Hex Alternatives: 5 Options for Analytics Teams

Deepnote, Omni, Sigma, Metabase, and Orion by Gravity are worth considering as Hex alternatives for different jobs: collaborative notebooks, governed BI, spreadsheet-style exploration, query-based dashboards, and shared investigations with recurring reports. Start by identifying which work you need to move. None should be assumed to reproduce every Hex project, app, permission, and schedule.

Imagine a team using Hex for three things: a Python analysis maintained by an analyst, an interactive sales app used by managers, and a weekly revenue explanation. One replacement may suit the Python work but require rebuilding the app. Another may improve the reporting experience while leaving the analyst's notebook in Hex. Those are different buying decisions.

First, check whether Hex already solves the problem

Hex supports more than a code-first notebook workflow. Threads lets business users ask natural-language questions over semantic models and warehouse tables, with an Explorer role or higher. A team seeking conversational analytics should test that experience before assuming it must switch.

Tasks, currently in beta on Team and Enterprise plans, run a saved prompt as a fresh Threads analysis and deliver through Slack or email. Published apps also have a separate scheduled-run path. Replaying established logic and asking an agent to produce a new analysis are different requirements; record which one each recurring job needs.

Context is also part of Hex's offering. Its Context Sync documentation describes bringing externally maintained guides, semantic models, and evaluations into Hex. A concern about maintaining definitions is a reason to inspect this path, rather than assume Hex lacks one.

Hex's October 27, 2026 announcement of Hex 4.0 describes code-backed projects and new ways to review agent-generated work. The announcement labels 4.0 a private beta with broader rollout to follow. Confirm access and the behavior of your own projects before treating announced capabilities as available to every deployment.

Keeping Hex is a reasonable result when its authoring, conversational, and sharing workflows meet your needs. A switch should solve a specific problem large enough to justify the work of moving.

How this shortlist was selected

This is a documentation-based selection guide, not a hands-on ranking or a measured comparison of accuracy, speed, or cost. It covers five different operating models, not every analytics vendor. Product documentation was reviewed in late October 2026; confirm packaging and beta access during evaluation.

Use four selection criteria:

• The work people must finish. Distinguish maintaining analytical code, exploring a model, manipulating a workbook, viewing dashboards, and receiving an explanation. “AI analytics” alone does not identify the job.
• The definitions that must survive. Identify where calculations execute, where additional business rules live, and who can change each. Importing descriptions or translating a model is different from calling an existing metric service.
• The experience recipients need. A shared link, interactive app, scheduled dashboard export, and fresh agent investigation are different deliverables. Test access as the recipient as well as the author.
• The work and cost after launch. Include author and viewer roles, AI usage, compute, hosting, rebuilding, and maintenance. Compare quotes for the same workload rather than assuming the lowest entry price is the cheapest deployment.

If your priority is a broad category overview rather than replacing existing Hex work, see the self-service analytics shortlist.

1. Deepnote: keep a collaborative Python and SQL notebook workflow

Deepnote combines Python, SQL, no-code blocks, AI assistance, and notebook outputs that can be published as apps or dashboards. It belongs on the shortlist when analysts want to keep building and maintaining computational analyses, rather than move primarily to a BI modeling interface.

For an existing Hex team, the important question is whether a representative project remains understandable and maintainable after moving. Bring one with dependencies, warehouse queries, input controls, and a stakeholder-facing output. Recreate it and inspect the results; support for the same languages does not establish app or execution parity.

Deepnote scheduling runs notebooks and supports run notifications. Availability varies by plan, with hourly and custom schedules documented for Enterprise. Check the required cadence, authentication, and failure behavior rather than treating all scheduling features as interchangeable.

Choose this direction when owning the analysis code remains central. It changes the notebook environment; it does not remove the need to maintain the underlying analytical work.

2. Omni: move toward a shared BI model and several exploration interfaces

Omni's documentation describes natural-language analysis grounded in its semantic model, alongside workbooks and dashboards. It also documents AI routines and model-quality tools. Omni is a relevant option when the desired destination is a governed BI platform used by analysts and business users, rather than another notebook environment.

The main project is deciding which existing logic belongs in that destination model. Take a calculation currently embedded in a Hex project and determine how its joins, filters, and business meaning will be represented and maintained. Then test the conversational and dashboard answers against the same reference result.

Consider Omni when you want more analytical work to share one modeled foundation. Budget for model development, content rebuilding, and permission testing. A successful natural-language answer does not demonstrate that a Python-dependent project or an existing shared app has moved with it.

