Python & Django Development Services for US & EU SaaS, Fintech and Data Products

Django wins on three axes: a mature ORM that handles complex schemas without leaking to raw SQL too often, the Django Admin which is operationally indispensable for B2B SaaS and internal-ops products, and an ecosystem (django-allauth, django-tenants, django-filter, DRF, Wagtail) that covers 80% of common SaaS needs without you reinventing them. Pick FastAPI for pure-async high-concurrency services. Pick Go for CPU-bound binaries. Pick Rails only if you already have Ruby expertise on the team — the gap to Django is small but the talent pool in EU and US in 2026 strongly favours Python.

Django 5.1 LTS is the default for new builds (LTS through April 2028), with Django 4.2 LTS for legacy migration targets. Python 3.12 default, 3.13 once ecosystem catches up (Q3 2026 for our shop). DRF 3.15+ with Spectacular for OpenAPI 3.1. mypy 1.11+ in strict mode with django-stubs. We use uv for dependency management and ruff for linting and formatting — both have replaced pip-tools, black and flake8 in our toolchain since 2025.

Yes. Default deployment is AWS eu-central-1 / eu-west-1, GCP europe-west3 / europe-west1, Azure germany-westcentral on request. PII at rest is encrypted with django-cryptography or pgcrypto, with KMS keys held in the customer account. We sign GDPR Article 28 DPAs, run DPIAs for high-risk processing, and maintain Article 30 records. For SOC 2 Type I/II we ship the controls evidence pack: access reviews, change management via PRs, encrypted backups, audit logs, vendor management. For US clients we mirror in us-east-1 / us-west-2 with CCPA and (where relevant) HIPAA controls.

Past ~500 tasks/sec on a single Redis broker, we shard Celery queues by workload type, move heavy fan-out to Dramatiq or Temporal, and adopt Redis Cluster or RabbitMQ with proper prefetch tuning. We monitor with Flower plus Sentry Cron and Datadog APM. For exactly-once semantics we use the outbox pattern on Postgres rather than relying on broker delivery guarantees. The largest pipeline we operate currently runs 8M Celery tasks per day on a 12-worker EKS deployment with p95 latency under 1.2 seconds.

Yes, this is one of our most common engagements. We run incremental upgrades: 2.2 LTS → 3.2 LTS → 4.2 LTS → 5.1 LTS, with the test suite green at every step. Python upgrades 3.7 → 3.8 → 3.10 → 3.12 in parallel. We add django-stubs and mypy strict per app, not globally. The strangler-fig pattern peels off bounded contexts into FastAPI or Django Ninja services where async or independent scaling earns it. Typical 100k-LOC monolith takes 5 to 8 months with no customer-visible downtime.

Pricing is fixed-scope and all-in, quoted in USD, with no recruitment markup and no tool surcharges. A web app MVP or client dashboard runs from $4,100 (4–8 weeks); a CRM or B2B portal from $8,100 (8–12 weeks); a production multi-tenant SaaS platform from $13,800 (12+ weeks); a marketplace or highload build from $28,700. The exact number depends on the data-model and admin surface, the integration count, the throughput profile and the compliance scope. You see the line-item budget before any code is written and sign off on it. All engagements include CET workday with 9 AM–1 PM ET overlap for US clients, and NDA + DPA + IP assignment signed before kickoff.

Yes, though the pattern matters. For low-frequency inference (<100 req/min) we call a scikit-learn, XGBoost or ONNX model directly from a Django view — simple and no extra infrastructure. For higher throughput we run a dedicated FastAPI inference service (with Uvicorn + model loaded once at startup) behind an internal load balancer, and the Django app calls it via httpx async. For GPU-heavy workloads (LLM inference, image generation) we deploy separate Triton Inference Server or vLLM instances on GPU nodes and call them from Celery tasks to keep the web request path fast. Feature pipelines that feed models go through Airflow or Prefect with dbt transformations and output to ClickHouse or Postgres for serving. We have production experience with OpenAI, Anthropic, Mistral and self-hosted Llama integrations from Django via the standard SDKs.

Pytest with factory_boy for fixtures — we avoid Django's TestCase where pytest-django's transactional fixtures serve better. Coverage target is 80%+ on business logic and API views; we do not chase 100% on trivial CRUD. Property-based tests with Hypothesis for validation logic and serialisers where the input space is large. Contract tests with Pact or a lightweight OpenAPI snapshot against downstream services so we catch breaking changes without running the full dependency. Load tests with Locust before each major release — especially Celery queue throughput and Postgres query plans under realistic concurrency. mypy strict catches a class of runtime bugs at CI time rather than in production. Every PR runs the full suite on GitHub Actions (typically under 4 minutes on a mid-tier runner with test parallelism via pytest-xdist).

We treat migrations as production-critical code. Every migration is reviewed for lock acquisition: adding a column with a default or adding a NOT NULL column without a default takes a full table lock on Postgres, which blocks reads on large tables. We use django-pg-zero-downtime-migrations or write manual multi-step migrations (add nullable, backfill in batches via RunPython, set NOT NULL with a check constraint to avoid a full scan). Index creation always runs CONCURRENTLY. Squashing happens on a schedule to keep the migration history manageable. On the deploy side: migrations run in a dedicated pre-deploy step before traffic shifts (blue/green or rolling), and we keep a tested rollback plan (usually a reverse migration or a data snapshot) for every migration that touches existing data. For the strangler-fig monolith-to-services split, we coordinate dual-write periods and use feature flags to gate reads from the new service while the old table is still the source of truth.

Three models, chosen at the start rather than after an incident. A warranty period (typically 90 days, included in the fixed-scope price) covers defects attributable to our implementation at no additional charge. A retainer model gives you a defined SLA on response time (P1 <1 h, P2 <4 h, P3 next business day), monthly dependency upgrades via Renovate, Postgres slow-query reviews, cloud cost checks and a fixed number of hours per sprint for small features and integrations. Staff augmentation keeps one or two senior engineers slotted into your squad permanently — your stand-up, your backlog, your process. All models include on-call handover documentation (runbooks, Grafana SLO dashboards, PagerDuty routing) and a knowledge-transfer session before we reduce team size.