Tyler Von Harz, Community Partner
Feb 10, 2026
If you’re using the Deepnote app today, there’s a good chance it solved a real problem for you. Maybe you wanted something more collaborative than Jupyter, more flexible than BI dashboards, and definitely less brittle than scripts you glued together with exports. Deepnote delivered on that promise, at least at first.
As data work becomes more collaborative and more time-sensitive, the constraints matter more. Analysts need faster iteration. Stakeholders need clearer explanations. Teams need to explore data together without worrying about execution order, hidden state, or breaking someone else’s work.
That’s where a different approach starts to make sense. Instead of asking which notebook is best, it’s worth asking whether notebooks are still the right abstraction at all. This article explores why notebook-based workflows hit a ceiling, how Deepnote compares to its competitors, and why replacing notebooks entirely may be the more intuitive and scalable move for modern data teams.
What Deepnote gets right and where notebooks still break down
Deepnote is popular because it solves the practical problems that make traditional notebooks painful in a team setting. It’s cloud-native, link-shareable, and built around collaborative workspaces, so you don’t need to worry much about fighting local environments or dependency mismatches.
Deepnote also leans into the reality of modern analytics: teams want to move between SQL and Python (and sometimes no-code charts) without context-switching into separate tools, then share the result with teammates as a living document instead of a static export.
Structural limitations of notebook-based workflows
Deepnote improves the notebook experience, but it can’t escape the notebook’s underlying execution model. That model is built around a stateful kernel and linear cell execution, an approach that starts to crack as soon as more than one person relies on the work.
Imagine a shared Deepnote notebook used for weekly revenue analysis. An analyst tweaks a SQL query near the top, reruns a few downstream cells, and the charts look right. Later, a teammate opens the same notebook, runs a different cell first, and gets slightly different results. Nothing “broke,” but the output now depends on execution order and in-memory state, not just the code on the page. That hidden state is a well-known property of notebook environments, and it makes reproducibility fragile even in collaborative tools.
Iteration compounds the issue. Any change, small or large, often means re-running cells, waiting for outputs to re-render, and sanity-checking upstream logic. Deepnote reduces friction around sharing and compute, but it can’t remove the stop-start nature of stateful execution.
Collaboration still tends to happen one step at a time. Multiple people can edit the same notebook, but understanding why results changed often means piecing together what was run, and in what order. Reviews spill into chat threads and meetings, and true parallel work is harder than it looks because logic and state are tightly intertwined.
For non-technical teammates, the gap widens further. They see charts and tables, but the reasoning lives in code cells and execution context that’s easy to misinterpret without running, or re-running, the notebook themselves.
These limitations aren’t unique to Deepnote. They show up across notebook tools (Deepnote competitors included) because they’re properties of the notebook paradigm itself, not any one implementation.
Why do notebooks struggle with mixed technical and business teams?
Notebook tools work best when the same person both writes the analysis and interprets the result. Problems emerge when those roles split, which is exactly what happens in most real teams.
There is a collaboration mismatch.
In practice, analysts write code while stakeholders consume outcomes. Notebooks blur this boundary rather than clarifying it. Code, commentary, and results live in the same document, but they serve different audiences. Reviews often happen outside the notebook, in meetings, chat threads, or follow-up documents. The notebook itself doesn’t make intent, assumptions, or changes easy to track for everyone involved.
Then there is the cognitive overhead.
Even for technically fluent readers, notebooks demand a lot of mental bookkeeping. Understanding an output often requires reconstructing execution order, knowing which cells were rerun, and inferring context that isn’t explicitly modeled anywhere. Small changes can invalidate downstream results, forcing reruns and rechecks. The logic doesn’t live in a shared, stable structure.
Replacing notebooks with a collaborative, AI-native spreadsheet
These challenges have pushed some teams to move away from linear, stateful execution altogether. Instead of asking users to manage run order and hidden state, newer environments adopt a reactive model where data, logic, and outputs stay synchronized by default.
In a live, spreadsheet-style system, computation is continuous rather than episodic. There’s no “run all,” no stale outputs, and no ambiguity about which results reflect the current logic. Changes propagate immediately, reducing cognitive overhead and making collaboration more natural.
Tools like Quadratic represent this shift. Rather than extending notebooks, they combine SQL, Python, formulas, and AI in a single reactive workspace where logic sits alongside data and results. The important point is the underlying model. When execution order disappears, many of the collaboration, explainability, and handoff problems associated with notebooks disappear with it.
How Quadratic replaces the Deepnote workflow entirely
When the core assumption of a workflow changes, the experience of doing that work changes too. Deepnote and other notebook tools inherit their model from interactive coding: discrete execution steps, mutable state, and implicit dependencies. That works well for exploration, but it also means teams spend a lot of time managing how they work, not just what they work on.
In contrast, modern environments such as Quadratic adopt a reactive, continuous computation model where logic, data, and results live together in real time. In practice, this leads to a few notable differences in how analysis feels day to day:
Collaboration means a shared state, not shared files
Instead of coordinating edits around execution order or remembering which cell someone last ran, teams work in a shared live space. Edits are immediately visible, and context stays with the content. No need to pause, rerun, or revalidate. You and your team see current results without chasing hidden state or kernel history. Real-time shared workspaces like this also align more naturally with collaborative analytics workflows described in the research on collaborative data tools.
Tighter feedback loops with instant visuals and data
When visuals update automatically alongside logic and data, the feedback loop changes. Rather than rerunning groups of cells to propagate changes, results refresh as you work. Querying live data sources (like production databases) directly into the analysis reduces intermediate steps and the risk of drift between data and insights that can happen in notebook workflows.
AI assistance that lives in the context of the work
AI has become a standard part of many analytics toolchains, but its role varies. Some integrations act as sidebars or separate query interfaces; others generate standalone outputs that require manual integration back into your workflow. AI can help interpret your existing logic, suggest refinements, or generate new analytic expressions in place, to help bridge the gap between exploratory and explainable analytics.
The best Deepnote alternative may be AI-native
Deepnote represents genuine progress. It brings notebooks into the cloud, makes collaboration easier, and lowers the barrier for teams that want to work with SQL and Python together. For exploratory analysis and technical workflows, it’s a meaningful upgrade over local Jupyter and ad-hoc scripts.
But as analytics becomes more collaborative, more continuous, and more tightly connected to live data, the limits of the notebook model become harder to ignore. Linear execution, hidden state, and code-centric context introduce friction precisely where teams need shared understanding and fast iteration. AI can assist with writing cells, but it can’t resolve the structural constraints baked into notebook workflows.
The next shift is about moving beyond notebooks entirely. Reactive, AI-native environments change how analysis works by keeping data, logic, and reasoning synchronized in real time. When execution order disappears and context stays intact, insights don’t have to survive reruns, rewrites, or handoffs.
Quadratic offers a practical glimpse of what analytics looks like after notebooks. If Deepnote feels like the most refined version of the notebook era, Quadratic is a straightforward way to experience what comes next.
