Mljar Studio
local AI data analyst that saves analysis as notebooks
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What is Mljar Studio?
MLJAR Studio is a private AI data lab and desktop application for exploring data, running machine learning experiments, and building analysis tools. It runs entirely locally on your computer, allowing users to chat with their data in natural language and get real Python code, charts, and reproducible notebook-based results. The tool combines conversational analytics with autonomous machine learning capabilities, making it suitable for data analysts, data scientists, researchers, and teams working with sensitive data who want AI assistance without cloud risks.
Key features include AI Data Analyst for conversational notebook workflows, AutoLab Experiments for autonomous ML optimization with iterative improvement, AI Code Assistant for faster Python development, ready-to-use Code Recipes, and Workflow management for prompt sequences. The tool automatically sets up a local Python environment, handles feature engineering, performs hyperparameter tuning, tracks experiments, and generates explainability reports. Users can convert notebooks to interactive web apps powered by Mercury and self-host them on their own infrastructure.
MLJAR Studio is designed for data analysts exploring datasets, data scientists running experiments, researchers working with sensitive data, and teams that want AI without cloud risks. It supports tabular data including CSV, Excel, Stata, and Parquet files, and connects to databases like PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, and Supabase. The tool works on Windows, macOS (including Apple Silicon M1/M2/M3), and Linux.
Mljar Studio pricing
Pricing model: Freemium
MLJAR Studio offers a Free plan ($0) with all core features including AI Data Analyst, AutoML, AutoLab Experiments, and AI-assisted notebooks, limited to 50 prompts/month, 10 published conversations, and 1 public Mercury web app. The Pro and Business subscription plans are available for hosted usage. The Perpetual License costs $199 as a one-time payment, giving you continued use of the purchased version forever, 1 year of updates included, and access to local LLM workflows with Ollama plus bring-your-own-provider API keys. A 7-day free trial is available through platform.mljar.com. The MLJAR AI add-on is available at $49/month for additional hosted AI features.
Mljar Studio pros
- 100% local execution - data never leaves your computer
- Free tier available with no time limit and 50 prompts per month
- Perpetual license option at $199 one-time payment
- Automatic Python environment setup - no manual configuration needed
- Built-in AutoML for tabular classification, regression, and multiclass tasks
- AutoLab Experiments with AI agents for autonomous ML optimization
- Conversational notebook workflow - ask questions in plain English
- All results saved as reproducible IPython notebook files
- Supports local LLMs via Ollama with zero data egress
- Bring your own OpenAI and other provider API keys
- One-click conversion of notebooks to interactive Mercury web apps
- Built-in experiment tracking dashboard with performance trends
- Built-in explainability with feature importance insights
- Code Recipes with ready-to-use Python snippets for common tasks
- Works with CSV, Excel, Stata, Parquet, and multiple database connections
- Iterative improvement loop - learns from previous experiment runs
- Transparent code generation - every line is visible and editable
- No external APIs required for core functionality
- 7-day free trial available before purchase
- Supports Mac Apple Silicon (M1, M2, M3) natively
Mljar Studio cons
- Perpetual license costs $199 which is expensive for individuals
- Free tier limited to 50 prompts per month and 10 published conversations
- Only supports structured/tabular data, not images or unstructured text
- Requires downloading and installing desktop application
- Updates after one year require purchasing renewal for update access
- MLJAR AI add-on costs additional $49/month for hosted features
- Beta/early-stage software still receiving frequent bug fixes
- Small company (2-10 employees) with limited support resources
- No cloud collaboration features - all work is local only
- Linux installation requires manual deb package management
Frequently asked questions about Mljar Studio
Does MLJAR Studio require cloud services?
No, MLJAR Studio works 100% locally on your computer. Your data never leaves your machine. You can use local LLMs via Ollama with zero data egress, or bring your own OpenAI and other provider API keys if you prefer. The perpetual license is required if you want to use local LLMs with Ollama or connect your own provider keys.
Can I use local AI models?
Yes, MLJAR Studio supports local LLMs through Ollama. This allows you to run AI models entirely on your machine with zero data egress. The perpetual license at $199 is required if you want to use local LLM workflows with Ollama inside MLJAR Studio.
Do I need programming experience?
No, MLJAR Studio works for both beginners and experts. Beginners can start quickly by asking questions in plain English and getting instant insights with AI-generated Python code. Advanced users keep full control and can inspect, edit, and modify every line of generated code. You only need to define the target column and evaluation metric for AutoLab experiments.
Is MLJAR Studio just an AI tool?
No, MLJAR Studio is a complete desktop application for data analysis and machine learning. It includes AI Data Analyst for conversational workflows, AutoLab Experiments for autonomous ML, AI Code Assistant for Python development, Code Recipes, Workflow management, and exports to interactive Mercury web apps. It's a full data science workspace, not just an AI interface.
What type of data can I analyze?
MLJAR Studio works with structured/tabular data including CSV files, Excel, Stata, and Parquet formats. It automatically detects numeric features, categorical features, and missing values. It also connects to databases including PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, and Supabase. It is designed for classification and regression tasks on tabular data.
Can I share my analysis with others?
Yes, you can share your analysis in multiple ways. All work is saved as reproducible IPython notebook files that you can share directly. With one click, you can convert notebooks to interactive Mercury web apps and self-host them on your own server. Version 1.2.1 added one-click sharing for AI Data Analyst conversations. No cloud services are required for sharing.
Is MLJAR Studio open source?
MLJAR Studio itself is not open source - it is commercial software with Free, Pro, Business, and Perpetual License options. However, the underlying mljar-supervised Python package for AutoML on tabular data is open source and available on GitHub. Mercury, the framework powering notebook-to-app conversion, is open source.
How is AutoLab different from traditional AutoML?
Traditional AutoML focuses on finding the best model. AutoLab Experiments goes further by running the full experimentation process: it generates feature engineering ideas, tests multiple approaches, tracks all experiments with an advanced dashboard, and explains what worked and what failed. AutoLab includes iterative learning where it learns from previous runs, has built-in explainability, and produces reproducible notebooks for every experiment.
Does AutoLab replace a data scientist?
No, AutoLab helps data scientists work faster by automating repetitive work like feature engineering trials, hyperparameter tuning, and experiment tracking. You still make decisions and interpret results. You can edit the AGENTS.md plan, adjust experiment settings, and modify notebooks. AutoLab is notebook-first and not a black box - every experiment is visible, editable, and reproducible.
What outputs do I get from AutoLab Experiments?
AutoLab produces trained models, experiment metrics, feature importance insights, a final summary of results showing best model and best score, what worked and what failed, and reproducible notebook files for every experiment. Each experiment is saved as a notebook so you can inspect code, rerun it, modify pipelines, and export it. You get a clear summary with key feature drivers and practical insights.