3. Sigma: give spreadsheet-oriented users a warehouse workbook

Sigma workbooks combine tables, charts, pivots, controls, and interactive elements over connected data. This is a useful direction when finance or operations users want to manipulate and explore the numbers themselves in a spreadsheet-style environment.

Moving a Hex app to this workflow is a redesign decision. Identify the interactions users rely on, then reproduce them in a workbook. Validate formulas and data access before comparing appearance. A familiar interface alone does not preserve the analytical behavior of the old app.

Sigma's basics documentation distinguishes reusable data models from workbooks and explains how connection credentials affect query identity. Include both in the evaluation: where shared logic lives, and whose access a recipient uses.

Shortlist Sigma when working directly with tables, formulas, and controls is the missing experience. If recipients mainly want a finished explanation, test whether another workbook would actually change their behavior.

4. Metabase: rebuild around saved questions and dashboards

Metabase questions can be created through a graphical query builder or native SQL editor, then saved, shared, and organized into dashboards. The documentation also describes natural-language question creation through Metabot. Metabase is relevant when most Hex usage is query-and-dashboard reporting and the team wants to evaluate a different BI interface or deployment model.

Its open-source and commercial options make deployment ownership a real decision. Self-hosting transfers operational responsibility to your team; it does not make administration disappear. Confirm the edition needed for your controls. For example, native row and column security is documented for Pro and Enterprise.

Test whether saved questions cover the work your users actually do. If an important Hex project depends on Python, plan a separate home for that computation or a deliberate rewrite. Do not assume moving its final chart moves the analysis behind it.

5. Orion: evaluate shared investigations and recurring analytical outputs

Orion by Gravity lets teams ask questions in plain language and inspect the resulting queries, code, charts, and tables. Analyses are captured as reusable notebooks. It is worth evaluating when the missing job is investigating a business question and turning reviewed work into a recurring output alongside an existing data stack.

Orion Workflows, currently marked v2 Beta, rerun a validated notebook. Output options include reports, dashboards, slide decks, and data exports; native Workflow notification delivery is email only. This differs from Hex Tasks' fresh agent analyses and Slack/email delivery. Choose based on the run behavior and destination you need, not a claim that only one product automates analysis.

Orion is not a drop-in replacement for an analyst's general Python workspace. Keep Hex or evaluate Deepnote when code authoring is the core requirement. Test Orion when business investigations and repeatable analytical deliverables are the unmet need. Verify supported connections, metric execution, and access with your deployment. An Orion notebook being reviewable does not establish identical execution or permissions to a Hex project.

For the focused two-product decision, see Orion by Gravity vs. Hex.

What needs to move besides the notebook?

Before a trial, select one important Hex project and inventory its dependencies. For each item below, name the destination and the person responsible for validating it:

• Calculation and runtime: SQL, Python packages, input variables, data connections, and the result used as the reference. Decide what remains in the warehouse or an existing service and what must be rebuilt.
• Business context: guides, endorsed assets, semantic models, exceptions, and evaluation questions. Separate the authoritative source from a copy imported into the destination.
• User experience: app controls, published links, embedded consumers, and follow-up analysis. Test the interactions people use, not just a screenshot of the final chart.
• Access and identity: author access, recipient access, connection credentials, and restricted data. Repeat the important test as an ordinary user.
• Recurring operation: schedule, run owner, output, recipients, failure handling, and what happens when the owner leaves. Preserve the distinction between rerunning fixed logic and asking a fresh question.

If dbt owns governed metric calculations, keep that role explicit. The dbt Semantic Layer defines and serves metrics centrally. A destination's ability to read dbt metadata is not proof that it executes every question through that service. Ask to see the actual path. The semantic layer and business-context guide explains why both concerns matter.

A switch can be smaller than replacing Hex

Return to the hypothetical team with three workloads. Keep its Python analysis in Hex unless another notebook environment demonstrably serves it better. Evaluate Deepnote for that project. Evaluate Omni or Metabase if the sales app is mainly a governed reporting surface, or Sigma if managers need spreadsheet-style interaction. Evaluate Orion for the recurring investigation and report, while also testing whether Hex Tasks or a scheduled app already meets that requirement.

For each workload, reproduce the reference answer, exercise a real follow-up, inspect access, and run the actual delivery path. Record analyst interventions and failures as well as successes. This is a proposed evaluation, not a completed trial or a promise of migration parity.

Replace Hex only where the destination earns the move. Retain it where it continues to serve the team, and check the cost of operating two systems before choosing a complement. For a wider market view, see AI business intelligence tools and platforms.