Everyone who is comparing Anaconda and conda to Astral and uv is missing that the conda ecosystem is language-agnostic while uv is python specific. uv won't help you install gfortran, for example. It is not a replacement, unless you only do python (and use at most common non-python libraries that are available on PyPI).
On the other hand you don't have to use anything associated with Anaconda to use the conda ecosystem. Alternative package managers like mamba and pixi rely on the conda-forge channel instead. Pixi in particular (https://pixi.sh/) is sort-of the uv for the conda ecosystem workflow-wise, and works pretty well if you want that.
I admit I'm not super well versed in what conda's main uses are. Python's whole tooling situation has felt like a nightmare & I've tried to keep far away, but I've had to face it a lot more recently because it's so prevalent for AI. Thankfully uv seems to have done a huge amount of what I need.
Still, if the concern is language-agnostic ways to use tooling, mise (nee rtx) is the 1000 pound gorilla in the room today. Incredibly fast well built Rust based tool that has really massively expanded in scope & offerings, with grace & elegance. I thought it was an asdf replacement, for installing/using toolchains, for .tool-versions files. But it's really grown to be a lot more, capable of letting you isolatedly manage tools it can install from a huge variety of backends (pip, npm, cargo, others). https://mise.jdx.dev/dev-tools/backends/
Hadn't heard about that before, looks interesting. But AFAICS mise focuses on installing tools, not actually all dependencies. It integrates with the languages dependency management tools, which doesn't help you if the language doesn't even have one or the integration wasn't written yet.
I'd rather see more adoption of guix for this purpose. It is a single package manager with a functional approach that allows for introspection of all dependencies (down to the bootstrap toolchains used to build the bootstrap toolchains that build your toolchains, which is something that AFAIK no other package manager except for nix can do), has a fairly large package repository, straightforward locking, actually tested packages, and very easy build recipes (unlike conda-forge...).
If you are in a commercial environment, I can only warn to think that using alternative conda clients will be safe. Condaforge for instance will happily download from the main channel if the recipe requires it. It's pretty hard to make sure this does not happen, best solution is to block access on a network level.
Do you have an example for a package on conda-forge that actually does this? I can only find a vague announcement from 2021 that the "defaults channel is now dropped when building conda-forge packages", as well as statements that the conda-forge repositories are considered incompatible with the defaults channel and having both enabled is an unsupported configuration. Access is blocked on the network level anyway.
That can only happen if you as a user have the 'defaults' channel still configured as available, and conda-forge considers it a user error whenever this happens (the official line is `conda-forge is incompatible with the packages provided in defaults`). Many bug reports are closed simply by telling the user to fix their channel priorities and stop mixing the two
I use Miniforge in a commercial environment and never found a package downloading from the main channel. I'm pretty sure a recipe that does that would be blocked by conda-forge reviewers.
And how many people (count them with fingers) use Conda for anything other than Python? It’s a bloatware. People stopped using Conda because these people kept making the bloat worse.
A single one of the gfortran packages released about 20 hours ago already has 362 downloads, so I think it is safe to say that the non-python users of conda-forge are more than just a handful.
If you don't need it that's fine, no one is stopping you from using PyPI and uv instead. But for some that is not a replacement.
And yes, some things about conda(-forge) could be described as bloated. I particularly dislike the convoluted packaging process.
It is always tradeoffs and deciding based on your own use cases. E.g. if you want to distribute tested packages to users of your software then both conda-forge and PyPI are ill-suited for you. They (and most other package managers) do install-time dependency resolution, so the installation cannot be guaranteed to be tested as working at all. Some package managers do that better, so is conda-forge and PyPI obsolete now?
Heh, looks like they've just pivoted to calling what they do advancing AI and managed to mention AI enough times to get a big new valuation.
Seriously, if you've ever used them before, check out their website now. "Advance AI with Clarity and Confidence", "Simplify, safeguard, and accelerate AI value with open source.", "Millions Rely on Anaconda to Advance Their AI Initiatives"
What does any of that mean? No idea, seems like the actual product is the same conda.
Astral's uv is an impressive, ultra-fast Python package manager that’s rapidly clearing up the pip/virtualenv/poetry mess and setting a new bar for dx. However, Astral is a startup with an unclear path to monetization, whereas Anaconda is a mature company focused on enterprise AI/ML offerings and long-term customer relationships, with conda being just one part of their broader stack. It’s entirely plausible for Anaconda to adopt tools like uv. Comparing Astral and Anaconda directly overlooks their vastly different missions and scales—uv’s technical leap could help unify Python tooling, but Anaconda addresses a different problem.
I'm saying when you compare the resources that have gone in vs. the results that have come out the other side?
Putting these two teams up against one another isn't even fair: its like pushing baby chicks into a pond full of Piranhha fish.
It's not just a package manager: uv and ruff and tye are rapidly becoming an ecosystem. You think they don't have plans for Jupyter?
Google was a tiny company without a monetization strategy. Yahoo being gigantic and "divetsified" just made them a tastier meal for a different breed of competition.
I work with less technical users and the problem with UV is that the installation instructions are slightly more complicated.
