Angular has changed in recent versions, especially around how developers handle reactive state. Signals are now an important part of the framework, and that makes the architecture behind an Angular component worth paying attention to.
A grid can start as a simple list of rows and columns. Then requirements start piling up: sorting, filtering, editing, large datasets, virtualization, grouping, pivot tables, accessibility, theming, and more. Building all of those capabilities around a basic table can become a project of its own.
That's where Angular DataGrid comes in. It's an open-source, MIT-licensed Angular data grid built with Angular, TypeScript, and Angular CDK, with a Signals-based architecture and a separate paid Enterprise tier for advanced spreadsheet and data-governance features.
In this article, I'll go through what Angular DataGrid offers, how to get started with it, which features matter when you're dealing with large datasets, how its free and Enterprise tiers differ, and how it compares with AG Grid.
Angular DataGrid is a free, MIT-licensed Angular data grid built around modern Angular patterns, including Signals, with Angular CDK powering its virtualization layer. The project currently reports 100k+ rows supported, 25+ built-in features, 116 passing tests, and $0 license cost for the open-source tier.
✅ MIT-licensed open-source core ✅ Signals-based architecture ✅ Virtual scrolling for large datasets ✅ Sorting, filtering, pagination, and quick search ✅ Row selection and inline editing ✅ Column resizing, reordering, pinning, and layout persistence ✅ Row grouping, aggregation, and Tree Data ✅ Basic Master/Detail ✅ Pivot tables and integrated charts ✅ Faceted search and live updates ✅ Context menus, tooltips, overlays, and cell/row styling ✅ Light, dark, and high-contrast themes ✅ Keyboard navigation and ARIA grid semantics ✅ CSV export ✅ No separate CSS import required
There is also an optional Enterprise tier for capabilities such as Formula Engine, Undo/Redo, Range Selection, Fill Handle, Server-Side Row Model, Cell Permissions, Audit Trail, Row Locking, Spreadsheet Import, PDF Export, Saved Views, and Form Editor.
So if you're looking for an Angular data grid that gives you a broad feature set without a commercial license for the core functionality, Angular DataGrid is worth evaluating.
An Angular data grid component is a table-like UI component designed for large, interactive datasets. It typically handles features such as sorting, filtering, pagination, inline editing, row selection, column management, and data visualization as part of the grid itself.
A basic Angular table can work perfectly well for a small dataset. Once the number of rows grows, though, the browser has to deal with a much larger DOM, and developers have to build more of the interaction layer themselves.
There is no built-in virtualization in a plain table. Sorting, filtering, editing, selection, column resizing, keyboard navigation, and other behaviors also need to be implemented separately.
For applications such as admin dashboards, inventory systems, analytics tools, CRM interfaces, financial applications, and internal data platforms, that can make a big difference in how much grid-specific code the application needs to maintain.
The important distinction is that a data grid isn't simply a prettier table. It's an interactive data-management component designed around the problems that appear when users need to work with a lot of structured information.
The features need to solve actual problems that appear when applications start working with structured and growing datasets.
When evaluating an Angular data grid, I'd look at five areas first: performance, data operations, hierarchical data, accessibility, and customization. These are also the areas highlighted in the Angular DataGrid brief.
The first thing to consider is how the grid behaves when the number of records grows.
Rendering a few dozen rows is easy. Rendering thousands of rows creates a very different workload for the browser.
A virtualized grid can keep the number of DOM elements under control by rendering the portion of the dataset that needs to be visible.