We build SaaS products on Django — the Python framework trusted by Instagram, Disqus, and Mozilla — with battle-tested multi-tenancy via django-tenants, asynchronous task processing with Celery, RESTful APIs through Django REST Framework, and native ML pipeline integration that lets your SaaS leverage machine learning without a separate inference service. Django's batteries-included philosophy means authentication, admin, ORM, and migrations work out of the box, so we spend time building your product, not infrastructure.
INR 2000
Per Hour
30+
SaaS Products Built
4.9/5
Client Rating
<4 Weeks
MVP Delivery
Integrates with the tools you already use
Leveraging Django's mature ecosystem and Python's ML libraries to build SaaS products that are robust, scalable, and intelligent
We implement true schema-based multi-tenancy using django-tenants, where each tenant gets an isolated PostgreSQL schema with its own tables, indexes, and data. Shared tables for authentication and billing live in the public schema while tenant-specific data is fully isolated. Tenant resolution happens via subdomain or custom domain, and the middleware automatically sets the schema context for every request — making tenant isolation transparent to your application code.
Background processing runs on Celery with Redis or RabbitMQ as the message broker. We configure task queues for email delivery, report generation, data imports, scheduled billing cycles, and ML inference jobs. Tasks support priority routing, rate limiting, retry with exponential backoff, and result backends for tracking completion. Celery Beat handles periodic tasks like usage aggregation, subscription renewals, and data cleanup on configurable schedules.
Your SaaS API is built on Django REST Framework with serializers for input validation and response shaping, viewsets for CRUD operations, custom permissions for tenant-scoped access control, and pagination for list endpoints. We generate interactive API documentation with drf-spectacular (OpenAPI 3.0), implement token and JWT authentication, configure throttling policies per endpoint, and build versioned API endpoints that let you evolve the API without breaking existing integrations.
Python's ML ecosystem integrates natively with your Django SaaS. We build inference pipelines using scikit-learn, TensorFlow, or PyTorch models that run as Celery tasks or dedicated worker processes. Models are versioned, loaded at startup, and served through your API or triggered by domain events. Use cases include recommendation engines, anomaly detection, NLP text classification, sentiment analysis, and predictive analytics — all running inside your SaaS backend without a separate ML platform.
We customize Django Admin into a powerful internal operations tool for your SaaS team. Custom admin views for tenant management, user impersonation, subscription overrides, feature flag configuration, and support ticket resolution. Admin actions for bulk operations, inline editing for related records, and custom filters for finding specific tenants or users. The admin panel ships with audit logging so every internal action is tracked and attributable.
Real-time features run on Django Channels with ASGI and Redis channel layer for horizontal scaling. We implement WebSocket consumers for live notifications, real-time dashboard updates, collaborative editing, and chat functionality. Channels integrates with Django's authentication and permission system, so WebSocket connections are authenticated and tenant-scoped. The same codebase handles both HTTP requests and WebSocket events without running a separate real-time service.
Django ORM
Task Processing
Python Ecosystem
Mature Ecosystem
A structured 5-step process to deliver production-ready Django SaaS products
We design the Django project structure with app-based modularity — each domain area (tenants, billing, notifications, your core product) lives in its own Django app with clear model relationships, signal handlers, and service layers. We plan the PostgreSQL schema for django-tenants, define shared vs tenant-specific models, and design the URL routing for subdomain-based tenant resolution. The architecture supports both synchronous views and ASGI-based async endpoints.
We configure django-tenants with PostgreSQL schema isolation, subdomain routing, and tenant-aware middleware. Authentication integrates django-allauth for social logins, django-rest-framework-simplejwt for API tokens, and optional SAML/SSO for enterprise tenants. The permission layer uses Django's built-in permission framework extended with tenant-scoped groups, object-level permissions with django-guardian, and custom decorators that enforce access at the view level.
We build the Django REST Framework API with viewsets, serializers, and permission classes for every endpoint. Celery workers are configured with dedicated queues for different workload types — a high-priority queue for user-facing tasks like email and notifications, a standard queue for report generation and data processing, and a scheduled queue for periodic billing and cleanup tasks. Each task is idempotent, retryable, and monitored through Flower or a custom dashboard.
If your SaaS includes ML capabilities, we integrate trained models as Celery tasks that process data asynchronously and return predictions through webhooks or real-time WebSocket events. Django Channels handles live features — notifications pushed to connected users, collaborative document updates, and dashboard metrics that refresh without polling. The channel layer scales horizontally via Redis, and all WebSocket connections are authenticated and tenant-scoped.
We write tests with pytest-django covering models, views, serializers, Celery tasks, and WebSocket consumers. Database query performance is optimized with select_related, prefetch_related, database indexes, and Django Debug Toolbar profiling. The application deploys to Docker containers with Gunicorn for WSGI, Daphne or Uvicorn for ASGI, and Celery workers as separate containers. Monitoring covers Django request metrics, Celery queue depths, and database query performance.
Transparent pricing for Django SaaS products at every stage
"Edesy built our multi-tenant analytics SaaS on Django with django-tenants handling 400+ customer schemas in a single PostgreSQL database. Each tenant has complete data isolation, their own admin configuration, and custom branding. The schema-based approach means we can run tenant-specific database migrations without affecting other customers. It has been incredibly reliable — zero data leakage incidents across 18 months of production."
C
CTO
Technology at Analytics SaaS Platform
"Our SaaS needed ML-powered document classification — customers upload contracts and our system extracts key terms, categorizes clauses, and flags risks. Edesy integrated a fine-tuned NLP model directly into our Django backend as Celery tasks. Documents are processed in under 10 seconds, results are pushed via Django Channels in real-time, and the whole pipeline runs inside our existing infrastructure without a separate ML platform."
F
Founder
Founder at Legal Tech SaaS
"We migrated from a messy Flask codebase to a properly structured Django application. Edesy set up django-tenants for multi-tenancy, Celery for our 30+ background job types, and Django REST Framework with full OpenAPI documentation. Our API response times dropped by 40% thanks to query optimization, and the new codebase is so well-structured that onboarding new developers now takes days instead of weeks."
VO
VP of Engineering
Engineering at HR Tech SaaS
Resources to help you evaluate and implement
Get a free consultation and detailed estimate for your Django SaaS platform. We will help you design the right multi-tenancy architecture, Celery task infrastructure, and ML integration strategy to build a product that is robust, scalable, and intelligent from day one.