For users that just want conda to download python + a bunch of packages and won't ever bother to create environments, anaconda will always be superior.
Now, if UV bundles with a "default python version" with an installer, that may change things.
the other use case is if your env requires compiled binaries, you can't do that with uv. i.e., the Intel MKL package is available on conda but not on pypi. We've also run into this with some cuda related packages.
Where's Anaconda at these days? I've not touched it since starting out with Python and don't think I've touched it since discovering other package managers.
Anyone here using it regularly in 2025, is there anything I'm missing out on?
Everywhere where they may be a shadow data science team. And that's how they get you. One has to be very careful to install the Open Source portions. They make no effort to make the boundary obvious. If one does not, IT will get a call asking for licensing money.
Can confirm, this is exactly what happened. They demand ridiculous back payments unless you buy multiple-year licenses. It would be trivial for them to require an account to use the main channel, but they deliberately make it easy to accidentally use it. If you have to use it, make sure to DNS block anaconda.com (.org seems to be fine), but even better, just avoid them like the plague.
Anaconda sells a managed environment for data science applications, right? Basically the Red Hat business model?
I've used conda for years and haven't set aside the day it'll take to switch to something simple and modern (uv's top of the list, but I'm open to suggestions)
use uv, you dont need to bin your existing installation (it just wont get used anymore) and it will take you 5 minutes to switch. next time you get new hardware you wont bother installing anything else python related.
Setting up a python environment for ML work (pytorch + Nvidia) is simpler with Anaconda, it's a pure dependency nightmare doing it with something else.
Counterpoint: conda is a constant, neverending source of pure dependency nightmares here, that consistently costs us a two-digit percentage of velocity. I'm glad it's working for you, but I really wouldn't generalize. Or recommend it to anyone getting into Python. Use uv, new people, for real.
Yeah, I remember finding that the case for a while. I can't remember when things clicked and it was fine without. I moved to arch a while back, perhaps then.
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Everyone who is comparing Anaconda and conda to Astral and uv is missing that the conda ecosystem is language-agnostic while uv is python specific. uv won't help you install gfortran, for example. It is not a replacement, unless you only do python (and use at most common non-python libraries that are available on PyPI).
On the other hand you don't have to use anything associated with Anaconda to use the conda ecosystem. Alternative package managers like mamba and pixi rely on the conda-forge channel instead. Pixi in particular (https://pixi.sh/) is sort-of the uv for the conda ecosystem workflow-wise, and works pretty well if you want that.
Still, if the concern is language-agnostic ways to use tooling, mise (nee rtx) is the 1000 pound gorilla in the room today. Incredibly fast well built Rust based tool that has really massively expanded in scope & offerings, with grace & elegance. I thought it was an asdf replacement, for installing/using toolchains, for .tool-versions files. But it's really grown to be a lot more, capable of letting you isolatedly manage tools it can install from a huge variety of backends (pip, npm, cargo, others). https://mise.jdx.dev/dev-tools/backends/
I'd rather see more adoption of guix for this purpose. It is a single package manager with a functional approach that allows for introspection of all dependencies (down to the bootstrap toolchains used to build the bootstrap toolchains that build your toolchains, which is something that AFAIK no other package manager except for nix can do), has a fairly large package repository, straightforward locking, actually tested packages, and very easy build recipes (unlike conda-forge...).
If you don't need it that's fine, no one is stopping you from using PyPI and uv instead. But for some that is not a replacement.
And yes, some things about conda(-forge) could be described as bloated. I particularly dislike the convoluted packaging process.
It is always tradeoffs and deciding based on your own use cases. E.g. if you want to distribute tested packages to users of your software then both conda-forge and PyPI are ill-suited for you. They (and most other package managers) do install-time dependency resolution, so the installation cannot be guaranteed to be tested as working at all. Some package managers do that better, so is conda-forge and PyPI obsolete now?
Seriously, if you've ever used them before, check out their website now. "Advance AI with Clarity and Confidence", "Simplify, safeguard, and accelerate AI value with open source.", "Millions Rely on Anaconda to Advance Their AI Initiatives"
What does any of that mean? No idea, seems like the actual product is the same conda.
Putting these two teams up against one another isn't even fair: its like pushing baby chicks into a pond full of Piranhha fish.
It's not just a package manager: uv and ruff and tye are rapidly becoming an ecosystem. You think they don't have plans for Jupyter?
Google was a tiny company without a monetization strategy. Yahoo being gigantic and "divetsified" just made them a tastier meal for a different breed of competition.
I work with less technical users and the problem with UV is that the installation instructions are slightly more complicated.
For users that just want conda to download python + a bunch of packages and won't ever bother to create environments, anaconda will always be superior.
Now, if UV bundles with a "default python version" with an installer, that may change things.
Deleted Comment
Anyone here using it regularly in 2025, is there anything I'm missing out on?
I've used conda for years and haven't set aside the day it'll take to switch to something simple and modern (uv's top of the list, but I'm open to suggestions)
Ahh makes sense now.
Slapping Ai somewhere, anywhere on every page of the pitch deck is the most effective endeavour of the 2020's