Python Development Company: Django and FastAPI Back Ends, Data Pipelines, Automation and AI Features

Updated September 2026 · Written and maintained by the Progression Agency strategy team

A Python development company designs, writes and runs software in Python: web back ends and APIs built with Django, FastAPI or Flask, data pipelines that move and clean data on a schedule, automation that takes repetitive work off people, integrations between business systems, and machine learning features inside products. Progression Agency takes that work from a written scope to production for startups, product teams and operations leaders, then keeps it on supported Python and framework versions. Progression Agency is based in New York City and works with clients across the United States and worldwide.

What does a Python development company build?

Mostly the software behind the screen: web back ends and APIs, data pipelines, automation, integrations and machine learning features. Software development with Python covers all five because the language’s web frameworks, data tooling and machine learning libraries let one team own a product’s server side, the jobs that feed its reporting and the models inside it.

Web back ends and APIs

Django, FastAPI and Flask power the server side of web products: accounts, business rules, data access and the APIs that browsers and mobile apps call. The interface itself, whether server-rendered pages or a React front end, is covered on our web app development page; this page covers the Python underneath.

Data pipelines

Scheduled jobs that pull data from source systems, clean and reshape it, and load it where people and software can use it. Pipelines are judged on unglamorous virtues: they finish on time, fail loudly, and can be rerun without duplicating anything.

Automation

Scripts and services that take repetitive work off people: reconciling exports, generating documents, moving files between systems, checking inboxes and vendor portals. The best targets are frequent, rule-based and currently done by someone whose time is expensive.

Integrations

Connectors that keep a CRM, an ERP, an accounting system, a warehouse and a product database in agreement. Python’s HTTP clients and data libraries make it natural glue, and our CRM and ERP teams use it for exactly that.

Machine learning and AI features

Forecasts, classification, recommendations, search, document extraction and language-model features inside an existing product. When AI is the product itself, our AI app development company page describes that work; the Python engineering described here is what those features run on.

Internal tools and dashboards

Admin screens, approval workflows and reporting for your own staff. Some start as a Django admin and grow into full internal applications; data-heavy ones feed the reporting described on our dashboard development page.

Python for web app development: Django, FastAPI or Flask?

Choose Django when the product needs accounts, content administration and a relational data model from the first release; FastAPI when it is an API first and typed request and response models matter; Flask when the service is small and you want to choose each component yourself. All three are mature choices, and the right one follows the shape of the product.

Django web app development

Django software development suits products where the business edits data every day: marketplaces, portals, back-office systems and content-heavy products. Django’s overview describes user authentication, content administration, site maps and RSS feeds handled right out of the box, and its security documentation explains the default protections: template escaping against cross-site scripting, a CSRF secret checked on POST requests, parameterized querysets against SQL injection, clickjacking protection through X-Frame-Options middleware, and Host header validation against ALLOWED_HOSTS.

FastAPI for typed APIs

FastAPI builds APIs from standard Python type hints. Its documentation describes it as built on Starlette for the web parts and Pydantic for the data parts, fully compatible with OpenAPI and JSON Schema, and serving interactive documentation through Swagger UI at /docs and ReDoc at /redoc. That makes it a strong choice for APIs consumed by mobile apps, partners and other services.

Flask for small services

Flask’s documentation calls it a lightweight WSGI web application framework designed to make getting started quick and easy, with the ability to scale up to complex applications. We use it for small internal services and for maintaining existing Flask estates; for new products with accounts and admin needs, we usually reach for Django.

Combining frameworks in one product

Frameworks can share a product. A Django application can own accounts, billing and the admin while a FastAPI service handles a high-volume public API against the same database or a read replica. We split only when load, team ownership or release cadence gives a reason, and our API development practice designs the contracts between the parts.

Which Python version should your software run on?

New work should target a release in bugfix status, and nothing should run past end of life. On the Python developer’s guide status table, read September 30, 2026, Python 3.14 and 3.13 are in bugfix status, 3.12, 3.11 and 3.10 receive security fixes only, and 3.9 reached end of life on October 31, 2025; 3.10 follows in October 2026.

