Python & Django
Django is our default backend, and the reason is unglamorous: it makes the boring things correct by default. Transactions, migrations, permissions, admin and security are solved, so our effort goes into your business rules.
ACID
Transactional integrity by default
DRF
Production REST API layer
Celery
Background & scheduled work
Batteries
Auth, admin, migrations included
Framework arguments usually focus on request throughput, which is almost never the constraint that decides whether a business system succeeds. The things that decide it are whether a multi-step operation can partially fail and leave inconsistent data, whether a schema change can be applied safely to a live database, whether permissions are enforced consistently, and whether a year from now anyone can still explain what the code does.
Django is opinionated about all of those. Database transactions, migrations, an authentication and permission system, protection against the common web vulnerabilities, and a usable admin interface all come with it. That means our time goes into the parts that are actually specific to your business — the costing rules, the amortisation schedule, the reconciliation logic — rather than into rebuilding infrastructure that has been solved for fifteen years.
It is the backend under most of our serious work: manufacturing ERP systems with heavy reconciliation requirements, financial platforms where the maths must be provable, and the API layers behind our Angular and Next.js front-ends.
The Django Problems We Are Called In To Fix
The framework is rarely the issue. How it was used, usually is.
N+1 queries everywhere
A list view that looks fine on twenty records issues one query per row plus one per relation on two thousand. The ORM makes this easy to write and invisible until the data grows.
Business logic in views
Rules written directly in view functions, so the same rule is duplicated across the API, the admin and a management command — and the three copies disagree. Nothing is testable without an HTTP request.
No transaction boundaries
A multi-step operation writing several records without an atomic block. A failure halfway through leaves the database in a state that nothing in the codebase knows how to interpret.
Synchronous calls to slow third parties
Payment gateways, tax APIs and email providers called inline during a request, so the user waits and an unavailable provider becomes a failed transaction instead of a delayed one.
Django Capabilities
The system types where Django is genuinely the right answer.
REST APIs (DRF)
Versioned APIs with serialisers, permission classes, filtering, pagination and throttling — consistent error shapes so every client handles failure the same way.
ERP & Operational Backends
Transaction-heavy systems where documents reference documents and quantities must reconcile across stages — the workload Django's transactional model is well suited to.
Auth, Roles & Permissions
Authentication, JWT or session handling, granular object-level permissions, and maker-checker approval flows on operations that need a second pair of eyes.
Celery Background Processing
Async task execution over RabbitMQ with scheduled jobs, priority routing and retry policy, so heavy work never sits in a user's request.
Document & Report Generation
Invoices, challans, packing lists, statements and exports generated server-side from authoritative data — queued rather than blocking, and reproducible.
Complex Data Models & Migrations
Schema design for genuinely complex domains, with migrations written to be backward-compatible so deployments do not require downtime.
Third-Party Integrations
Payment gateways, e-way bill and GST APIs, SMS, email and WhatsApp providers, integrated through a queue with retry and dead-letter handling.
Django Admin as an Internal Tool
A customised admin as a genuine back-office interface for operations staff, saving the cost of building internal CRUD screens that only a handful of people use.
Reporting & Analytics Layers
Aggregation and reporting endpoints computed server-side, with heavy queries isolated from transactional load so a report never slows down operations.
What Makes a Django Codebase Last
Business logic belongs in services, not views
The most consequential structural decision in a Django project is where the business rules live. Putting them in views is fast at first and expensive forever after: the same rule ends up duplicated in an API view, the admin and a management command, the copies drift apart, and nothing can be tested without constructing an HTTP request.
We keep views thin — parse input, call a service, shape the response — and put the rules in a service layer that knows nothing about HTTP. A costing calculation, an EMI schedule or a stock reconciliation becomes a plain function that can be tested directly, called from anywhere, and read by someone new without tracing through request handling. It costs a little more structure up front and it is the single clearest predictor of whether a codebase is still pleasant to work in after two years.
- Thin views; business rules in a service layer
- Rules defined once and reused by API, admin and commands
- Logic testable without HTTP, which means it actually gets tested
- Transaction boundaries explicit at the service level
The ORM is excellent and will hurt you if you ignore it
Django's ORM makes it trivially easy to write a view that issues thousands of queries, because the code that does so looks completely reasonable. A list of orders, each showing its buyer and item count, becomes one query for the orders plus two per order. On a test dataset it is instant. In production it times out, and the cause is not visible from reading the code.
So we treat query counts as something to be measured rather than assumed. Related data is loaded explicitly with select and prefetch, aggregation is pushed into the database rather than done in Python, and the endpoints that matter have their query counts asserted in tests so a regression is caught in review rather than in production. Where a query genuinely needs to be hand-written for performance, we write it — the ORM is a default, not a religion.
- Related data loaded explicitly — no accidental N+1
- Aggregation pushed into the database, not looped in Python
- Query counts asserted in tests on the endpoints that matter
- Raw SQL used deliberately where it is genuinely the right tool
Correctness under partial failure
Business operations are rarely a single write. Confirming a dispatch might create a delivery challan, decrement stock, update an order status, record a movement and trigger an e-way bill. If that sequence fails halfway, the database is left in a state no part of the system knows how to interpret — and someone will be reconciling it by hand months later.
We wrap those operations in explicit atomic blocks so they either complete entirely or not at all, and we deliberately keep external calls outside the transaction. Nothing that depends on a third party's availability belongs inside a database transaction: the e-way bill call goes onto a queue and is retried until it succeeds, while the dispatch itself is already durably committed.
- Explicit atomic blocks around multi-step operations
- External API calls kept outside the transaction, always
- Idempotency on anything that can be retried
- Database constraints as the final guarantee, not application checks alone
When Django is the wrong choice
Django is our default, not our answer to everything. We would recommend against it here:
- Real-time systems with thousands of persistent connections. Live chat, collaborative editing or game backends are better served by an event-driven runtime built for that.
- Very high-throughput, low-logic services. If a service does almost nothing per request but does it millions of times, Python's overhead is a real cost and Go is a better fit.
- Small static sites. A Django deployment for a five-page brochure site is unnecessary infrastructure. Use Next.js static export.
- Heavy compute in the request path. Python is fine as an orchestrator, but genuinely CPU-bound work belongs in a specialised worker rather than a web process.
What We Pair It With
Data
Frontend
Async
Platform
Quality
Frequently Asked Questions
For transactional business systems, Django's defaults are simply better suited: real database transactions, a migration system safe to run against live data, a mature permission model, and security protections built in rather than assembled from packages. Node is a better fit for real-time and event-driven workloads. We choose per project, and we will say when Django is not the right answer.
Related Expertise
PostgreSQL
The database we pair with Django by default.
RabbitMQ
Celery task processing and reliable third-party integration.
Angular Development
The enterprise front-end over these APIs.
Manufacturing ERP
The transactional workload Django was chosen for.
Fintech
Exact-decimal arithmetic and auditable financial state.
Web Application Development
How full-stack projects are scoped and delivered.
Backend That Has To Be Correct, Not Just Fast?
Transactions that must not partially fail, calculations that will be audited, integrations that cannot lose work. Tell us what the system has to guarantee and we'll tell you how we'd build it.
Serving startups, factories and enterprises across India, the US, UK, Australia & Europe.