I am a data scientist and use Anaconda Platform for all my work on my local machine. It's a great all-in-one location where I can use Spyder, Jupyter notebooks, and more—all in the same place. I love that everything is downloaded together at once; I don't need to download and maintain separate versions of Python, Spyder, etc. It's easy to set up, with steps to guide you through. Overall, it's a great tool for data science and analytics. Review collected by and hosted on G2.com.

I'm not sure if it exists, but there should be cloud compute support for Anaconda. I choose to do my work in the cloud so that I can deploy a GPU if needed, since my local doesn't have that. That would really make Anaconda more powerful and more useful. Review collected by and hosted on G2.com.

I find the Anaconda AI Platform very easy to use for data science workflows, which is great for both learning and experimenting. Its enterprise pricing plans are quite affordable and ideal for my needs. I appreciate that it has integrated tooling, notebooks, package managers, interpreters, and all the main libraries already in place, making it very useful for building workflows and practicing skills. It's also accessible for beginners and students who are learning data science and AI. Review collected by and hosted on G2.com.

Yes. The interface could be made more user friendly because right now it's a bit hard to get used to at first. And, also, it's very heavy and resource intensive, I would say. And the free tier is limited as well. So that could be improved. Those problems could be solved. Review collected by and hosted on G2.com.

Anaconda has been very useful in resolving some of the current issues that were accruing in the environment setup, dependency management, and other tool-related issues. Previously, setting up the environment has been tedious; however, with Anaconda, this has been made very easy, allowing me to concentrate on data analysis rather than constantly fighting with setup issues. Review collected by and hosted on G2.com.

There is something that I dislike about Anaconda, however. One feature that I dislike is that sometimes the site can be a bit heavy, especially at times of startup or updating. Conflicts in packages still occur, albeit rarely. At times, resolving these could take extra time. The variety of features could be overwhelming to new users. Review collected by and hosted on G2.com.

As a AI developer I have used Anaconda for some of my projects and the feature I most like about it are:-

1) Good for projects with shared environment

2) Bundled important python libraries for AI development and Data science which really makes it easy to use

3)Strong open source community to clear doubts

4) Interactive platform for user support and new updates along with the forum

5) Integration with jupyter lab makes it more useful for AI developers Review collected by and hosted on G2.com.

The features I dislike are:-

1) It uses too much resources for some project which makes it hard to use on local machine

2) Bundled package are not latest sometimes. which makes it difficult to use new features Review collected by and hosted on G2.com.

The batteries included approach is the biggest advantage. I find it very hepful that it pre-installs the most critical data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib) without needing to fight with dependency conflicts manually. The Anaconda Navigator is great for managing separate environment, Eg- I can keep my deep learning environment separate from my standard stock analysis environment. Review collected by and hosted on G2.com.

It can be quite heavy on system resources. The base installation consumes a significant amount of disk space. Also the Navigator GUI can sometimes feel sluggish to load on older hardware. The 'Solver' (used for resolving package dependencies) can occasionally be slow when installing new packages. Review collected by and hosted on G2.com.

1) Anaconda gives immediate access to a large, vetted collection of open source packages commonly used in data analysis, machine learning, data science and AI.

That means you don’t have to manually install, and configure everything.

2) Through its package/environment manager (Conda), Anaconda makes it easy to create isolated environments per project, control package versions, avoid dependency conflicts, and ensure reproducibility across machines.

3) Because many data-science and ML libraries come pre-packaged or easily installable, you can start coding, data-analysis, or model prototyping almost immediately without spending a lot of time on installation and setup. This is especially useful when dealing with data preprocessing, exploratory analysis, or quick experiments.

4) Ease of Use is generally strong: I find Anaconda easy to use and its interface intuitive, especially for data-science work.

5) Ease of Implementation is good: It bundles many common libraries and tools, so setup is quick and you can start working quickly (no need to install everything manually).

6) Customer Support is OK but not exceptional: Support and documentation exist; community support and docs are often used.

7) Frequency of Use is high: I use Anaconda daily for data analysis, ML, or research work especially because of convenience of environments, libraries, and notebooks.

