You've built your FastAPI app, it's running great locally, and now you want to share it with the world. But then reality hits -- containers, load balancers, HTTPS certificates, cloud consoles with 200 options. What if deploying was just one command? That's exactly what Sebastian Ramirez and the FastAPI Cloud team are building. On this episode, I sit down with Sebastian, Patrick Arminio, Savannah Ostrowski, and Jonathan Ehwald to go inside FastAPI Cloud, explore what it means to build a "Pythonic" cloud, and dig into how this commercial venture is actually making FastAPI the open-source project stronger than ever.

Command Book

Python in Production

Talk Python Courses

Links from the show

Sebastián Ramírez: github.com

Savannah Ostrowski: github.com

Patrick Arminio: github.com

Jonathan Ehwald: github.com

FastAPI labs: fastapilabs.com

quickstart: fastapicloud.com

an episode on diskcache: talkpython.fm

Fastar: github.com

FastAPI: The Documentary: www.youtube.com

Tailwind CSS Situation: adams-morning-walk.transistor.fm

FastAPI Job Meme: fastapi.meme

Migrate an Existing Project: fastapicloud.com

Join the waitlist: fastapicloud.com

Talk Python CLI Announcement: talkpython.fm

Talk Python CLI GitHub: github.com

Download Command Book: commandbookapp.com

Announcement post: mkennedy.codes

Watch this episode on YouTube: youtube.com

Episode #536 deep-dive: talkpython.fm/536

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

Building on the web is like working with the perfect clay. It’s malleable and can become almost anything. But too often, frameworks try to hide the web’s best parts away from us. Today, we’re looking at PyView, a project that brings the real-time power of Phoenix LiveView directly into the Python world. I'm joined by Larry Ogrodnek to dive into PyView.

Talk Python Courses

Python in Production

Links from the show

Larry Ogrodnek: hachyderm.io

pyview.rocks: pyview.rocks

Phoenix LiveView: github.com

this section: pyview.rocks

Core Concepts: pyview.rocks

Socket and Context: pyview.rocks

Event Handling: pyview.rocks

LiveComponents: pyview.rocks

Routing: pyview.rocks

Templating: pyview.rocks

HTML Templates: pyview.rocks

T-String Templates: pyview.rocks

File Uploads: pyview.rocks

Streams: pyview.rocks

Sessions & Authentication: pyview.rocks

Single-File Apps: pyview.rocks

starlette: starlette.dev

wsproto: github.com

apscheduler: github.com

t-dom project: github.com

Watch this episode on YouTube: youtube.com

Episode #535 deep-dive: talkpython.fm/535

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

Your cloud SSD is sitting there, bored, and it would like a job. Today we’re putting it to work with DiskCache, a simple, practical cache built on SQLite that can speed things up without spinning up Redis or extra services. Once you start to see what it can do, a universe of possibilities opens up. We're joined by Vincent Warmerdam to dive into DiskCache.

Talk Python Courses

Python in Production

Links from the show

diskcache docs: grantjenks.com

LLM Building Blocks for Python course: training.talkpython.fm

JSONDisk: grantjenks.com

Git Code Archaeology Charts: koaning.github.io

Talk Python Cache Admin UI: blobs.talkpython.fm

Litestream SQLite streaming: litestream.io

Plash hosting: pla.sh

Watch this episode on YouTube: youtube.com

Episode #534 deep-dive: talkpython.fm/534

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

Today on Talk Python, the creators behind FastAPI, Flask, Django, Quart, and Litestar get practical about running apps based on their framework in production. Deployment patterns, async gotchas, servers, scaling, and the stuff you only learn at 2 a.m. when the pager goes off. For Django, we have Carlton Gibson and Jeff Triplet. For Flask, we have David Lord and Phil Jones, and on team Litestar we have Janek Nouvertné and Cody Fincher, and finally Sebastián Ramírez from FastAPI is here. Let’s jump in.

