Top JS DataTables in 2025: Performance Comparison

We decided to do a bit of research to check out how things are going with big data management solutions represented on the market, to be more precise — to study the list of most popular JavaScript data tables, choose some of the options and test them. Spoiler alert: we’ve received fairly interesting results, especially since Webix has its own in-house complex widget for handling huge amounts of data. Let’s have a look at how the Webix JavaScript DataTable performs compared to other popular solutions.

Top JavaScript DataTables performance comparison

We are sure you are well aware of what a data table is, so let’s meet the participants of our analysis. They are popular JavaScript grids known as modern and powerful tools for big data management (just hold on a bit, we are going to check their performance very soon): Webix JavaScript DataTable, AG Grid, Kendo jQuery Data Grid and Bryntum Grid.

Kendo jQuery Data Grid

An advanced grid that covers a gamut of features, including adaptive rendering, column interaction, aggregates based on the selected data, rows and columns spanning, virtualization, keyboard navigation, in addition to basic grid functionalities.

Webix JavaScript DataTable

A powerful JavaScript widget suitable for working with BigData, equipped with the full range of features for handling data sets of varying complexity: sorting, paging, filtering, validation, drag-n-drop between tables, clipboard support, etc. The Webix JavaScript DataTable has a responsive UI and provides a number of advanced functionalities, including area selection, grid grouping, sparklines, advanced editor, subrows and subviews.

AG Grid

An interactive table for large data sets capable of filtering, sorting, resizing, selecting, editing data, etc. Supports a number of frameworks, including Angular, React and Vue. Provides the users with wide customization opportunities.

Bryntum Grid

A professional tool that can be integrated with Vue, Angular, React or Salesforce. The list of the features includes, but is not limited to locked columns, cell editing, custom cell rendering, rows and headers grouping, filtering and sorting.

It is worth noting that the choice of the JS grids was not accidental. On the one hand, the above-mentioned tables identify themselves as high-performance solutions complying with modern standards for big data handling. On the other — they are widely known for their quality and have quite a number of loyal customers proving they can efficiently manage complex matters associated with large data sets manipulations.

We are going to have two rounds of the performance competition evaluating the results of the participants and unveiling the winner of the former and the latter. We called them “The Rows Round” and “The Columns Round” as we check how the grids tackle the increasing numbers of rows and columns. We identified the subject of our research as grid performance rates in big data handling.

Can’t wait to see the results? We are well on the way to checking them out, but need to specify the conditions first.

Conditions

To carry out the comparison in an impartial way we will observe the following conditions:

• use default and simple setting for all the grids (we will only define the size of the columns, by the way, the same for all the participants);
• generate local data (the same data set) and measure JavaScript table initialization speed;
• use timestamps of the performance.now() method for evaluating performance;
• make the measurements in the same browser and on the same device (Chrome web browser, 16GB RAM device).

The Rows Round: How About Handling Millions of Rows?

We populated the tables with records, gradually making the data set bigger and bigger reaching the final point of 1,000,000 rows. If you look at the details in the chart below you will see that for some participants growing data volumes mean increased rendering time and lower performance, while some of them are almost insensitive (in terms of rendering time) to the size of the incoming data.

But let’s look more closely at the details: as you can see, all the JS data tables have a good showing when handling small to medium-sized data sets. The figures differ, but this difference cannot be detected by a human eye, which means no delays in rendering.

But as the number of rows is coming closer and closer to the final estimation point of one million rows things change dramatically — while Webix and Kendo continue showing consistently good results, AG Grid rendering time goes up after it passes the 100,000 mark, Bryntum becomes slower and slower with lags perceptible to the eye even sooner.

Table 1. Data table initialization time (rows), ms.

Here is the ranking of the participants in accordance with the rows rendering time:

Number #1 — Webix DataTable with the best performance;

Number #2 — Kendo jQuery DataGrid with consistently good results and just a bit behind Webix;

Number #3 — AG Grid with good performance up to the 100,000 mark, but with delays when the mark is passed.

Number #4 — Bryntum Grid with good results related to small-sized data sets, but with the rendering time rapidly going up with medium-sized and large ones.

The Columns Round: How About Managing Thousands of Columns?

In the second round, we measured the participants’ capabilities to successfully handle big numbers of columns: starting again with small data sets and progressing to the final mark of 1,000 columns.

You can find the outcomes of the Columns Round in the Chart below.

Here we have the following results: three out of four participants — Webix, AG Grid and Kendo — successfully managed data sets of varying size, while Bryntum could not make a good showing operating with noticeable lags in the case of medium-sized to large ones.

Table 2. Data table initialization time (columns), ms.

Thus, the ranking of the participants in terms of columns rendering time is as follows:

Number #1 — Webix DataTable with the invariably excellent rates irrespective of the data set size;

Number #2 — AG Grid with very good performance and very close to Webix, but still a bit behind;

Number #3 — Kendo jQuery Data Grid with very good results for small data sets, but left behind by Webix and AG Grid when populated with a bigger number of columns;

Number #4 — Bryntum Grid with a significant downturn in performance as the number of columns rises.

Webix JavaScript DataTable: Revealing the Winning Formula

Is it unexpected that the Webix JavaScript DataTable outperformed its rivals in the competition? Not really. It comes as no surprise to us.

The key to success in the case of Webix is the innovative approach to data rendering that ensures exceptionally high performance irrespective of the size of the data set your application needs to manage. Let’s consider some details to understand how it works.

Webix JS DataTable uses a lazy drawing strategy for rendering both rows and columns, which means that only visible elements are included into the DOM, so the amount of items rendered is limited and always the same. This helps to avoid performance issues and ensures the same rendering time, whatever the size of the data set. You do not have to worry about the amount of information your table needs to process and it becomes simply impossible to overload your grid.

On top of that, the widget has an in-built mechanism (available by default) to get around browser limits (the size of the browser container is not endless): the issue is resolved by rendering a certain amount of data and a scroll UI separately.

The above-mentioned techniques are an integral part of the Webix DataTable enhanced logic ensuring the flawless functioning of the data grid: no worries about the data amount, complex technical details or browser limitations.

You can find detailed information about Webix strategies for designing highly-efficient grids in the article Webix JS DataTable Best-in-class Performance.

Conclusion

Our research illustrates the way modern JavaScript grids cope with big amounts of information: most of them do a good job of handling large data sets providing high performance and outstanding rendering speed, while some are not yet able to demonstrate the same prominent results. Anyway, today we have a wide variety of top-quality tools to fit every customer’s taste — just choose whatever is best-suited for your business app. You can use this article as a starting point of your personal study on the benefits and drawbacks of the existing instruments adding the most innovative and efficient of them on your list — now you are definitely aware of the things to focus on when making a decision.