The five-year support window

Under PEP 602, a new feature release ships every October. From Python 3.13 on, each release gets 24 months of bug fixes followed by 36 months of security fixes; releases from 3.9 to 3.12 had 18 months of full support and three and a half years of security fixes. In bugfix status, new binaries are released roughly every two months; once a branch moves to security status, no more binaries are released. Long-lived software development in Python therefore plans an interpreter upgrade every two to three years.

What Python 3.13 and 3.14 changed for production teams

Python 3.13, released October 7, 2024, added an experimental free-threaded build with the global interpreter lock disabled, an experimental JIT compiler, a new interactive shell, and tier 3 support for iOS and Android (What’s New in Python 3.13). Python 3.14, released October 7, 2025, made free-threaded Python officially supported under PEP 779 and added template string literals, deferred evaluation of annotations, multiple interpreters in the standard library, Zstandard compression and Android binary releases (What’s New in Python 3.14). None of these forces an immediate change, but free threading and the JIT are worth benchmarking on CPU-bound services.

Django’s support calendar

Django runs its own schedule. The Django download page lists 5.2 as the current long-term support release with extended support to April 2028, 6.0 supported to April 2027, 6.1 to December 2027, and 6.2 LTS expected in April 2027. It also announces that beginning with Django 2028, feature releases will use a year-based version number and ship every January. Extended support covers security fixes and data-loss bugs only, so we plan Django upgrades against those dates the way we plan Python ones.

Data pipelines that finish on time

A pipeline is only useful if it finishes on schedule, fails loudly and can be rerun safely. We build batch pipelines orchestrated with Apache Airflow, queue-based processing with Celery, and validation at every hop, so each number in a report can be traced to its source.

Orchestrating batch work with Airflow

Apache Airflow describes itself as a platform for developing, scheduling and monitoring workflows, with workflows defined entirely in Python as DAGs. Its documentation positions it for batch workflows with a clear start and end that run on a schedule, which covers most reporting, finance and operations pipelines. For continuous event streams we choose a streaming tool instead.

Queues for work that should not wait

Some work must happen soon after an event rather than on a timetable: resizing an upload, scoring a lead, sending a notification. Celery describes task queues as a mechanism to distribute work across threads or machines, and lists RabbitMQ and Redis as feature-complete brokers. We use it to keep web requests fast and to spread heavy jobs over several workers.

Checks that stop bad data

Every pipeline stage validates what it receives: row counts, required fields, value ranges, duplicate keys and freshness. A failed check stops the run and alerts a person, because a pipeline that silently loads bad data is worse than one that stops.

Where the results land

Pipelines load into a warehouse or database that reporting tools read, and the dashboards on top are built by our dashboard development team. Each metric carries a written definition and an owner, so two dashboards never disagree about the same number.

Automation that takes repetitive work off your team

Python is well suited to rule-based tasks that people repeat every day or week. The list of targets comes from your team: anything done by hand, on a schedule, following rules that can be written down.

• Reconciling bank, payment-processor and ledger exports at the end of each day.
• Turning emailed orders, invoices and statements into validated records.
• Generating recurring client reports, certificates and invoices from templates.
• Moving files between SFTP servers, cloud storage and business systems on a schedule.
• Checking vendor portals for new documents, prices or stock levels.
• Cleaning and deduplicating CRM records before a campaign or migration.
• Watching logs and dashboards for thresholds and opening a ticket when one is crossed.

Scheduled scripts and services

Nightly reconciliations, weekly exports, report generation and data cleanups run as scheduled jobs with logging, alerting and a way to rerun a single day. Scripts that live on someone’s laptop move to a server with version control and monitoring.

Documents, spreadsheets and inboxes

Invoices, statements, forms and spreadsheets arrive in predictable shapes. Python reads them, extracts the fields, checks them against your records and routes exceptions to a person. Language models help with messy documents; validation rules keep them honest.

Browser automation when there is no API

Some vendor portals offer no API. Browser automation can log in, download reports and submit forms, but it breaks when the site changes, so we treat it as a stopgap, monitor it closely and replace it with an API connection as soon as one exists.

