Alternatives
Best Metabase Alternatives in 2026
Metabase is a popular open-source business intelligence tool that lets anyone query a database, build dashboards, and ask questions without SQL, and its free self-hosted edition makes it a default for many teams. But depending on your workflow you may want something different: a SQL-first analytics platform with polished reports, a notebook that blends SQL and Python, a collaborative data-science workspace, a conversational AI data analyst, or an enterprise AutoML and governance suite. The six alternatives below span that spectrum, from analyst-friendly notebooks to enterprise machine-learning platforms. Most offer free tiers or trials so you can test them on your own data before committing, and each brings a distinct strength worth weighing against Metabase.
Why look for Metabase alternatives?
- → You want a SQL-first analytics platform with notebook-based exploration and polished, scheduled reports rather than dashboard-centric self-serve BI.
- → Your analysts live in code and need a true notebook that blends SQL and Python with version control, scheduling, and shareable data apps.
- → You would rather chat with your data in plain language than build dashboards, and want an AI assistant that writes and explains the analysis code.
- → You need enterprise-grade AutoML, MLOps, model monitoring, and governance to build and deploy predictive models in production at scale.
Mode
SQL-first analytics and dashboards
Hex
SQL plus Python notebook teams
Deepnote
Collaborative data-science notebooks
Julius
Conversational AI data analyst
Dataiku
End-to-end governed data science
DataRobot
Enterprise AutoML in production
How they compare to Metabase
Each alternative wins on a different dimension. Skim the highlights below or click through for a full review.
Mode , 4.3/5
Best for SQL-first analytics and dashboards.
Mode is a SQL-first collaborative analytics platform that pairs a powerful SQL editor with Python and R notebooks and a drag-and-drop report builder for dashboards. Analysts query the data warehouse directly, then layer notebook-based exploration on top and ship interactive reports to stakeholders, all within one tool. Compared with Metabase, Mode leans toward established analytics teams whose workflows start in SQL and end in polished, scheduled reports rather than dashboard-first self-serve BI for non-technical staff. It connects to major warehouses, supports reusable datasets and definitions, and includes collaboration features so teams can build on each other's queries. Mode is especially strong when the goal is repeatable business reporting and notebook-driven exploration in the same place, with visualizations and parameters that let viewers explore data themselves. It offers a free Studio tier for individuals and small teams, with paid business plans adding advanced collaboration, security, and governance. Pick Mode when SQL fluency plus notebook analysis matter as much as shareable dashboards.
Hex , 4.4/5
Best for SQL plus Python notebook teams.
Hex is a notebook-native analytics workspace that blends SQL, Python, and no-code cells in a single reactive environment, then lets you publish the results as polished, interactive data apps. Its Magic AI features can write queries, generate charts, and explain code from natural-language prompts, giving you AI assistance inside a more structured, warehouse-connected workflow. Hex shines for data teams that live in SQL but still want Python flexibility, with strong connections to Snowflake, BigQuery, and other databases, plus version control, scheduling, and reusable components. Where Metabase centers on dashboards and self-serve questions for a broad audience, Hex centers on a reproducible project graph and shareable apps built by analysts. It supports collaboration with commenting and review, and parameterized apps that stakeholders can interact with directly. A free tier is available for individuals, with paid plans scaling collaboration, compute, and governance. Choose Hex if you want AI assistance layered on a serious, SQL-centric notebook and data-app platform.
Deepnote , 4.4/5
Best for Collaborative data-science notebooks.
Deepnote is a collaborative AI data notebook built around Jupyter-compatible kernels, so teams can write Python, run SQL cells, and edit the same notebook together in real time, much like a document. It connects directly to data warehouses and databases, supports scheduled notebook runs, environment management, and version history, and includes an AI copilot that helps generate and explain code inline. Compared with Metabase, Deepnote is a code-first, multi-user workspace for analysts and data scientists rather than a dashboard tool aimed at non-technical self-serve users. It also turns notebooks into interactive apps and dashboards you can publish to non-technical stakeholders, which narrows the gap, but the core experience is notebooks and code. A free tier lets individuals and small teams start without cost, with paid plans adding more compute, collaboration seats, and governance controls. If your priority is teamwork plus full notebook flexibility for a technical team, Deepnote is the closest collaborative match.
Julius , 4.4/5
Best for Conversational AI data analyst.
Julius is a natural-language data analysis platform: you upload a spreadsheet or connect a database, then ask questions in plain English and Julius writes and runs the Python or R code, returning charts, tables, and written insights. It shows the generated code so you can verify the methodology or copy it into your own notebook, and it is strong at statistical analyses like regression, correlation, and hypothesis testing. Compared with Metabase, Julius is conversation-first and built for ad-hoc exploration of a dataset rather than a persistent, governed BI platform connected to your warehouse with shareable dashboards. Where Metabase serves a whole organization with self-serve dashboards and permissions, Julius is more of a personal analyst that answers one-off questions quickly, especially for users who want statistics without writing code. It offers a free tier to test the interface, with paid plans unlocking higher message limits, larger datasets, and database connectors. Choose Julius when you want to chat with your data and get fast, code-backed answers rather than build and maintain dashboards.
Dataiku , 4.4/5
Best for End-to-end governed data science.
Dataiku is a full end-to-end data-science and machine-learning platform that combines a visual flow builder with code (Python, R, SQL) so technical and non-technical users can collaborate on the same projects. It covers the whole lifecycle: data preparation and cleaning, visual or coded modeling, AutoML, deployment, and ongoing monitoring, with governance, access controls, and project documentation built in. Compared with Metabase, Dataiku is an enterprise-scale workbench for teams that need to operationalize data pipelines and models, not just query data and build dashboards. Its drag-and-drop recipes let analysts build workflows without code, while data scientists can drop into notebooks when needed, making it a strong bridge between business and engineering. It also includes generative AI features and connects to a wide range of databases, cloud storage, and warehouses. Dataiku offers a free edition for getting started, with enterprise plans adding collaboration, governance, and MLOps at scale. Choose Dataiku when you need one governed platform spanning prep, modeling, and deployment across a whole team.
DataRobot , 4.4/5
Best for Enterprise AutoML in production.
DataRobot is an enterprise AutoML platform focused on building, deploying, and monitoring predictive and generative AI models at scale. Rather than dashboards and ad-hoc questions, it automates the machine-learning pipeline: feature engineering, model training across many algorithms, validation, and ranking, then handles deployment, monitoring, and drift detection in production through its MLOps tooling. Compared with Metabase, DataRobot is aimed at data scientists and enterprises that need governed, production-grade models, not business intelligence reporting. It supports both automated and code-first workflows, integrates with enterprise data sources, and emphasizes explainability, compliance, and governance, which matters for regulated industries. Teams use it to forecast demand, predict churn, detect fraud, and operationalize models that feed live business systems. DataRobot is priced for enterprise deployments rather than individual analysts, typically via custom plans and trials. Choose DataRobot when your real goal is deploying and maintaining reliable predictive models in production, with the scale, monitoring, and governance an enterprise requires.