Basedash vs Natural Language SQL

Basedash is an AI-native business intelligence platform for asking plain-English questions about live company data and turning answers into governed dashboards and automated reports. Its AI analyst validates generated SQL against connected schemas and displays the SQL behind each answer. Users describe the metrics they want to follow, and Basedash assembles charts, KPIs, and dashboard layouts. Teams can define dimensions, measures, and segments for reuse across chat, dashboards, and automations. A managed DuckDB warehouse centralizes data synced from more than 750 sources. Listed connections include PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, Databricks, Salesforce, HubSpot, and Stripe. The agent answers questions in Slack with chart images; reports and alerts can go to Slack and email. Deployment options are managed cloud, private VPC, or self-hosting with Docker, Kubernetes, and Helm. Security listings include SOC 2 Type II, encryption, SSO, SCIM, role-based and row-level security, and audit logs.

Natural Language SQL turns plain-English questions about databases into SQL, validates and runs the query, then streams back results. It connects to PostgreSQL and MySQL, including multiple databases of the same type in parallel. Hybrid retrieval combines BM25 and sentence-transformer vectors to select schema context for a question. Users can review and edit generated SQL before execution, while the interface shows progress through query stages. Results can be sorted, paginated at 50 rows per page, downloaded as CSV or JSON, or copied as tab-separated text. SELECT queries receive a default limit, explicit result counts are capped at 1,000 rows, and execution times out after 30 seconds by default. INSERT, UPDATE and DELETE are allowed by default, with warning banners; the project recommends a read-only database user to block writes. Ollama runs locally by default, keeping questions, schema and generated SQL on the user’s machine. OpenAI, Gemini and Groq are optional cloud providers and receive the question, relevant schema portion and SQL context. The project is free under GPL-3.0. Its recommended Docker setup needs Docker 20.10 or later with Compose v2 and about 10 GB of free disk space.