8) Number of Features is very good: It offers a wide set of pre-installed libraries, supports multiple languages, IDEs/notebooks, environment management, covering most data-science needs.

9) Ease of Integration is solid: I see that Anaconda integrates well with data-science tools, notebooks, and other workflows, allowing smooth setup of environments and dependencies. Review collected by and hosted on G2.com.

1) Not always the most up-to-date or flexible package versions.

Because Anaconda uses curated repositories, sometimes the “latest” packages are not immediately available which might matter if you rely on the newest features or fixes.

2)Large installation size.

Anaconda tends to install a big bundle of packages by default. That consumes a lot of disk space and can lead to heavy RAM/CPU usage, sometimes noticeably slowing down even light tasks. Review collected by and hosted on G2.com.

My journey with Anaconda started in a very unusual way — through a recommendation from an Instagram influencer who promoted it as the best entry point for beginners. I was skeptical, but I decided to give it a try. What I truly love is that Anaconda bridges the gap between a complete novice and the complex world of programming. I remember my first experience: unlike other tools where I got stuck on installation errors, Anaconda worked perfectly on the first try. That feeling of immediate success was incredibly empowering. Even though my job isn't 100% coding-related, I recently received a newsletter from Anaconda that actually worked — it brought back those warm memories of my first steps. It motivated me to return to "vibecoding" using their platform because it feels safe, inclusive, and hassle-free. Review collected by and hosted on G2.com.

or a "vibecoder" or a hobbyist like me, it is hard to find real flaws. However, if I must be objective, the Anaconda Navigator can sometimes be a bit heavy and slow to launch on older machines compared to lightweight code editors. But considering it carries the entire ecosystem inside, I accept this trade-off for the convenience it provides. Review collected by and hosted on G2.com.

I love how the Anaconda AI Platform allows me to seamlessly swap between different runtimes that involve multiple packages, which would typically conflict with each other. This is incredibly valuable for my AI projects, such as when I need to work with object detection and large language model packages simultaneously. I appreciate the ability to save my project runtimes and run them on any device, which provides unparalleled flexibility and ease of access. Swapping between projects with just a single click is a feature that enhances my workflow significantly. Furthermore, I find assembling environments without running into package compatibility issues to be a significant advantage that simplifies project setup and execution. The initial setup of Anaconda AI Platform was pretty easy, which made it hassle-free to start my projects. Overall, these features collectively enhance my productivity and streamline complex AI project workflows. Review collected by and hosted on G2.com.

I sometimes find that the Navigator UI doesn't work as intended, which forces me to access features through other means. Review collected by and hosted on G2.com.

I love that Anaconda AI Platform makes the user interface seamless and legible, especially for new users like me. It's a great platform for following what I'm being taught in data analysis courses on LinkedIn Learning. I find the visual clarity it provides invaluable as a beginner, helping me follow through and learn each step. The setup is amazing with a step-by-step guide and lots of materials available on the platform to help with any issues. I also appreciate the seamless user journey without any problems. Review collected by and hosted on G2.com.

I don't know if I'm saying the right thing, but there could be the option for different language functionality. For someone like me who speaks other languages, and other people in places like Africa, I believe that with other indigenous languages available, it would help people who don't understand English because obviously the default language is English. Review collected by and hosted on G2.com.

I find Anaconda AI Platform really helpful for machine learning and deep learning projects because it has a built-in interpreter with most useful packages like numpy, pandas, scikit-learn, PyTorch, and TensorFlow, so I don't have to manually install them. It also has all the IDEs accessible from its platform, making it easier to access, manage, and use. Installing new Python packages in the interpreter is easy through the Anaconda terminal. I like how the user interface is easy to navigate, which is very convenient for a beginner coder like me. This helps me as a beginner in the machine learning and deep learning domain to use different Python packages and access a variety of different IDEs. The initial setup was also very easy. Review collected by and hosted on G2.com.

I don't like that it takes too long to load sometimes, and installing new packages in Anaconda terminal may need different input commands than regular Python, which can be confusing for some. Review collected by and hosted on G2.com.