Compare Hex vs. Zerve AI

Hex unites the finest features of notebooks, business intelligence, and documentation into a cohesive and collaborative user interface, establishing itself as a contemporary Data Workspace. It simplifies the process of connecting to various data sources and allows for collaborative analysis via SQL and Python-based notebooks, enabling users to share their findings as interactive data applications and narratives. Upon entering Hex, the Projects page serves as the default landing area, making it easy to access both your own projects and those shared within your workspace. The outline feature offers a streamlined overview of all cells contained in a project's Logic View, where each cell is annotated with the variables it defines. Furthermore, cells that produce visible outputs—such as chart cells, input parameters, and markdown cells—provide a preview of their results. By clicking on any cell within the outline, users can instantly navigate to that specific location in the logic, enhancing the overall efficiency of the workflow. This functionality ensures that collaboration and data exploration are both intuitive and effective.

Zerve is the agentic data workspace designed for anyone who works with data, from solo analysts, data scientists and business users alike. Zerve brings together exploration, advanced analysis, collaboration, and production deployment into a single AI-native environment, so that important data work doesn’t stall, break, or disappear. Zerve is used by data professionals in companies such as BBC, QVC, Dun & Bradstreet, Airbus, and many others. Zerve makes advanced data work accessible, durable, and deployable from day one, starting with the messy, real-world data most projects begin with. At the heart of Zerve is a new way for humans and AI agents to work together. Zerve’s AI agents understand the full context of a project and actively help plan, build, debug, and iterate across multi-step analyses. Agents can assist with tasks like cleaning and transforming data, identifying issues, and testing approaches, reducing the manual effort that slows teams down. This means working at a higher level of abstraction without being slowed by setup or syntax. With Zerve, you always have an expert data scientist at your side, guiding decisions, suggesting next steps, and taking action. Unlike traditional data notebooks, workflows in Zerve are reproducible and stable. Users can work across Python, SQL, and R in a single workspace, connect directly to databases, data lakes, and warehouses, and integrate with Git for version control. The built-in distributed computing engine powers massively parallel execution for large-scale analysis, simulations, and AI workloads, with multi-agent orchestration coordinating complex pipelines behind the scenes. Zerve can be used as SaaS, self-hosted, or even on-premise for regulated environments.