RAG, prediction, or vision — introduces data readiness, evaluation, and monitoring work. Hosted LLMs keep this affordable; custom model training raises it significantly. Validate with a PoC first.
2. Django vs FastAPI fit
Django saves time when you need admin, ORM, and auth out of the box. FastAPI is leaner for API-first and ML-serving work. Forcing the wrong tool for a workload adds avoidable engineering time.
3. Integrations and data pipelines
Each major integration or pipeline — CRM, ETL, third-party API, streaming data — typically adds $2,500–$8,000 depending on documentation and edge cases.
4. Inherited codebase state
Greenfield Python is predictable. Messy inherited code or stalled notebooks need audit time before estimates hold. Rescue projects should budget discovery before feature sprints.
How GreeLogix prices Python work
- Discovery call — scope, data readiness, and engagement model.
- Written milestone plan — modules, ML approach, timeline, exclusions.
- Fixed quote per phase — no open-ended hourly on defined scope.
- QA and (for ML) evaluation included on critical paths.