When a workflow tool is the better answer

Not every automation needs custom code. Workflow tools such as n8n handle many connector-to-connector flows with less upkeep, and our n8n automation agency and AI automation pages cover that route. Python earns its place when the logic is complex, data volumes are large, or the automation must be tested like software.

Integrations between the systems you already pay for

An integration keeps two systems in agreement without anyone copying data between them. Python connectors call each system’s API, map fields between them, and record every change so a mismatch can be traced back to its cause.

CRM, ERP and finance connectors

The usual pairs are CRM to ERP, ERP to accounting, e-commerce to inventory, and product database to warehouse. Field mapping is agreed in writing before coding, because most integration bugs turn out to be disagreements about what a field means.

Webhooks and events

Where systems support webhooks, the integration reacts to changes as they happen instead of polling. Incoming events are verified, stored before processing and handled by background workers, so a slow downstream system never loses an event.

Retries, idempotency and audit logs

Networks fail and APIs rate-limit. Every call is retried with backoff, every write is idempotent so a retry cannot create duplicates, and every change is logged with its source. Our API development page covers building the APIs these connectors call when a system has none.

Machine learning and AI features built in Python

The main machine learning libraries, including scikit-learn and PyTorch, are Python libraries, so machine learning and AI features inside a product are usually built as Python services. We treat a model like any other component: it has requirements, tests, monitoring and a rollback plan.

Classic machine learning with scikit-learn

scikit-learn describes itself as simple and efficient tools for predictive data analysis, built on NumPy, SciPy and matplotlib and open source under the BSD license. Its task areas, classification, regression, clustering, dimensionality reduction, model selection and preprocessing, cover most business scoring and forecasting problems, such as churn risk, demand forecasts and lead scoring.

Deep learning with PyTorch

For images, audio, text and other unstructured data we use PyTorch. Its site notes that the latest stable release requires Python 3.10 or later, one more reason to keep interpreters current. Many products do not need to train deep models from scratch; fine-tuning or calling a hosted model is usually the cheaper path.

Chatbots, retrieval and AI agents

Language-model features, such as a support chatbot grounded in your documentation or an agent that files tickets and drafts replies, are orchestration problems: retrieving the right content, calling the model, validating the output and logging every step. Our AI chatbot development, AI agent development and AI integration pages describe those builds; the Python service underneath is engineered like any other production service.

Testing a model before customers see it

Every model is measured on data it has never seen, against a baseline that a simple rule could achieve. Language-model features get a written test set of real questions with expected answers, scored before each release, so a prompt or model change cannot quietly make answers worse.

Mobile app development in Python: where it fits

Python can run inside a phone app, but for most commercial products the stronger pattern is a native or cross-platform app on the phone and Python on the server. App development in Python for phones is possible and improving; it is rarely the fastest route to a polished app store listing.

What changed in Python 3.13

Python 3.13 made iOS and Android officially supported CPython platforms at tier 3, under PEP 730 and PEP 738. On Android, Python runs as an embedded library loaded into the app’s process rather than as a standalone interpreter, and PEP 738 notes that Chaquopy, BeeWare and Kivy had already shipped apps on the Google Play Store that way.

Kivy and BeeWare

Kivy is an MIT-licensed framework for cross-platform Python GUI apps on Android, iOS, Linux, macOS and Windows. BeeWare lets you write an app in Python and release it on iOS, Android, Windows, macOS, Linux, the web and tvOS with native user interfaces, using its Toga toolkit and Briefcase packager. Both suit internal tools, prototypes and apps where sharing Python code matters more than platform polish.

Python for Android app development in practice

For an Android app aimed at the Play Store, mobile app development with Python usually means Kotlin, React Native or Flutter on the device and a Python API behind it. The phone handles the interface, notifications and offline storage; Python handles accounts, business logic, data and models. See our Android app development, React Native and Flutter pages for the device side.

When Python on the phone makes sense

Running Python on the device earns its place when the same Python code must run offline on the phone, such as a scientific calculation or an on-device model, or when a small team knows only Python and the app is for internal use. We test the packaging path first, since app store review, binary size and startup time are the risks that decide whether on-device Python is viable.

