Open-source ThoughtSpot alternatives

Apache Superset is a data exploration and visualization platform that runs as a web application. Analysts and business users can connect it to a database, build charts without code, assemble them into dashboards and share them, so it can augment or replace proprietary business intelligence tools for many teams.

The no-code chart builder sits next to a web-based SQL editor for advanced queries, and a lightweight semantic layer lets teams define reusable dimensions and metrics. Visualizations range from simple bar charts to geospatial views. Superset can query any SQL datastore or engine provided a matching Python database driver and SQLAlchemy dialect exist, including Presto, Trino and Athena. A configurable caching layer reduces database load, and security roles and authentication options are extensible.

The stack is Python with Flask on the back end and React on the front end, under the Apache-2.0 license. A REST API and extension framework allow programmatic control and customization. Documentation is split into guides for users, administrators and developers, and it is a typical choice for organizations that want a self-managed BI layer.

Metabase is an open-source business intelligence and embedded analytics tool meant for everyone in a company, not only analysts. People can ask questions about their data without knowing SQL, while an SQL editor is available for more complex queries. Written in Clojure, it connects to databases such as PostgreSQL and MySQL.

Its features include interactive dashboards with filters, auto-refresh and custom click behavior, plus documents for long-form analysis that colleagues can comment on. An AI assistant called Metabot helps answer questions and write queries, and you can build your own AI agent against your data. Data Studio supports transforming raw data into analytics-ready tables and defining canonical metrics. Alerts and scheduled dashboard subscriptions go to email, Slack or a webhook, and a Library plus Git integration help version your work.

Metabase can also be embedded in your own product with components for charts, dashboards, a data browser and AI chat, and granular permissions work for internal teams and embedded customers. You can self-host it or use Metabase Cloud, which the project says includes support, backups, upgrades and a free trial. The repository license is listed as 'Other', so review its terms.

DB-GPT is an open-source AI data assistant for the next generation of products that combine AI and data. It connects to databases, CSV and Excel files, data warehouses and knowledge bases, then lets people ask questions in natural language while the AI writes SQL and code to answer them.

Beyond question answering, it supports agentic data analysis: planning a task, breaking it into steps, calling tools and finishing the analysis end to end. It can run Python and code-driven workflows, load reusable skills for domain-specific tasks, and generate charts, dashboards, HTML reports and summaries. Tasks run in sandboxed environments for safety. Beyond the assistant, it serves as a platform for building AI-native data agents and applications with its AWEL workflow language, retrieval-augmented generation and multi-model support, with topics naming models such as DeepSeek and Vicuna.

It is written in Python and licensed under MIT, with documentation, a community and a research paper. Privacy and security are highlighted in the topics, and it can run on your own infrastructure with local models. It suits data teams that want natural-language access to databases while keeping data in-house.

Lightdash is an open-source business intelligence platform that it describes as agentic BI, meant for data teams that want analytics to be shipped like software. Metrics, joins, permissions, business logic and caching are defined once in a context layer, and that layer feeds MCP, SDKs, embedded analytics, data apps, AI agents and dashboards.

Analytics can be built as code: metrics, charts and dashboards live as files that you can edit with coding agents, preview from the Lightdash CLI, validate in CI and review in pull requests. Business users can pose questions in plain English, browse dashboards or build custom data apps, all while governance stays in place. Agents answer from the context layer rather than guessing from raw tables, respect permissions and return queries that can be inspected. The repository topics mention dbt.

Lightdash is written in TypeScript. The vendor recommends starting with Lightdash Cloud, which needs no infrastructure to run, and also offers a live demo, documentation and a sales call. The repository lists its licence as other, so check the terms before self-hosting.

nao is an open-source framework for building an analytics agent and deploying it as a chat interface. Data teams assemble the agent's context with the nao-core command-line tool, then give business users a UI where they can ask questions in natural language and receive analysis, charts and the reasoning behind each answer.

On the data team side, the context builder works like a file system and can include data, metadata, documentation, tools and MCP servers. The framework is described as stack-agnostic, with topics pointing to BigQuery, Snowflake, Databricks and PostgreSQL. Teams can unit test the agent's performance before release, version the context, track usage and collect feedback from users who mark answers right or wrong.

nao is written in TypeScript and is designed to be self-hosted with your own LLM keys, which keeps data under your control. A Slack connection is available as an option during setup. It suits data and analytics engineering teams who want a governed text-to-SQL assistant for the rest of the company rather than ad hoc prompts.

Frappe Insights is an open-source business intelligence tool that aims to make data analysis and reporting accessible to both technical and non-technical users. It grew out of the experience of working with Frappe Framework applications, where building structured data was easy but extracting reports usually required SQL knowledge.

You can connect multiple sources, including databases, files and spreadsheets, and use a step-by-step query builder to select tables, add joins, apply filters and calculations without writing SQL. Results can be visualized with various charts, with the tool suggesting a fitting chart, and dashboards are assembled by drag and drop with dashboard filters. Supported databases include MySQL, PostgreSQL, DuckDB and BigQuery. A SQL editor is also available.

Insights is built on the Frappe Framework, Frappe UI with Vue, and the Ibis library for composing SQL with dataframe APIs. It is written in Python, licensed under AGPL-3.0, and can be self-hosted, with a live demo and documentation. It suits ERPNext and Frappe users as well as teams wanting a lightweight BI tool.