Deepnote vs Livedocs
Comparing cloud notebooks: reactive execution, AI agents, and performance.
Deepnote
A collaborative, cloud-based Jupyter notebook platform that lets teams write SQL and Python together, visualize data, and share results easily in a managed environment.
Livedocs
The AI-native notebook for data teams. Write Python and SQL, query any data, build interactive dashboards, and collaborate in real time. Powered by DuckDB, Polars, and AI — it's the fastest way to analyze, automate, and share insights.
Design & UX
Livedocs offers a modern, minimal, and aesthetically refined interface designed for storytelling, dashboards, and data exploration. Every component — from the code cells to the charts — feels crafted with attention to visual polish and clarity. The interface is distraction-free and makes notebooks feel like living documents or apps. Deepnote, by comparison, is highly functional but more utilitarian in design. It focuses on usability and collaboration, but the visual presentation can feel dense and less refined. For teams who care about the look and feel of what they present — whether to stakeholders or the public — Livedocs provides a superior experience out of the box.
Jupyter Compatibility
Both platforms are Jupyter-compatible, meaning you can import and export IPYNB notebooks seamlessly. Deepnote fully embraces the Jupyter ecosystem, offering a managed environment that feels familiar to anyone who’s used Jupyter before. Livedocs goes a step further: it not only maintains compatibility but enhances it with a reactive execution model. When you modify one cell, Livedocs automatically re-executes dependent cells, eliminating the messy re-run dance that plagues traditional notebooks. You still get all the same magics and Python syntax you expect, but with the consistency of a directed acyclic graph (DAG)-based runtime. It’s Jupyter, evolved.
AI Agent & Model Choice
Deepnote has recently introduced an AI assistant, but it’s largely limited to inline code suggestions and does not let users choose which underlying model powers it. The AI behaves like a smart autocomplete tool. Livedocs, on the other hand, is built from the ground up as an AI-native notebook. Its agent can write SQL, Python, or Markdown blocks, explain data, design visualizations, and automate repetitive workflows. Crucially, you can pick your preferred model — whether GPT-5, Claude Sonnet, Gemini, or others — and swap models per task, workspace, or organization. Livedocs’ AI has access to your notebook context (dataframes, tables, and charts), enabling more intelligent, context-aware reasoning that feels like a real AI data scientist working alongside you.
Sharing Speed & Experience
Sharing notebooks in Livedocs is nearly instantaneous. Static, read-only views of documents load extremely fast thanks to optimized rendering and CDN caching. Deepnote allows public and private sharing too, but its static views can feel heavier due to server-side rendering and environment bootstrapping. In Livedocs, sharing a notebook doesn’t mean spinning up a kernel — it simply serves a pre-rendered, responsive document. That makes it ideal for public demos, dashboards, and portfolio pieces. Whether you’re sharing a live analysis or embedding a static dashboard, Livedocs ensures your audience gets a smooth, instant experience.
Collaboration & Publishing Modes
Deepnote set the early standard for collaborative notebooks with real-time editing and Google Docs–style commenting. Livedocs builds on that foundation with an even broader set of sharing and publishing options. Multiple users can co-edit a document simultaneously, with real-time presence indicators and conflict-free syncing. Beyond simple collaboration, Livedocs allows you to publish notebooks in three distinct ways: as live collaborative documents, as interactive apps with input controls, or as static snapshots. You can also embed them directly into Notion, Coda, or any external site. This flexibility makes Livedocs a natural choice for teams that not only analyze data but also present and operationalize it.
Free Tier & Limits
Livedocs offers a generous free tier designed to help teams explore without friction. Every new account comes with $10 in free AI credits, unlimited documents, and unlimited published apps. Users can invite collaborators, build dashboards, and use all the major connectors with no upfront commitment. Deepnote also provides a free tier, but it comes with tighter limitations on team size, resource usage, and compute availability. In contrast, Livedocs focuses on giving users freedom to explore and create — the free plan is meant to show the full potential of the platform rather than lock key functionality behind paywalls.
Engines, Performance & File Query
Under the hood, Livedocs is powered by DuckDB and Polars, two of the fastest analytical engines available today. This means you can query massive CSVs or Parquet files locally without relying on an external data warehouse or heavy cloud runtime. Operations that might take several seconds in Deepnote — like filtering millions of rows — often complete in milliseconds in Livedocs. Deepnote, being a cloud-managed Jupyter environment, depends heavily on Python libraries and external warehouses for data processing, which introduces overhead and latency. For teams working with local data or large files, Livedocs offers a substantial performance advantage.
Visualizations
Deepnote supports standard chart blocks and Python visualization libraries such as Plotly, Seaborn, and Matplotlib. It’s flexible but minimal. Livedocs takes visualizations to another level: it includes a native chart builder powered by Vega-Lite and Altair, supports Polars DataFrames natively, and allows fully reactive visual dashboards. Every visualization can be styled and resized with intuitive controls, and charts update automatically when underlying data changes. This makes Livedocs particularly strong for data storytelling, where clarity and interactivity matter as much as analysis. You can build live dashboards and share them publicly without ever leaving the notebook.