Modernizing an existing Python codebase

Python modernization is unglamorous and safe when it is done in order: inventory, tests, interpreter upgrade, dependency upgrades, then refactoring. We do not rewrite working code to follow fashion; we make it supportable.

Inventory first

We record the interpreter version, every dependency with its latest release and known vulnerabilities, the framework version against its support dates, test coverage on the critical paths, and how the code is deployed. The result is a written list of risks in priority order.

Code still on Python 2

Python 2.7 reached end of life on January 1, 2020, according to the developer’s guide, so anything still running it has gone years without fixes. Porting is a project of its own: dependencies first, then syntax and text handling, then the tests, run under both interpreters until the switch is made.

Framework and dependency upgrades

Django feature releases arrive roughly every eight months, and long-term support releases typically receive security and data-loss fixes for three years, according to Django’s release process. We move through each feature release in turn, fixing deprecation warnings as we go, and upgrade the rest of the dependency tree in the same small steps.

Performance and scaling in Python

Python services are fast enough for most products when the database, caching and background work are designed well; when they are not, the language is rarely the cause. We measure first, then change the part that is actually slow.

• Profile with production-like data before changing any code.
• Fix database queries and indexes before touching application logic.
• Move slow or bursty work to a queue so web requests stay short.
• Cache responses that many users read and few users change.
• Benchmark CPU-bound code on the free-threaded build before adding processes.
• Scale horizontally with more workers once a single worker is efficient.

Measure before optimizing

Profiling a slow endpoint usually points at a query, a missing index, an unbatched external call or work that belongs in a queue. We profile with production-like data, fix the largest cost first, and keep a benchmark that proves each change.

Concurrency: async, processes and free threading

Async frameworks such as FastAPI handle many concurrent requests that spend their time waiting on networks and databases. CPU-bound work has traditionally used several processes; Python 3.14 officially supports the free-threaded build, and its release notes put the single-threaded performance penalty at roughly 5-10%, which makes threads worth testing for CPU-heavy services.

Caching and queues

Read-heavy pages and API responses are cached; slow work moves to Celery or another queue so requests return quickly; the database gets the indexes its real queries need. Horizontal scaling, more workers behind a load balancer, handles the rest.

Security, typing and code quality

Maintainable Python is written for the next developer as much as for the interpreter. We keep framework protections on, audit dependencies, check types in CI and test the paths that matter before every merge.

Framework protections we keep switched on

Django’s protections against cross-site scripting, CSRF, SQL injection and clickjacking stay enabled, and ALLOWED_HOSTS is set for every environment. FastAPI and Flask services get the equivalent measures added explicitly, because their smaller cores leave more to the developer.

Dependencies and the software supply chain

Every dependency is pinned in a lock file, scanned for known vulnerabilities in CI and listed in a software bill of materials, which CISA describes as a nested inventory of the ingredients that make up software components. The practices follow NIST’s Secure Software Development Framework, a core set of high-level secure development practices designed to fit into any development life cycle.

Type hints and static checking

The typing module documentation is explicit that the Python runtime does not enforce type annotations; they are used by third-party tools such as type checkers, IDEs and linters. We run a type checker in CI so annotations stay true, which matters most in large codebases and in FastAPI services, where types also define the API.

Tests and continuous integration

Unit tests cover business rules, integration tests cover databases and external APIs with recorded responses, and a small set of end-to-end tests covers the critical flows. CI runs formatters, linters, type checks, tests and dependency scans on every pull request, and a failing check blocks the merge.

How much does Python software development cost?

Scope, integrations and data quality decide the price, not the language. Our python software development services are planned against the ranges we publish for custom software, from $15,000–$50,000 for an integration or automation project to $250,000 and up for enterprise modernization, and every quote follows a written scope.

Mobile front ends for a Python back end follow the ranges on our app development agency page: $8,000–$25,000 for a clickable prototype, $40,000–$100,000 for a focused MVP on one or two platforms, $100,000–$250,000 for a full business app, and $250,000 and up for marketplace and enterprise apps. All of these are planning ranges from our software development company and app pages; a fixed price follows a written scope.

