Hex is an analytics and data science workspace that combines notebooks, AI agents, SQL, Python, dashboards, and collaborative data applications. Its AI features can help analysts and technical teams explore data, work with notebooks, and automate parts of analytical workflows without separating expl
AI notebook and data agents
SQL and Python analytics
Interactive data apps and dashboards
About
Hex is an analytics and data science workspace that combines notebooks, AI agents, SQL, Python, dashboards, and collaborative data applications. Its AI features can help analysts and technical teams explore data, work with notebooks, and automate parts of analytical workflows without separating exploration from presentation. For founders, it provides a way to investigate product, revenue, growth, and operational data in one environment while retaining the ability to work directly with code and queries.
A startup team can use Hex to connect data sources, explore metrics, build analyses, publish interactive apps, and create reusable reporting workflows. Its Notebook Agent and Threads Agent can assist with data work, while semantic models can provide more structured context. Hex also supports scheduled runs, alerts, collaboration, and advanced compute, making it useful when a founder wants a repeatable analytical workflow instead of manually rebuilding reports every week.
Hex is differentiated by the combination of a notebook-style environment, AI agents, interactive reporting, and production-oriented data workflows. It is more technical than lightweight dashboard tools and can require data engineering or SQL knowledge for advanced use. Compute and AI credits can also introduce usage costs. Buyers should assess connector support, data permissions, credit allowances, compute requirements, and whether their team wants a technical analytics environment.
For FutureStack, Hex belongs in Research because its primary buyer intent is analyzing business and product data to make better decisions. It fits founders who want deeper analytical workflows than a simple dashboard can provide. Buyers should verify data-source compatibility and expected compute and AI usage before choosing a plan.
Use Cases
Product and growth analysis
Explore product, revenue, and growth data with SQL, Python, notebooks, and AI-assisted analysis.
Interactive reporting
Turn analytical work into interactive apps and dashboards that stakeholders can explore without rebuilding the analysis.
AI-assisted data exploration
Use notebook and analytical agents to investigate datasets and accelerate repetitive analysis tasks.
Scheduled analytics
Automate recurring runs, alerts, and reporting workflows so important metrics can be monitored continuously.