Atlas vs Kepler.gl

Atlas and Kepler.gl both visualize geospatial data in the browser, but they approach the problem from different angles. This guide compares the two so you can decide whether you need a collaborative GIS platform or a high-performance open-source visualization engine.

Introducing Atlas and Kepler.gl

Atlas

Atlas is a browser-based collaborative GIS platform for teams that need to go beyond visualization. It combines data uploads, spatial analysis (buffers, heatmaps, spatial joins), real-time collaboration, and a no-code app builder into a single cloud workspace. Atlas is built for users who want to turn raw data into shareable maps and interactive applications without writing code or installing desktop software.

Kepler.gl

Kepler.gl is an open-source geospatial analysis tool created by Uber's visualization team. It is designed to render large datasets—millions of points—with GPU-accelerated WebGL layers including arcs, hexbins, heatmaps, and trip animations. Kepler.gl runs as a client-side web app or a React component, and is popular with data scientists and analysts who need powerful exploratory visualization of big geospatial datasets.

Quick Comparison Table

Visualization and Rendering

Atlas

Atlas offers a range of visualization styles including point maps, choropleth maps, heatmaps, cluster maps, and custom marker icons. Styling is handled through a visual UI—pick colors, set size scales, and configure pop-ups without code. Atlas is optimized for clarity and collaboration rather than raw rendering throughput.

• Pros: No-code styling, multiple visualization types, easy to share results
• Cons: Not optimized for rendering millions of points in a single view

Kepler.gl

Kepler.gl's core strength is high-performance visualization. It uses deck.gl under the hood to render arcs, hexbins, 3D columns, heatmaps, trip animations, and point clouds at scale. If you have a dataset with hundreds of thousands or millions of rows, Kepler.gl handles it smoothly. The visual configuration panel lets you experiment with layer types, filters, and color scales interactively.

• Pros: GPU-accelerated rendering, handles massive datasets, rich layer types (arcs, trips, 3D)
• Cons: Styling options are powerful but have a steeper learning curve

Which to Choose?

Choose Kepler.gl if you need to visualize very large datasets with advanced layer types like trip animations or 3D hexbins. Choose Atlas if you need collaborative, shareable maps with straightforward styling and your datasets are moderate in size.

Collaboration and Sharing

Atlas

Collaboration is central to Atlas. Multiple users can edit the same map simultaneously, leave comments, and track changes. Role-based permissions (viewer, editor, admin) let organizations control access. Published maps and apps are accessible via URL or embed code, making it easy to distribute work to stakeholders who do not have an account.

• Pros: Real-time co-editing, role-based access, embeddable outputs
• Cons: Requires accounts for editors; viewers can access via public link

Kepler.gl

Kepler.gl is a single-user tool. Your data and map configuration live in the browser session. To share your work, you export a JSON configuration file (and optionally the data) that someone else can re-import. There are no accounts, permissions, or real-time collaboration. For developers, Kepler.gl can be embedded as a React component, but this requires coding.

• Pros: No accounts needed, fully local and private by default
• Cons: No real-time collaboration, sharing requires manual file exchange

Which to Choose?

Choose Atlas if multiple people need to work on the same map or if you need to share live, always-up-to-date maps with stakeholders. Choose Kepler.gl if you work solo and privacy or local-only data handling is a priority.

Spatial Analysis

Atlas

Atlas includes built-in spatial analysis tools: buffer generation, spatial joins, heatmaps, attribute-based filtering, geocoding, and measurement tools. These run server-side in the cloud, so results are saved and shareable. For many teams, Atlas replaces the need for desktop GIS for common analytical tasks.

• Pros: Buffers, spatial joins, geocoding, and filtering—all in-browser
• Cons: Not suited for highly custom or scripted geoprocessing workflows

Kepler.gl

Kepler.gl provides aggregation-based analysis through hexbin, grid, and heatmap layers, plus time-series filtering and cross-filtering between layers. However, it does not perform spatial operations like buffer generation or spatial joins. For those tasks, users typically pre-process data in Python (with GeoPandas or similar) and then load the results into Kepler.gl.

• Pros: Powerful aggregation layers, time-series filtering
• Cons: No spatial joins, buffers, or geocoding—analysis must happen externally

Which to Choose?

Pick Atlas if you need spatial analysis operations built into the platform. Pick Kepler.gl if aggregation-style visualization is enough and you handle analysis in code.

App Building and Workflow

Atlas

Atlas goes beyond visualization with a no-code app builder. You can create applications that include interactive filters, search bars, data entry forms, charts, and dashboard panels—all connected to your spatial data. Published apps let external stakeholders explore data on their own terms without needing GIS expertise.

• Pros: Full no-code app builder, forms for data entry, shareable dashboards
• Cons: Complex apps require time to configure

Kepler.gl

Kepler.gl does not have an app-building layer. Developers can embed Kepler.gl as a React component inside a custom application, but this requires JavaScript development. There are no forms, dashboards, or interactive app features out of the box.

• Pros: Embeddable as a React component for developers
• Cons: No no-code app builder, no forms or dashboards without custom development

Which to Choose?

Choose Atlas if you want to turn your data into an interactive app without writing code. Choose Kepler.gl if you are a developer who wants to embed a powerful visualization component into a custom-built application.

Data Persistence and Infrastructure

Atlas

Atlas stores your data and maps in the cloud. Projects are always accessible from any device with a browser, and updates sync automatically. Backups, uptime, and infrastructure are managed by the Atlas team, so there is nothing to host or maintain.

• Pros: Cloud-hosted, always accessible, no infrastructure to manage
• Cons: Data lives on Atlas servers—may not suit strict data-residency requirements

Kepler.gl

Kepler.gl is fully client-side. Your data loads into the browser and never leaves your machine (unless you use kepler.gl's cloud export). This makes it appealing for sensitive data, but it also means there is no persistence—close the tab and your work is gone unless you saved the configuration. Self-hosting the app requires some DevOps effort.

• Pros: Data stays local, no vendor dependency, fully open-source
• Cons: No persistence without manual export, self-hosting requires effort

Which to Choose?

Choose Atlas for managed cloud infrastructure with automatic persistence. Choose Kepler.gl if local-only data handling is critical or you want full control over the stack.

Final Thoughts

Choose Atlas if you:

• Need real-time collaboration with role-based permissions
• Want built-in spatial analysis (buffers, joins, geocoding) without code
• Need to build and share interactive apps and dashboards
• Prefer managed cloud infrastructure with no setup
• Work with teams that include non-technical stakeholders

Choose Kepler.gl if you:

• Need to visualize very large datasets (millions of rows) with GPU acceleration
• Want advanced layer types like arc maps, trip animations, and 3D hexbins
• Prefer open-source software with no vendor lock-in
• Need data to stay entirely local and private
• Are a developer comfortable embedding visualization into custom apps

For a feature checklist and FAQs, see the Kepler.gl alternative page.