Software advice
Best AI Data Notebook Software in 2026
AI data notebook software is a cell-based environment where analysts write SQL or Python, see results beside the code and share the output as a report or app, with an AI assistant that drafts queries, fixes errors or answers questions in plain language. It is not a business intelligence suite or a model-training platform, although notebooks feed both.
Quick answer
Hex ranks first because its plan prices, per-minute compute billing and SOC 2 Type II report are printed. Deepnote is the pick for a free team start with fixed AI credits, and Jupyter fits anyone who wants open source they run themselves.
A data team picking a notebook usually starts from a pile of ad hoc scripts, a warehouse login and a stakeholder who wants an answer by Friday. Vendors now pitch agents that write the analysis for you, yet their pages say far less about what that costs, where the code runs and who can read your queries. Some tools sold here are free open source projects with no vendor to ask, and others publish only a contact form. This ranking weighs published prices, stated AI limits, security statements and how much the notebook file can leave with you. We read vendor pages in November 2026 and ran no tool ourselves.
The 11 best AI data notebook software in 2026, ranked
There is no score. Vendors can pay for more visibility on this website, for example a higher place in a ranking or more room on a page. Commercial relationships can also affect which products we feature and how prominently. How we rank.
| # | Tool | Best for | Free plan | Entry price |
|---|---|---|---|---|
| 1 | Hex | Analyst teams that mix SQL and Python and publish apps | Yes | $36 per Editor per month |
| 2 | Deepnote | Small teams starting free who want AI credits with a fixed monthly budget | Yes | Free forever |
| 3 | Jupyter | Researchers, teachers and engineers who run their own notebook servers | Yes | Free |
| 4 | Marimo | Python developers who want reactive notebooks stored as Git-friendly files | Yes | Quoted |
| 5 | Google Colab | Students and individuals who want a browser notebook with borrowed cloud compute | Yes | Quoted |
| 6 | Fabi.ai | Business analysts who want an AI agent to build notebooks and reports | No | Quoted |
| 7 | Anaconda | Python teams that already manage packages with Anaconda and want governed AI tooling | Yes | From $15 per user per month |
| 8 | Nextjournal | Researchers who need reproducible runs in custom Docker environments | Yes | $0 (Personal) |
| 9 | Zerve | Financial firms that need agent workers inside an air-gapped perimeter | Trial | Quoted |
| 10 | Livebook | Elixir developers who want interactive notebooks for apps and machine learning models | Yes | Free |
| 11 | Apache Zeppelin | Big data teams already running Spark or Flink clusters | Yes | Free (open source) |
- 1
Hex takes first place because it is the most fully documented hosted option: four plan tiers with prices, per-minute compute billing, a named AI agent and a SOC 2 Type II report. The cost is per-Editor pricing and an app cap of five on Professional. Nothing else here combines that much printed detail with a free entry plan.
Strong on
- Plan prices, app caps and compute billing are all printed on the pricing page.
- Publishes a SOC 2 Type II report on its security page.
- Compute profiles run from 2 GB to 128 GB of memory, with GPU options.
Weak on
- Professional caps published apps at five, so app-heavy teams move to the $75 tier.
- Hosting region and model training policy were not stated on pages we read.
Skip it if: Teams that want open source files or a fully self-hosted notebook with no per-editor fee. Professional is the lowest paid plan; compute is billed per minute and Community is free.
- 2
Deepnote ranks second because its free plan is generous and its Team plan states exactly how much AI credit and CPU a month includes. That makes a first-year budget easy to estimate. It falls behind Hex because security claims are only named in navigation, with no report opened, and the Free tier limits editors to three.
Strong on
- Free plan allows three editors and five projects before any payment.
- Team lists $39 of AI credits and $280 of CPU each month.
- Enterprise adds bring-your-own LLM, SSO and audit logs.
Weak on
- SOC2 and HIPAA appear in navigation, but no report or policy page was opened.
- Free plan gives 10 AI completions a month and only seven days of revision history.
Skip it if: Teams that need a published security report or a hosted region chosen up front. Team is $39 per editor monthly billed yearly, with fixed AI credits and CPU allowance.
- 3
Jupyter sits third as the open standard that most other notebook tools read or imitate. It costs nothing and covers any language, which no hosted competitor matches. It ranks below the hosted pair because there is no vendor, no stated AI feature on the pages we read and no support, so every operational chore falls to you.
Strong on
- Free and open source, with governance and security pages on the project site.