Talk Python Courses

Python in Production

Links from the show

Carlton Gibson - Django: github.com

Sebastian Ramirez - FastAPI: github.com

David Lord - Flask: davidism.com

Phil Jones - Flask and Quartz(async): pgjones.dev

Yanik Nouvertne - LiteStar: github.com

Cody Fincher - LiteStar: github.com

Jeff Triplett - Django: jefftriplett.com

Django: www.djangoproject.com

Flask: flask.palletsprojects.com

Quart: quart.palletsprojects.com

Litestar: litestar.dev

FastAPI: fastapi.tiangolo.com

Coolify: coolify.io

ASGI: asgi.readthedocs.io

WSGI (PEP 3333): peps.python.org

Granian: github.com

Hypercorn: github.com

uvicorn: uvicorn.dev

Gunicorn: gunicorn.org

Hypercorn: hypercorn.readthedocs.io

Daphne: github.com

Nginx: nginx.org

Docker: www.docker.com

Kubernetes: kubernetes.io

PostgreSQL: www.postgresql.org

SQLite: www.sqlite.org

Celery: docs.celeryq.dev

SQLAlchemy: www.sqlalchemy.org

Django REST framework: www.django-rest-framework.org

Jinja: jinja.palletsprojects.com

Click: click.palletsprojects.com

HTMX: htmx.org

Server-Sent Events (SSE): developer.mozilla.org

WebSockets (RFC 6455): www.rfc-editor.org

HTTP/2 (RFC 9113): www.rfc-editor.org

HTTP/3 (RFC 9114): www.rfc-editor.org

uv: docs.astral.sh

Amazon Web Services (AWS): aws.amazon.com

Microsoft Azure: azure.microsoft.com

Google Cloud Run: cloud.google.com

Amazon ECS: aws.amazon.com

AlloyDB for PostgreSQL: cloud.google.com

Fly.io: fly.io

Render: render.com

Cloudflare: www.cloudflare.com

Fastly: www.fastly.com

Watch this episode on YouTube: youtube.com

Episode #533 deep-dive: talkpython.fm/533

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

Python in 2025 is in a delightfully refreshing place: the GIL's days are numbered, packaging is getting sharper tools, and the type checkers are multiplying like gremlins snacking after midnight. On this episode, we have an amazing panel to give us a range of perspectives on what matter in 2025 in Python. We have Barry Warsaw, Brett Cannon, Gregory Kapfhammer, Jodie Burchell, Reuven Lerner, and Thomas Wouters on to give us their thoughts.

Seer: AI Debugging, Code talkpython26

Talk Python Courses

Links from the show

Python Software Foundation (PSF): www.python.org

PEP 810: Explicit lazy imports: peps.python.org

PEP 779: Free-threaded Python is officially supported: peps.python.org

PEP 723: Inline script metadata: peps.python.org

PyCharm: www.jetbrains.com

JetBrains: www.jetbrains.com

Visual Studio Code: code.visualstudio.com

pandas: pandas.pydata.org

PydanticAI: ai.pydantic.dev

OpenAI API docs: platform.openai.com

uv: docs.astral.sh

Hatch: github.com

PDM: pdm-project.org

Poetry: python-poetry.org

Project Jupyter: jupyter.org

JupyterLite: jupyterlite.readthedocs.io

PEP 690: Lazy Imports: peps.python.org

PyTorch: pytorch.org

Python concurrent.futures: docs.python.org

Python Package Index (PyPI): pypi.org

EuroPython: tickets.europython.eu

TensorFlow: www.tensorflow.org

Keras: keras.io

PyCon US: us.pycon.org

NumFOCUS: numfocus.org

Python discussion forum (discuss.python.org): discuss.python.org

Language Server Protocol: microsoft.github.io

mypy: mypy-lang.org

Pyright: github.com

Pylance: marketplace.visualstudio.com

Pyrefly: github.com

ty: github.com

Zuban: docs.zubanls.com

Jedi: jedi.readthedocs.io

GitHub: github.com

PyOhio: www.pyohio.org

Watch this episode on YouTube: youtube.com

Episode #532 deep-dive: talkpython.fm/532

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

Have you ever thought about getting your small product into production, but are worried about the cost of the big cloud providers? Or maybe you think your current cloud service is over-architected and costing you too much? Well, in this episode, we interview Michael Kennedy, author of "Talk Python in Production," a new book that guides you through deploying web apps at scale with right-sized engineering.

Seer: AI Debugging, Code talkpython26

Agntcy

Talk Python Courses

Links from the show

Christopher Trudeau - guest host: www.linkedin.com

Michael's personal site: mkennedy.codes

Talk Python in Production Book: talkpython.fm

glances: github.com

btop: github.com

Uptimekuma: uptimekuma.org

Coolify: coolify.io

Talk Python Blog: talkpython.fm

Hetzner (€20 credit with link): hetzner.cloud

OpalStack: www.opalstack.com

Bunny.net CDN: bunny.net

Galleries from the book: github.com

Pandoc: pandoc.org

Docker: www.docker.com

Watch this episode on YouTube: youtube.com

Episode #531 deep-dive: talkpython.fm/531

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

For years, building interactive widgets in Python notebooks meant wrestling with toolchains, platform quirks, and a mountain of JavaScript machinery. Most developers took one look and backed away slowly. Trevor Manz decided that barrier did not need to exist. His idea was simple: give Python users just enough JavaScript to unlock the web’s interactivity, without dragging along the rest of the web ecosystem. That idea became anywidget, and it is quickly becoming the quiet connective tissue of modern interactive computing. Today we dig into how it works, why it has taken off, and how it might change the way we explore data.

