Runcell pricing, free plan and limits

Last updated: 2026-09-06

Runcell is a JupyterLab extension and AI agent that writes, runs, and debugs Python code inside Jupyter notebooks from natural-language instructions, cutting a typical 45-minute exploratory analysis session to under 10 minutes. It reads the notebook's live cell outputs, variable values, and DataFrame structures, grounding suggestions in real data rather than static file contents alone.

About Runcell

Runcell is a JupyterLab extension and Python package that adds an AI agent directly inside Jupyter notebooks, enabling data scientists and analysts to write, execute, and debug Python code using plain-English instructions without leaving the notebook environment. Unlike general-purpose coding assistants, Runcell reads the actual notebook state, including cell outputs, DataFrame structures, variable values, and execution order, giving its AI grounding in the real data rather than guessing from file contents alone. The platform organizes its capabilities into four distinct modes. Autonomous Agent Mode takes full control of the notebook: it plans a multi-step analysis task, writes each cell, runs it, reads the output, fixes any errors, installs missing packages, and retries until the goal is complete. Reasoning Mode provides a step-by-step problem-solving layer for complex refactors or statistical analyses that require thinking through intermediate steps before writing code. Interactive Learning Mode functions as an AI tutor, producing live-coded examples to explain concepts like the difference between K-means and DBSCAN clustering directly in notebook cells. Smart Edit Mode acts as a context-aware chat assistant for targeted code edits, chart generation, and result explanations. Runcell is purpose-built for data professionals who spend the majority of their day in Jupyter. Common workflows include exploratory data analysis, automated data cleaning, feature engineering, visualization generation, and stakeholder report summarization, all executed with a single instruction instead of multiple manual cell edits. Pricing is credit-based, with a free Hobby tier and a paid Pro tier; exact allotments are in the pricing FAQ below. The extension requires JupyterLab 4.0 or newer and installs via pip or conda. As a newer tool with limited public company information, Runcell occupies a narrow but defensible niche as the only production-grade AI agent built specifically for Jupyter, rather than adapted from a general-purpose coding assistant.

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Pricing

Hobby (Free): 20 credits/month, limited AI execution credits and model access, no Privacy Mode. Pro: $20/month ($180/year), 500 credits/month, access to GPT 5.5, Claude Opus 4.7, and Gemini 3.5 Pro, priority support, Privacy Mode on by default. Pro+: $60/month ($600/year), 2,000 credits/month, all models, priority access to new features. Teams: $40/seat/month ($400/year), 500 credits per seat, centralized billing and admin controls. Ultra: $200/month ($2,000/year), 10,000 credits/month, everything in Pro+.

Key Features

• Autonomous Agent Mode: Plans multi-step data tasks, writes and runs each cell, reads outputs, fixes errors, installs missing packages, and retries automatically until the analysis is complete.
• Notebook State Awareness: Reads actual cell outputs, variable values, and DataFrame structures in memory so AI suggestions are grounded in real data, not inferred from source code alone.
• Reasoning Mode: Step-by-step problem-solving layer that thinks through complex statistical refactors or multi-stage analyses before writing a single line of code, reducing incorrect first attempts.
• Interactive Learning Mode: AI tutor that generates live-coded notebook cells to explain algorithms or methods (e.g., comparing K-means vs. DBSCAN) with working examples runnable directly in the session.
• Smart Edit and Chart Generation: Context-aware chat panel that generates visualizations, explains cell outputs in plain English, writes targeted code patches, and inserts results directly into notebook cells.
• Multi-Model Access with Privacy Mode: Paid tiers add a choice of frontier models beyond the free default, with Privacy Mode on so prompts and outputs are excluded from training; the free Hobby tier has no Privacy Mode and its sessions may be used to improve the product.

Pros

• Purpose-built for Jupyter: reads real cell outputs and DataFrame contents, so its code suggestions reference actual data values rather than placeholder logic that general-purpose tools like Copilot produce.
• Autonomous Agent Mode handles complete end-to-end workflows including error recovery and package installation, cutting a 45-minute manual EDA session to under 10 minutes for standard datasets.
• The free Hobby tier is enough to trial the core workflow before paying, and the paid Pro plan remains cheaper than enterprise data science tools like DataRobot or Databricks AI.

Cons

• Works exclusively with Jupyter notebooks (JupyterLab 4.0+), making it incompatible with VS Code notebooks, Google Colab, or script-based Python workflows that represent a large share of data science work.
• Credit-based pricing means heavy Autonomous Agent Mode sessions can exhaust a full month's Pro allotment within a week, leaving analysts rate-limited unless they move up to one of Runcell's higher-priced tiers.
• Runcell's parent company Kanaries raised only an undisclosed seed round (MiraclePlus, Yinxinggu Capital, 2024) with no confirmed Series A, and Runcell has limited public documentation and a small community, raising questions about long-term support and feature velocity compared to GitHub Copilot or Cursor.

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