- Subprojects such as JupyterHub, Binder and Voila cover sharing and deployment.
- Works across programming languages, not only Python, according to the About page.
Weak on
- No hosted service is described, so you provision and secure servers yourself.
- No AI capability was confirmed on the pages we opened.
Skip it if: Business teams that want a managed workspace with support and built-in AI. Open source software; the project is funded by donations, not paid plans.
- 4
Marimo comes fourth because its design fixes the two complaints engineers have about classic notebooks: hidden cell state and unreadable diffs. Reactive execution and plain Python files help, and AI assistance sits in the editor. It ranks under Jupyter because no pricing, privacy or hosting information could be read, so teams cannot judge its commercial side.
Strong on
- Running a cell reruns the cells that depend on it.
- Notebooks are plain Python files that work in Git and run as scripts or apps.
- Editor offers AI assistance with any model, plus built-in SQL.
Weak on
- The pricing and privacy pages returned 404, so hosted plans and data policy are unknown.
- Gallery, classes and molab were linked but not opened, so their scope is unverified.
Skip it if: Non-programmers and teams who need a vendor-run workspace with a published security statement. Homepage says all open source; the pricing page returned 404, so no hosted price was found.
- 5
Google Colab ranks fifth as the easiest place to start: open a browser, get a notebook and an AI coding partner at no cost. Pro and Pro+ add monthly compute units, and Enterprise runs inside Vertex AI. It sits mid-table because paid prices were not captured and the individual tiers say little about data handling.
Strong on
- Free tier needs only a browser and a Google account.
- Pro and Pro+ add monthly compute units for heavier jobs.
- Colab Enterprise offers IAM-secured workspaces inside Vertex AI and Google Cloud.
Weak on
- Pro and Pro+ prices and exact compute caps were not captured here.
- Enterprise features require Google Cloud and Vertex AI, which is a larger commitment.
Skip it if: Teams that need fixed pricing, a stated data policy for personal tiers or a non-Google stack. Free tier exists; Pro and Pro+ prices were not captured because they are not in the static page.
- 6
Fabi.ai takes sixth place on the strength of its product line: Smartbooks, an Analyst Agent, dashboards, workflows and an MCP server, with SOC 2 Type 2 named on its security page. It cannot rank higher because no price could be found, so a buyer cannot budget before talking to the sales team. Without a price, its SOC 2 claim is the main evidence for trust. Its homepage also announces that Fabi.ai is joining Omni, with no details, so check the roadmap before committing.
Strong on
- Names an Analyst Agent and Smartbooks as its AI notebook layer.
- Security page names SOC 2 Type 2; homepage lists GDPR and CCPA readiness.
- Lists dashboards, data apps, workflows and an MCP server.
Weak on
- No pricing was readable, so free tier, caps and seat costs are unknown.
- Integrations are listed in navigation but were not opened.
Skip it if: Buyers who must see a price list before a sales call or want open source files. The pricing page returned 404, so no plan or price was found; treat it as unpublished.
- 7
Anaconda ranks seventh because the notebook is one item on a product list that also covers package management, AI workspaces and a coding agent. Starter from $15 per user is cheap, but the notebook has no separate price or description. It suits organizations already inside the Anaconda ecosystem, less so those shopping for a notebook alone.
Strong on
- Core Starter lists secure Python package management from $15 per user.
- Platform covers cloud or on-premises deployment with governed AI workspaces.
- Kilo open-source coding agent supports bring-your-own-key model choice.
Weak on
- The Notebooks product has no separate price or feature page in what we read.
- AI inference and cloud compute are billed separately from the seat price.
Skip it if: Teams wanting a standalone notebook with a clear per-editor price and a stated AI policy. Four products priced separately; AI inference and cloud compute are billed on top.
- 8
Nextjournal lands eighth: it runs anything that fits in a Docker container, versions every run automatically and prints three plan prices. Those strengths suit reproducible research. It trails the leaders because Team starts at $299 for five editors, no AI feature is named and the footer carries a 2021 copyright line. Its pricing is at least clear.
Strong on
- Runs anything you can put into a Docker container, with GPU support.
- Automatic versioning and real-time collaboration on every notebook, plus polyglot support.
- Enterprise offers hosted or on-prem deployment on AWS or Google Cloud.
Weak on
- Team costs $299 a month including five editors, with extra editors at $99.
- No AI feature was named on the pages we read.
Skip it if: Solo analysts who want AI-written queries or small teams with a tight budget. Team and Enterprise include five editors; paid plans have a 14-day trial.
