Basedash vs Dataherald
Basedash
basedash.comDataherald
github.comSide by side
| Figure | Basedash | Dataherald |
|---|---|---|
| Price from | $1,000/mo | — |
| Free plan | — | — |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 0 |
| Spec score · AI SQL Generators | 8.4 (#2) | 7.7 (#10) |
| Deployment | Basedash is available as managed cloud, private VPC, or self-hosted deployment, including Docker, Kubernetes, and Helm options. | The quick start describes self-hosting the engine with Docker, with Mongo running in a separate container for application data. |
| Intended users | Basedash describes its audience as data leaders and business teams, including product, sales, marketing, finance, and operations teams. | The documentation is intended for developers building natural-language interfaces from structured data in their own projects and contributors to the engine. |
| Product | Basedash is an AI-native business intelligence platform that turns plain-English questions into governed answers, dashboards, and automated reports on live company data. | Dataherald is a natural language-to-SQL engine that exposes an API for asking questions about relational data in plain English. |
| Security | Basedash lists SOC 2 Type II certification, TLS encryption in transit, AES-256 encryption at rest, SSO, SCIM, role-based access, row-level security, and audit logs. | The quick start says database connection data is encrypted before storage in MongoDB using an ENCRYPT_KEY and recommends changing the default MongoDB username and password. |
Plans
In their own words
Basedash
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.
Dataherald
Dataherald is an open-source natural language-to-SQL engine that exposes an API for asking questions about relational data in plain English. Its stated uses include helping business users query warehouses, embedding data Q&A in SaaS products, and creating ChatGPT plug-ins for proprietary data. The project targets developers building natural-language interfaces or contributing to the engine. Self-hosting uses Docker, with MongoDB in a separate container for application data. Listed warehouse connections are PostgreSQL, BigQuery, Databricks, and Snowflake. Included SQL-generation approaches are LangChain SQL Agent, LangChain SQL Chain, LlamaIndex SQL Generator, and Dataherald SQL Agent. A context store can include business and database details, validated question-and-SQL examples, and table information; active learning from usage is also described. An evaluator assigns confidence scores to generated SQL. The modular architecture allows implementations to be changed through .env configuration. Startup requires an OpenAI API key, organization ID, and encryption key. Connection data is encrypted before Mongo storage. The repository uses Apache-2.0 licensing.