Product Spotlight: Atlas Charts — Tiny, Declarative Charts for Dashboards

Atlas Charts positions itself as a minimal, highly-performant charting toolkit for dashboard UIs. Its selling points are a compact core, a declarative API, and easy theming with CSS variables. In this spotlight we walk through practical integration scenarios, measure performance on large datasets, and show how to extend Atlas with custom renderers.

Core mental model

Atlas adopts a declarative data + config model. You supply a dataset and a chart configuration, and Atlas renders SVG or Canvas based on the complexity of the view. By default, Atlas chooses SVG for small-to-moderate datasets and falls back to Canvas for tens of thousands of points. This dual renderer helps maintain interactivity while scaling visual output.

Bundle size and tree-shaking

One of Atlas' strong suits is its modular build. Importing a single chart type pulls only the necessary code in most bundlers. On a simple line chart example, the minified gzipped footprint was under 8KB—competitive with the lightest charting solutions.

Integration and theming

Theming is straightforward: Atlas consumes CSS variables for colors, typography, and grid stroke widths. Teams can integrate it into existing design systems by mapping tokens to CSS variables globally. For React and Vue bindings, Atlas exposes small wrappers that pass props directly through to the render core.

Performance on large datasets

We benchmarked Atlas with an increasing series of dataset sizes. For up to 5k points, Canvas mode maintained interactive pan/zoom under 60 FPS on a mid-range laptop. For 50k points, Canvas rendering combined with simplified markers and sampled down to a 2–5% representation maintained acceptable performance while preserving trend fidelity.

Customization and plugin hooks

Atlas exposes hooks to register custom renderers and to intercept data transforms. This is helpful if you need a specialized drawing mode (e.g., WebGL for extremely large datasets) or want to apply server-side aggregation strategies before visualization.

When to choose Atlas

Atlas is a strong fit for analytics dashboards where bundle size, predictable theming and moderately large data are priorities. If you need a fully-featured plotting library with advanced statistical chart types out-of-the-box, a larger ecosystem library may be preferable. Atlas is best when you want a small, composable building block that integrates cleanly into a design system.

Pricing and licensing

Atlas is open-core: the rendering core is MIT licensed, and commercial features (advanced aggregators and an enterprise support lane) are available via paid plans. For most public dashboards the open-core edition is sufficient, but enterprise users may want the support SLA and extended aggregation capabilities.

Final thoughts

Atlas Charts fills a useful niche: a tiny, declarative chart core that scales gracefully into Canvas for larger datasets while keeping theming and integration simple. If your dashboard needs tight bundle budgets and smooth developer ergonomics, Atlas deserves a spot in your shortlist.