The chart shows what advertisers pay for a single click on Python development searches in the United States. Higher bids tend to mark phrases typed by buyers rather than by people learning the language.

From scoping call to production: timeline and checkpoints

A focused Python integration can reach production in 4–10 weeks and a SaaS MVP in 3–6 months, following the durations on our published ranges. Each phase ends with something you can see and a measure we report.

Hire Python developers or bring in an outside team?

Hire in-house when Python work is permanent and central to what you sell; bring in an outside team when the work is a project, needs skills you lack, or must start before a hire could. Some teams do both: an outside team builds and an in-house engineer takes over.

What the labor market data says

The U.S. Bureau of Labor Statistics reports a 2025 median pay of $135,980 a year for software developers, and $134,040 for the wider group that includes quality assurance analysts and testers. It projects employment in that group to grow 10% from 2025 to 2035, much faster than average, with about 106,100 openings a year. BLS does not publish Python-specific figures, but the numbers explain why experienced engineers take time to hire.

Python developer or Python engineer?

The titles overlap and employers use them loosely. In job descriptions, a developer usually builds features, while an engineer is also expected to own design, reliability, testing and operations. When hiring, look past the title to what the candidate designed, how they tested it and how they handled failures in production.

Mixing in-house and outside help

One pattern that works is an outside team for the first build and a hire who joins before handover, shadows the build and inherits the code. Our dedicated developer model also places Python engineers inside your team, under your process, for months rather than weeks.

AI answers: how technical buyers ask assistants for a Python partner

Buyers describe the job, not the vendor, when they ask ChatGPT, Claude, Perplexity, Gemini, Microsoft Copilot or Google’s AI Overviews for a Python partner. The table pairs typical prompts with what an assistant needs to find on a site before it names that firm.

Where assistants find candidates

Assistants that browse build answers from what a search returns. For ‘python development company’ in September 2026 that meant agency service pages, the Clutch directory, a ’15 best Python development companies in the USA’ article and other ‘top 10’ lists. Google states that its AI features need no special optimization beyond being indexed and eligible for a snippet (Google Search Central), and ChatGPT search surfaces sites that allow OpenAI’s OAI-SearchBot crawler (OpenAI crawler documentation).

Signals that get a Python firm named

Specific, checkable statements beat adjectives. The pages that help most:

• Stack pages that name frameworks and versions, such as Django 5.2 LTS or Python 3.13, with dates.
• Scoped examples: what was built, which systems it connected and what it replaced, published with permission.
• Planning prices and durations in text rather than behind a form.
• Engineers’ names on articles, talks and open-source work, so an assistant can connect people to the firm.
• Consistent company facts across the site, directories and profiles.
• Crawler access for the assistants’ search bots, and prompt notice to Bing of new pages through IndexNow, which Bing supports.

Our answer engine optimization team runs this program for clients, including software firms.

How to choose a Python software development company

Choose on evidence: current versions, tested code, careful data handling and a clean handover. The checks below work whether you are comparing a python app development company for a mobile back end, a data consultancy or a general software firm with a Python team.

A firm that passes these checks can show its work, which is what you want from python app development services that will handle your data.

Related services

Python work usually sits inside a larger product; these pages cover the pieces around it.

• AI app development company: products where AI is the core feature, from design to launch.
• Web app development: complete browser applications, including the front ends on Python back ends.
• API development: API design, security and documentation in any stack.
• Dashboard development: reporting and analytics interfaces fed by Python pipelines.
• AI agent development: agents that act inside your systems, with guardrails and logs.
• AI chatbot development: support and sales chatbots grounded in your own content.
• Back-end development: server-side work across Python, Node.js, .NET and PHP.
• Hire dedicated developers: Python engineers embedded in your team.
• n8n automation agency: workflow automation where a visual tool beats custom code.
• Cloud app development: cloud-native services and infrastructure for Python workloads.
• Ruby on Rails development company: Rails products that call Python services.
• Software development for startups: an ongoing engineering partner after the MVP.

Frequently asked questions