Why Omni over Hex?

Both Omni and Hex have AI features designed to help business users get answers without waiting on the data team. The difference is in the foundation those answers are built on.

Hex's AI generates SQL and Python on the fly. It can reference a semantic model if one exists, but the AI isn't required to go through it. When an answer looks off, non-technical users have no governed model to trace it back to.

Omni's AI queries your semantic layer — the same governed metrics, joins, and business logic behind your dashboards. Every answer is consistent and auditable from the UI. Business users can inspect the underlying query, drill into fields, and trust the results.

Hex started as a notebook platform. Analysts and data scientists are comfortable there, and recent additions (Threads, the Explore UI, Magic AI) have extended it toward self-service. But the underlying experience is still code-first. Business users who live in spreadsheets often hit a ceiling and route work back to the data team.

Omni was built for both audiences from the start. Analysts get SQL, a modeling IDE, Git integration, and dbt workflows. Business users get dashboards, cross-filtering, drill-throughs, spreadsheets with Excel formulas, and an AI agent — all grounded in the same semantic layer. Everyone works in the same workbook without handoffs.

Hex can sync models and metrics from dbt Cloud (not dbt Core), but the integration is one-way. Changes made in Hex don't push back into dbt, which means analysts often edit logic outside the BI layer to keep things in sync.

Omni's dbt integration is two-way. You can push metrics developed in Omni back to dbt to make them universally accessible, and dynamic schemas let you switch between dev and prod environments in a click to test changes safely. Omni works with both dbt Cloud and dbt Core.

Hex covers the basics — SSO, SCIM, permissioned projects — but lacks native row-level or column-level security in the BI layer. Access control relies on warehouse permissions or manual workarounds, which creates governance gaps as more users and sensitive data enter the platform.

Omni has row and column-level security defined natively in the semantic layer, content approval workflows with drafts and reviews, user attributes for personalized access, and embedded analytics with full multi-tenant governance. These controls apply consistently across dashboards, workbooks, AI, and the API.

Hex syncs notebook projects to GitHub and has version history, with optional PR workflows for publishing apps. But there's no Git-native version control for the semantic layer itself, and no branching of the full analytics environment.

Omni treats the semantic model like a codebase. Branch mode gives every developer a safe sandbox to test changes against real content, Git integration provides full version control, and the content validator surfaces broken references before users see them. You develop analytics the way you ship code.

Omni's AI is built on the same semantic layer that powers your dashboards — so every AI answer uses governed metric definitions, joins, and access controls your team already maintains. Business users can ask questions in natural language, get verifiable answers, and then keep going: switching to spreadsheets, SQL, or point-and-click without leaving the workbook. Row-level security applies to AI results automatically, so users only see data they're authorized to see. And AI is included for every Omni customer — no premium tier required.

The result is fewer ad-hoc requests, because users can self-serve confidently. When AI answers a question, the user is not stuck — they can pivot, filter, drill down, or build on the result using familiar tools. That's what actually deflects requests from the data team: not just answering the first question, but enabling the follow-up.

Hex's AI generates and executes SQL and Python code, with reliability guided by endorsed tables, workspace rules, and context docs. Threads (conversational AI for non-technical users) is only available on Team and Enterprise plans, and AI compute is billed on top of seat costs. Users can ask follow-up questions or open results in the Explore UI, but there's no spreadsheet with Excel-style formulas and no unified workbook where AI results and manual analysis coexist. When follow-up goes beyond what the agent produces, it routes back to the data team.

Omni's two-way dbt integration lets you push metric definitions from Omni back to dbt, keeping your BI layer and warehouse in lockstep. Dynamic schemas let you switch between dbt dev and prod environments to safely test how model changes affect dashboards and AI before anything goes live. Branch mode gives every developer an isolated sandbox of the full analytics environment. And the content validator automatically catches any workbooks or dashboards broken by upstream dbt changes — so you do not have to find out from your users. Omni integrates with both dbt Core and dbt Cloud.