Seer: AI Debugging, Code talkpython26

PyCharm, code STRONGER PYTHON

Talk Python Courses

Links from the show

Trevor on GitHub: github.com

anywidget GitHub: github.com

Trevor's SciPy 2024 Talk: www.youtube.com

Marimo GitHub: github.com

Myst (Markdown docs): mystmd.org

Altair: altair-viz.github.io

DuckDB: duckdb.org

Mosaic: uwdata.github.io

ipywidgets: ipywidgets.readthedocs.io

Tension between Web and Data Sci Graphic: blobs.talkpython.fm

Quak: github.com

Walk through building a widget: anywidget.dev

Widget Gallery: anywidget.dev

Video: How do I anywidget?: www.youtube.com

PyCharm + PSF Fundraiser: pycharm-psf-2025 code STRONGER PYTHON

Watch this episode on YouTube: youtube.com

Episode #530 deep-dive: talkpython.fm/530

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

A lot of people building software today never took the traditional CS path. They arrived through curiosity, a job that needed automating, or a late-night itch to make something work. This week, David Kopec joins me to talk about rebuilding computer science for exactly those folks, the ones who learned to program first and are now ready to understand the deeper ideas that power the tools they use every day.

Sentry Error Monitoring, Code talkpython26

NordStellar

Talk Python Courses

Links from the show

David Kopec: davekopec.com

Classic Computer Science Book: amazon.com

Computer Science from Scratch Book: computersciencefromscratch.com

Computer Science from Scratch at NoStartch (CSFS30 for 30% off): nostarch.com

Watch this episode on YouTube: youtube.com

Episode #529 deep-dive: talkpython.fm/529

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

In this episode, I’m talking with Vincent Warmerdam about treating LLMs as just another API in your Python app, with clear boundaries, small focused endpoints, and good monitoring. We’ll dig into patterns for wrapping these calls, caching and inspecting responses, and deciding where an LLM API actually earns its keep in your architecture.

Seer: AI Debugging, Code talkpython26

NordStellar

Talk Python Courses

Links from the show

Vincent on X: @fishnets88

Vincent on Mastodon: @koaning

LLM Building Blocks for Python Co-urse: training.talkpython.fm

Top Talk Python Episodes of 2024: talkpython.fm

LLM Usage - Datasette: llm.datasette.io

DiskCache - Disk Backed Cache (Documentation): grantjenks.com

smartfunc - Turn docstrings into LLM-functions: github.com

Ollama: ollama.com

LM Studio - Local AI: lmstudio.ai

marimo - A Next-Generation Python Notebook: marimo.io

Pydantic: pydantic.dev

Instructor - Complex Schemas & Validation (Python): python.useinstructor.com

Diving into PydanticAI with marimo: youtube.com

Cline - AI Coding Agent: cline.bot

OpenRouter - The Unified Interface For LLMs: openrouter.ai

Leafcloud: leaf.cloud

OpenAI looks for its "Google Chrome" moment with new Atlas web browser: arstechnica.com

Watch this episode on YouTube: youtube.com

Episode #528 deep-dive: talkpython.fm/528

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

Today we’re digging into the Model Context Protocol, or MCP. Think LSP for AI: build a small Python service once and your tools and data show up across editors and agents like VS Code, Claude Code, and more. My guest, Den Delimarsky from Microsoft, helps build this space and will keep us honest about what’s solid versus what's just shiny. We’ll keep it practical: transports that actually work, guardrails you can trust, and a tiny server you could ship this week. By the end, you’ll have a clear mental model and a path to plug Python into the internet of agents.

Sentry AI Monitoring, Code talkpython26

NordStellar

Talk Python Courses

Links from the show

Den Delimarsky: den.dev

Agentic AI Programming for Python Course: training.talkpython.fm

Model Context Protocol: modelcontextprotocol.io

Model Context Protocol Specification (2025-03-26): modelcontextprotocol.io

MCP Python Package (PyPI): pypi.org

Awesome MCP Servers (punkpeye) GitHub Repo: github.com

Visual Studio Code Docs: Copilot MCP Servers: code.visualstudio.com

GitHub MCP Server (GitHub repo): github.com

GitHub Blog: Meet the GitHub MCP Registry: github.blog

MultiViewer App: multiviewer.app

GitHub Blog: Spec-driven development with AI (open source toolkit): github.blog

Model Context Protocol Registry (GitHub): github.com

mcp (GitHub organization): github.com

Tailscale: tailscale.com

Watch this episode on YouTube: youtube.com

Episode #527 deep-dive: talkpython.fm/527

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy