100% agreement, spot on observations! Indeed, we end up referring folks to cool diagramming tools all the time at Graphistry as d2/mermaid/diagrams.net are well-optimized for quick & beautiful diagram presentation tasks on manageably sized & fairly static datasets. Using Graphistry to do a quick markdown of how a 4 node cluster might work is a bit like driving a tank to pick up some milk :) Teams go to us more for gnarly investigation & splunking tasks that need a visual power tool, like looking at alert logs or big systems, so we optimize for making that scale interactive & easy.
The overlap is real however, so a lot of room for teams to learn. Ex: We're the easiest graph tool for jupyter/databricks/streamlit etc teams who use dataframes, and I can imagine those tools learning from us here. In the reverse direction, our work more in terms of quickly configurable global data<>viz data bindings ("using the UI or API, bind each event's score to a hot-and-cold coloring palette and use a warning icon on all type=alert events"), but we have a ways to go to support the more manual artisinal effects of diagramming tools like Figma, where each element might have a super fancy & unique border style.
The overlap is real however, so a lot of room for teams to learn. Ex: We're the easiest graph tool for jupyter/databricks/streamlit etc teams who use dataframes, and I can imagine those tools learning from us here. In the reverse direction, our work more in terms of quickly configurable global data<>viz data bindings ("using the UI or API, bind each event's score to a hot-and-cold coloring palette and use a warning icon on all type=alert events"), but we have a ways to go to support the more manual artisinal effects of diagramming tools like Figma, where each element might have a super fancy & unique border style.