- 9
Zerve ranks ninth because it is narrow by design. Superagents split one objective into parallel workers, and the security page states ISO 27001 with an air-gapped deployment on Enterprise. That is strong for hedge funds and banks. For everyone else the quote-only price and finance-focused positioning make it hard to evaluate. A no-cost enterprise trial exists.
Strong on
- Security page states ISO 27001 certification for the company.
- Runs inside the customer's own perimeter, with an air-gapped option.
- Agents are described as fully inspectable down to the code.
Weak on
- Pricing is quote only, so there is nothing to budget against.
- The homepage positions it at hedge funds, asset managers and banks.
Skip it if: Small teams or general analysts who want published prices and a self-serve plan. Enterprise is a custom agreement per firm; an enterprise trial is offered at no cost.
- 10
Livebook ranks tenth because it is excellent only inside one ecosystem. The homepage describes Elixir notebooks that can share knowledge, deploy apps and run machine learning models, and it costs nothing. The pages show no AI assistant, no hosted plan and no maker name, so most data teams will look elsewhere. The latest release note we read dates from September 2024.
Strong on
- Free and open source, with a Livebook Desktop option for local use.
- One notebook can share knowledge, deploy apps and run ML models.
- Release notes describe an outline panel and go-to-definition navigation.
Weak on
- The runtime is Elixir; Python notebooks are not described on the pages we read.
- No AI feature or hosted plan was named.
Skip it if: Python and SQL analysts who want AI help or a managed cloud workspace. Homepage describes it as open source and free to run anywhere.
- 11
Apache Zeppelin closes the list because it was built around big-data interpreters rather than AI-assisted analysis. It supports SQL, Scala, Python and R and runs on Kubernetes, Docker and YARN. Setup effort is real, and the pages name no AI feature, so it suits infrastructure-heavy teams with existing clusters of their own. Version 0.12.1 is the latest listed.
Strong on
- Interpreters for Spark, Flink, SQL, Python and R, installed separately.
- Run modes cover Kubernetes, Docker and YARN deployments.
- Free under the Apache License, current download listed as 0.12.1.
Weak on
- Each interpreter must be installed and configured separately.
- No AI feature was named and no hosted option exists on the pages we read.
Skip it if: Small teams without ops support or analysts who expect built-in AI help. Apache-licensed software you download and run; no hosted service was found.
How these AI data notebook software were judged
How good is the AI in a data notebook?
The AI differs more than the notebook cells do. Hex names an agent for conversational analytics, Deepnote lists AI completion and credits, and Fabi.ai calls its notebooks Smartbooks driven by an Analyst Agent. Marimo describes copilots and context-aware chat that work with any model. Colab calls its assistant a coding partner. Zerve builds its product around parallel agent workers. Jupyter, Livebook, Zeppelin and Nextjournal name no AI on the pages we read. Test each with one messy question about your own warehouse and count how many generated queries run without edits.
What does a data notebook cost per editor?
Prices are published for some tools and hidden for others. As of November 2026, Hex Professional is $36 per Editor a month and Team $75, with compute billed per minute. Deepnote Team is $39 per editor a month billed yearly, with $39 of AI credits and $280 of CPU included. Nextjournal Team is $299 a month including five editors, and Anaconda lists Starter from $15 per user. Jupyter, Marimo, Livebook and Zeppelin are free software. Fabi.ai and Zerve give no price, and Colab Pro prices were not captured.
Where do notebooks run and who can see your data?
The answer depends on whether you host the notebook. Open source tools such as Jupyter and Zeppelin run on your own servers, so the vendor sees nothing. Hosted tools publish less. Hex shows a SOC 2 Type II report on its security page, Fabi.ai names SOC 2 Type 2, and Zerve states ISO 27001 and runs inside the customer's perimeter. Deepnote names SOC2 and HIPAA in its navigation without a report. Few pages say whether prompts train models, so ask in writing before connecting a production warehouse.
Can you take your notebooks with you?
Format decides how portable the work is. Marimo stores notebooks as plain Python files that sit well in Git, and Jupyter is an open standard that many other tools read. Livebook and Zeppelin tie you to Elixir and to Zeppelin's interpreter model. Hosted tools add their own layers: Hex and Deepnote publish apps and dashboards, Nextjournal versions every run, and Fabi.ai offers Workflows and an MCP server. Before committing, export one real project and open it in a free tool to see what breaks.
Which data notebook fits your team?
