Each tool has advantages and disadvantages.
I appreciate the wide variety of visualizations offered through Voyant (word clouds, bubbles and bubblelines, corpus grid and summary). You are spoiled for choice in how to best visually represent your data. Even through you can share these visualization, such as embedding them on a website, there a a clear downside that you cannot create an account. For example, I can easily go back into Carto and retrieve the visualizations I already created based on the data I uploaded previously. I can then continue to work with this data set, tweaking where needed, and play around with the options. With Voyant, I often find myself having to start all over each time I use it.
Looking more closely at Carto, being able to create an account is a definitely advantage to being able to set up an account. As mentioned, you can create your maps and then go back to them later without starting from scratch. It is also helpful that you can create an account using your existing Google or Github accounts. As a start-up company, Carto made a smart decision in making account option easy to use. However, users may find that they prefer using Google maps or Google fusion for represent information in a similar way. This is where Carto’s fee accounts can offer greater flexibility and storage as an advantage over Google.
Palladio presents an similar issue to Voyant in that there is not account creation. However, options for how to represent your data are numerous. The exercises we did for Palladio show the enormous options for representing the same data set in slightly different ways. It allows scholars to ask and answer a variety of research questions and share those answers with their peers. However, some of the networking visualizations were overwhelming and difficult to read because of the number of nodes. This is where I appreciate the option in Google fusion to de-select certain nodes within a group. However, it seems like Palladio is still more robust in terms of how much it can handle since Google fusion usually cuts me off at a much lower number than the nodes that are present.
Depending on the research questions being asked, all of these tools offer information ways to visualize and share data. I can see how some of these questions would require a combination of these tools.