Caching & Execution Strategies
Livedocs implements a smart caching layer that tracks dependencies between cells and only re-runs the parts of a document that actually changed. This drastically improves both performance and reliability. Users can explicitly invalidate or persist cache states and choose from multiple execution modes — reactive (auto-update), top-down (run sequentially), or manual (user-triggered). Deepnote uses a more traditional notebook execution model: each cell runs independently and must be re-run manually or in sequence. For complex data pipelines or reproducible analyses, Livedocs’ caching and execution control translate directly into time savings and fewer errors.
Workflow Primitives (KV Store & Scheduling)
Livedocs introduces a built-in key–value store that acts as a lightweight state manager for your notebooks. You can store intermediate results, manage tokens, or persist state between runs — all without relying on an external database or environment variables. It also supports scheduled executions, allowing you to refresh dashboards or run workflows on a timer. Deepnote does not currently include native scheduling or stateful primitives; similar functionality must be set up externally using scripts or integrations. This makes Livedocs more suitable for turning notebooks into lightweight, automated data apps or mini-ETL workflows.
Data Connectivity
Deepnote offers a strong suite of built-in connectors to popular data warehouses like BigQuery, Snowflake, Redshift, and Athena, as well as cloud storage like Google Drive, S3, and OneDrive. It’s well-suited for teams operating entirely in cloud ecosystems. Livedocs, while offering these same connectors, goes further by natively supporting local files (CSV, Parquet, Excel) through DuckDB and seamless integration with Google Sheets. It also supports SQL templating via Jinja and lets you dynamically reference variables across cells. Deepnote is excellent for predefined cloud connections, while Livedocs shines when flexibility, performance, and hybrid data access are required.
When to Choose Each
Choose **Livedocs** if you want an AI-native, visually polished, and reactive notebook designed for storytelling, analysis, and lightweight app development. It’s faster, more flexible, and more generous with free-tier resources. The built-in AI agent, model selection, and DuckDB-powered performance make it ideal for data scientists and analysts who want autonomy and speed. Choose **Deepnote** if your team is already embedded in the cloud ecosystem, heavily relies on managed integrations, and values simplicity over flexibility. Deepnote remains an excellent choice for cloud-native Jupyter users — but for those seeking next-generation interactivity and performance, Livedocs stands out clearly.
Compare All Data Platforms
See how Livedocs stacks up against all major data notebook and analysis platforms.
| Tool | Setup | Languages | Data | Visualization | Collaboration | AI Agent | Engine | Scheduling | Sharing | Terminal | Pricing |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Livedocs | Zero-setup | Python, SQL, AI | All major DBs + files | Native + Python | Realtime | Yes, choose model | DuckDB + Polars | Yes + KV/secrets | Live/static/embed | Yes | $0 + $10 AI credits |
| Deepnote | Managed | Python, SQL | Cloud connectors | Charts + Python | Realtime | Basic, no choice | Standard runtime | Limited | Notebook only | No | Free with limits |
| Hex | Managed | SQL, Python | Enterprise only | No-code + libs | Team only | Limited, no choice | Cloud only | Workarounds | Apps only | No | Expensive |
| Jupyter | Manual setup | Python only | Libraries only | Code-based | File/Git | No | Sequential | No | Files only | External | Free OSS |
| Julius | Managed | Chat only | Minimal | Basic | Single-user | Chat only | Limited | No | Ephemeral | No | N/A |
| Colab | Managed | Python only | Drive/manual | Code-based | Link share | Autocomplete | Ephemeral VMs | No | Link only | No | Free + limits |
| Databricks | Cluster-based | Python, SQL | In-platform | Basic + libs | Team only | No | Slow starts | Enterprise jobs | Notebook only | Limited | Expensive |
| Modal | Serverless | Python | Storage mounts | Code-based | Partial | No | GPU focus | No | Notebook only | Container | Pay-per-use |
| Observable | Managed | JavaScript | Browser/APIs | D3/JS elite | Realtime | No | Browser only | No | Embeds/static | No | Free + paid |
| ChatGPT | N/A | N/A | No connections | Descriptive | Chat share | Fixed model | No execution | No | Chat only | No | Subscription |
| VSCode | Local setup | Multi-language | Manual | Libraries | Git/PR | Copilot | Local kernel | No | Files | Yes | Free |
| Cursor | Local setup | Multi-language | Manual | None | Git/PR | Code agent | Local | No | Code | Yes | Subscription |
| Marimo | Local/DIY | Python | Local files | Widgets + libs | No | Limited | Reactive | No | App/read-only | Local | Free OSS |
| Power BI | Desktop/cloud | No-code + DAX | Broad | BI visuals | Workspace | Basic | Extracts | Report refresh | Reports/embeds | No | Per-user |
| Mode | Managed | SQL only | Warehouses | Report charts | Team share | No | Warehouse-bound | Scheduled | Dashboard embeds | No | Enterprise |
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