Match the tool to who writes the analysis. A small team that wants to start free fits Deepnote, which allows up to three editors and five projects without cost. Analysts who live in SQL and publish apps fit Hex. Researchers and teachers fit Jupyter or Colab. Software engineers who want Git-friendly files fit Marimo. Banks and funds that need an air-gapped deployment fit Zerve. Elixir developers fit Livebook, and Spark users fit Zeppelin. Count editors, not viewers: plans treat them differently.
How to choose an AI data notebook in three steps
- Decide who hosts the compute before you compare features. Settle whether notebooks may run on a vendor's cloud, on your own cluster or only on laptops. That choice removes half the field. Jupyter, Marimo and Zeppelin run where you put them, Zerve deploys inside your perimeter, Hex and Deepnote bill for compute they host, and Colab runs in Google's cloud. If analysts will connect to a production warehouse, write down which credentials the notebook server will hold and who can read them, then ask each hosted vendor in writing where that connection runs and what is logged.
- Run the same three questions through every AI assistant. Prepare three questions of rising difficulty on your own data: a simple aggregation, a join across two tables and a vague request such as why signups dropped in March. Give each tool a trial notebook and record how many generated queries ran unchanged, how many needed a fix and whether the agent explained its steps. Note the credit cost, since Deepnote and Hex both meter AI use. An assistant that answers the easy question well but guesses on the vague one still saves time, but only if analysts check it.
- Count editors, apps and compute minutes for the first year. Tally the people who write notebooks, the people who only read results and the apps you expect to publish. Hex Professional caps published apps at five, Deepnote Free caps editors at three and projects at five, and Nextjournal sells a base bundle of five editors. Then estimate monthly compute hours, because Hex bills per minute and Deepnote includes a fixed CPU allowance. Multiply by a year, add an overage buffer and compare against a self-hosted option that costs an engineer's time instead of a seat price.
Frequently asked questions
What is the best AI data notebook software in 2026?
Hex ranks first here because it prints four plan tiers, bills compute per minute, names an AI agent and shows a SOC 2 Type II report. Deepnote is the pick for a free start with fixed AI credits, and Jupyter suits teams that want open source they host themselves.
Is Jupyter still worth using now that AI notebooks exist?
Yes for many teams. Jupyter is free, open source and read by a wide range of other tools, and its project pages describe JupyterLab, JupyterHub, Binder and Voila. The pages we read name no built-in AI, so you add that yourself or pick a hosted tool that bundles it.
Which data notebook has a genuinely free plan?
Deepnote Free allows up to three editors, five projects and limited AI. Hex Community is free for hobbyists and small projects, and Nextjournal Personal costs $0 with limited concurrent compute. Jupyter, Marimo, Livebook and Zeppelin are free open source software, and Colab has a free tier whose caps we did not capture.
Can a data notebook run inside my own network?
Yes with several of them. Zerve states a fully air-gapped deployment on Enterprise, Nextjournal Enterprise lists hosted or on-prem deployment, and Anaconda's platform lists on-premises or cloud. Jupyter, Marimo, Livebook and Zeppelin are software you run on servers you control, so the network boundary is yours to set.
How is Hex different from Deepnote?
Both are hosted collaborative notebooks with AI. Hex sells Professional at $36 and Team at $75 per Editor monthly and bills compute per minute, while Deepnote lists a free tier and Team at $39 per editor with fixed monthly AI credits and CPU. Hex publishes a SOC 2 Type II statement, and Deepnote names SOC2 and HIPAA in its navigation.
Do I need Python to use an AI data notebook?
Not always. Hex describes notebook-style analytics for SQL and Python plus conversational analytics through its agent, and Fabi.ai pitches an Analyst Agent for data questions. Marimo and Jupyter assume you write code. Livebook uses Elixir, so it is a poor match for teams that only know Python or SQL.
What happens to my notebooks if the vendor changes pricing?
It depends on the file format. Marimo stores notebooks as plain Python files and Jupyter notebooks open in many tools, so moving is cheap. Hosted apps, scheduled runs and shared components in tools such as Hex or Deepnote are harder to carry, so keep the source code in Git and test an export early.
Is Google Colab suitable for company data?
The free and Pro tiers are aimed at individuals, and the pages we read name Colab Enterprise as the version with IAM-secured workspaces inside Vertex AI and Google Cloud. Pro and Pro+ prices were not captured here. A company should check the Enterprise terms and data location before putting customer data in any Colab tier.