Python development services for APIs, data and AI

Python in 2026: framework, version and team

A practical guide for anyone commissioning Python work: which framework, which version, how Django’s support policy is changing, and what moves the budget. Figures were checked against the official release pages in October 2026.

FastAPI, Django or Flask?

A common and sensible split is Django for the back office and FastAPI for high-traffic or AI endpoints, sharing one database. A good Django development company will tell you when the admin alone saves you a month, and when it is dead weight.

Python version planner

Python releases a new version every October and supports each one for five years. Dates come from the Python developer’s guide.

Two practical notes. On AWS Lambda, the python3.10 runtime is deprecated from 31 October 2026, while the 3.15 runtime is still in public preview. And Django 6.1’s release notes list Python 3.15 support from 6.1.3, which had not shipped by the end of October 2026, so Django teams should hold 3.15 back a little longer.

Django’s support policy is changing

Until now Django shipped a feature release roughly every eight months and gave three years of support only to designated LTS releases. In August 2026 the project accepted DEP 20: from January 2028 there is one release a year, named after the year (Django 2028, Django 2029), and every release gets three years of support, one with bug fixes and two with security and data-loss fixes. The LTS label is retired.

What it means for you: anything older than 5.2 no longer receives security fixes. If you are on 5.2, which is the last series to support Python 3.10 and 3.11, you have until April 2028, and 6.2 LTS is the natural next stop. After 2028 the rhythm is simpler: one upgrade a year keeps you inside support.

Modern Python tooling

Python’s reputation for painful setup is mostly out of date. Our projects use:

• uv for Python versions, virtual environments and a lockfile, so a new developer or a CI runner gets an identical environment from one command.
• ruff for linting and formatting, fast enough to run on every save.
• Type hints checked with mypy or pyright in CI. They catch a class of bugs before review and make large codebases navigable.
• Pydantic 2 at every boundary: API input, configuration, messages from queues and responses from AI models.

Async: when it helps and when it hurts

Async Python shines when a request waits on many slow things at once: several upstream APIs, a model call, a database. One process can then serve many requests concurrently. It hurts when a single blocking call, such as a synchronous database driver or HTTP client inside an async route, stalls every request on that worker. Mixed sync and async code is the most common source of mysterious latency we are asked to fix. CPU-heavy work belongs in separate worker processes either way. The free-threaded build, officially supported since Python 3.14, is promising for CPU-bound code, but check that every C extension you rely on supports it before planning around it.

Python for AI development, data pipelines and LLM services

Most production AI features are ordinary software with one unusual dependency. The engineering that makes them reliable is retrieval that respects permissions, prompts and model calls behind one interface so you can change models, an evaluation set that scores answers on every change, and cost tracking per request and per customer. Our guide to RAG in production covers the guardrails. For agents that take actions in other systems, see AI agent development; for the data platform underneath, AI and data engineering.

Hiring models: dedicated developers, a project team, or offshore

If you plan to hire Python developers in India, decide first who will direct the work. Dedicated engineers through staff augmentation take tasks from your backlog and follow your reviews, which suits teams with an experienced tech lead. A project team owns a written scope, milestones and delivery risk, which suits companies without in-house engineering leadership. Offshore Python development from India works well when overlap hours, code ownership and review standards are agreed in writing at the start. Our staff augmentation vs agency comparison sets out the trade-offs.

What drives the cost of a Python project

We do not publish prices. These are the factors that decide the figure in a written proposal:

• Data quality. Cleaning and reconciling messy source data often takes longer than building the pipeline.
• Integrations. Each external system adds authentication, error handling, retries and test data.
• AI evaluation. Building a labelled test set and quality targets is real work, and skipping it is how AI features fail quietly.
• Model and infrastructure running costs. These are yours, not ours, and they belong in the business case from day one.
• Legacy upgrades. Old Django versions with unmaintained packages cost more to move than the version gap suggests.
• Compliance. Audit logging, data residency and retention rules for GDPR, HIPAA or DPDP.

Common mistakes

• Shipping a notebook as a service, with no tests, no pinned dependencies and secrets in the code.
• Blocking calls inside async routes.
• Long jobs inside web requests, which time out under load.
• Unpinned FastAPI and Pydantic versions that change behaviour between deploys.
• No evaluation set for an AI feature, so nobody notices when answer quality drops.
• Staying on an unsupported Django because the upgrade looks big, while each year makes it bigger.

How to choose a Python development company

• Ask which Python and Django versions they will target and how they will keep you in support.
• Ask to see their standard project template: uv or equivalent, ruff, type checking and tests in CI.
• For AI work, ask how they measure answer quality and cost before launch.
• Check that code, cloud accounts and model-provider accounts will be in your name.
• Meet the engineers who will do the work, not only the sales lead.
• Expect them to recommend a non-Python option when it is cheaper.