Hex pulls metadata from dbt Cloud and integrates with the dbt Semantic Layer. This is useful for surfacing context and querying governed metrics inside notebooks. But Hex cannot push changes back to dbt, does not offer dev/prod environment switching for the BI layer, and has no content validation tooling for catching breakage after upstream changes. Hex's dbt metadata integration also requires dbt Cloud, but dbt Core users do not get the same depth of integration.

dbt is excellent for defining core data models that need to be versioned, tested, and reused across products. But not every use case fits cleanly into a dbt deploy cycle. In practice, business teams constantly need new metrics, quick calculations, or ad-hoc slices to answer time-sensitive questions. When every change requires a dbt update and deployment, the data team becomes a bottleneck and iteration slows down.

Omni's semantic layer is complementary to dbt, not a replacement. It lets analysts define reusable logic and combine it at query time — period-over-period comparisons, ratios, filtered aggregations — without waiting on a dbt deploy. When an ad-hoc metric proves valuable, it can be promoted to Omni's shared model or pushed back to dbt so it's universally accessible. This flexibility is what makes just-in-time modeling practical. The semantic layer evolves through everyday work, not just top-down mandates.

Omni's semantic layer also creates the foundation for trustworthy AI. You can add descriptions, AI context, synonyms, and field-level instructions that inform how Blobby interprets questions — all of which can draw on metadata already defined in dbt. Without a BI-layer semantic model, AI tools are left generating raw SQL against your warehouse schema, with no governed abstraction to keep answers consistent.

Hex integrates with the dbt Semantic Layer via MetricFlow and can sync models from dbt, Snowflake, or Cube. This gives Hex users access to governed metrics inside notebooks. But Hex's own semantic layer is newer and more limited in depth — it lacks support for extensions, aggregate awareness, user attribute integration, and complex join topologies that teams need at scale. And because Hex cannot push logic back to dbt, insights built in Hex stay in Hex unless your team manually ports them.

With Omni, no. Business users get AI chat, spreadsheets with live data, Excel-style formulas, point-and-click exploration, and governed dashboards. Technical users get SQL, a model IDE with Git integration, API access, and an MCP server for using Omni's AI in any external tool. Everyone works on the same semantic layer, so metrics are consistent regardless of how someone accesses the data. When a business user builds something useful during exploration, they can promote it to the shared model — so the governed foundation grows through everyday work. And you can build beautiful custom charts directly in Omni with Markdown visualizations and CSS dashboard controls, or let AI build them for you.

Hex is built for technical users first. Notebooks are excellent for data scientists and analysts who write SQL, Python, and R. Threads provides a conversational AI interface for non-technical users, but once those users want spreadsheet formulas, governed shared metrics, or a unified workbook alongside dashboards, there's no path forward without the data team. In practice, many Hex customers still need a separate BI tool for their business users, which fragments metrics and governance across two platforms.

Omni's embedded analytics carry the full governance model into your product. Row-level security, column-level security, and user attributes all apply in embedded views — so each customer sees exactly the data they're authorized to see, with the same reliability as your internal dashboards. Customization options, including Markdown visualizations, CSS dashboard controls, and white-labeling, let you match your product's brand and deliver a consistent experience.

Hex supports embedding data apps, and they work well for exposing interactive analyses to external users. But Hex lacks native row-level and column-level security, so enforcing per-tenant data isolation in embedded contexts requires additional engineering work at the warehouse level. If your embedded analytics use case requires strict data segmentation across customers — which most do — Omni's native governance removes significant implementation and maintenance overhead.

Omni is designed to free up data team bandwidth. The shared semantic layer ensures consistent metrics across every user and workflow — so your team fields fewer questions about conflicting numbers. Just-in-time modeling lets business users contribute to the model during their analysis, reducing the backlog of "can you add this metric" requests. The content validator catches broken content after upstream changes, so your team is not manually auditing dashboards after every dbt deploy. Omni's intelligent caching delivers fast, fresh results without running every query live — keeping your warehouse costs under control as usage scales. And because AI, dashboards, spreadsheets, and SQL all run on the same governed model, there's only one central system to maintain.

In Hex, the data team is more central to everyday workflows. Notebooks are powerful for analysts, but non-technical users depend on Threads for AI answers and on the data team for anything beyond simple questions. There's no content validator to catch breakage after schema changes, no just-in-time modeling path for business users, and no built-in spreadsheet interface for users who think in rows and formulas. For small teams supporting large organizations, this translates to more ad-hoc requests, more manual maintenance, and less predictable costs. Hex also lacks an intelligent caching layer, meaning most queries hit the warehouse directly — which can increase compute costs as usage grows.