Top 7 best Hex alternatives for 2026

Hex sits at a specific intersection: SQL-first analytics with notebook-style cells, AI assistance through Hex Magic, and data apps for sharing results with stakeholders. In practice, the positioning leans more BI than the notebook framing suggests, and the pricing reflects it. For teams whose deliverables are mostly dashboards and reports, that’s a heavy bill for a tool that wasn’t built for governed BI from the ground up.

Teams typically look for Hex alternatives for one of a few reasons. The pricing model charges for both editors and viewers, which becomes costly fast as the audience grows; one published comparison puts a 10-editor, 10-viewer enterprise setup at $27,000 annually. The platform restricts users to pre-approved Python packages with no path to upgrade core libraries, which limits what data science teams can actually do. The one-notebook-per-project structure constrains larger workflows. There’s no open internet access for data pulling or web scraping. And the proprietary format makes migration hard once you’re invested. The right alternative depends on which of those is actually the breaking point for your team.

Quick comparison

The tools below fall into three rough groups: BI platforms built for the whole organization, notebook-and-analytics platforms for data teams, and AI-native analysis tools for people who want the AI to do most of the work.

Deepnote

Deepnote is the closest fit when the limit you’re hitting in Hex is really about data science, AI, and Python-heavy work. Hex started as a SQL-first analytics tool and added Python on top. Deepnote starts from a different assumption: the notebook is the shared runtime where data, code, execution history, and context all live, and where humans and AI agents work together on the same artifact. That difference shows up across a few specific places where Hex teams hit limits.

Python is a first-class citizen rather than a constrained surface. Teams can install any data science library, upgrade core packages, use custom Docker images, and pull data from the open internet without a workaround. That difference matters for teams doing Python-heavy analysis, data science, internal tooling, or agentic workflows. In Deepnote, notebooks can be versioned, scheduled, triggered through APIs, connected to data systems, turned into data apps, and composed into larger workflows. Its open .deepnote format, CLI, MCP server, and file sync also make the work easier to move, review, and connect to other systems than a closed analytics workspace and also makes Deepnote composable with external agents and convertible across notebook formats.

Deepnote Agent operates at the project level, editing Python, SQL, and text blocks across full workspace context rather than completing one cell at a time. The pricing model charges only for editors; viewers are free, which removes the per-seat tax that grows alongside the audience for any internal report or dashboard. A team from Ramp put it directly: “Hex isn’t a complete solution. For more Python-heavy or data engineering work, we always end up going back to Deepnote or VS Code, where the workflow is faster and more flexible.”

Pros:

• Full Python flexibility: any library, any version, custom Docker images, open internet access for data pulling, plus full Spark and Ray support; single-tenant, on-premise, and custom networking deployments are available when teams need them
• Notebooks are executable artifacts: schedulable, API-triggered, and deployable as data apps without leaving the workspace, supporting agent-driven workflows across the full project context
• Editors-only pricing with viewers free and month-to-month commitments available, which scales meaningfully differently from Hex’s per-seat editor-plus-viewer model

Cons:

• Not a BI tool. Teams whose primary need is pivot tables, governed metrics, or dashboard-heavy reporting will find a purpose-built BI platform serves them better
• The free plan caps at 3 editors and 5 projects; teams grow out of it quickly
• GPU and higher-spec machines require a paid tier

For readers comparing the two directly, the Hex vs. Deepnote comparison covers the side-by-side in detail.

Mode (now ThoughtSpot Analyst Studio)

Mode is a good Hex alternative when the team’s center of gravity is still SQL-led analysis and reporting. It combines SQL, Python, R, dashboards, reports, and visual exploration in one platform, and its notebooks connect directly to SQL query results so analysis can stay close to the dataset that produced it (sitting alongside ThoughtSpot’s broader self-service analytics).

For teams that previously chose Mode over Hex, Analyst Studio inherits the code-first surface but ties it to a much larger enterprise platform.

Pros:

• Strong fit for SQL-first analytics teams that also need Python or R
• Tightly integrated with ThoughtSpot’s AI-powered search and Liveboards for non-technical consumers
• Reasonable fit for organizations that wanted one platform serving both data teams and business users

Cons:

• Less aligned with agentic notebook workflows or notebooks as composable artifacts.
• Self-serve BI can be less capable than dedicated BI platforms.
• Strong fit for teams already evaluating ThoughtSpot independently; weaker fit for teams that just want a SQL/Python notebook

Observable

Observable is the right alternative when the use case is really about data visualization and interactive storytelling rather than analytics workflows. It’s built around JavaScript (and increasingly Python through Observable Framework), with reactive cells that re-execute automatically when their inputs change. It is not trying to replace Hex’s full analytics workspace. It is a better fit when the front-end experience matters, the team is comfortable writing code, and the final deliverable needs to feel like a polished data product.

Observable Framework, the open-source static-site generator built on the same primitives, is increasingly how teams use Observable in practice: build interactive dashboards locally, deploy as static sites, version-control the whole thing.

Pros:

• Strongest visualization layer of any tool on this list; reactive cells and D3 integration are genuinely differentiated
• Good fit for teams that want polished, custom interactive visualizations rather than templated charts
• Data loaders allow back-end preparation in SQL, Python, R, and other languages.

Cons:

• JavaScript-first orientation is a real switching cost for SQL or Python-native teams
• Not built around the same notebook-and-app workflow that Hex offers; teams looking for a direct replacement will find the model significantly different
• Smaller community for analytics-style work than for visualization-heavy use cases

Count

Count is the canvas-based alternative. Where Hex is a vertical scroll of cells, Count is a 2D whiteboard where SQL queries, charts, and notes can be arranged spatially, with explicit lineage between them. The metaphor maps better than it sounds for collaborative analysis: you can see how one query feeds another, branch off into exploratory tangents, and keep multiple lines of inquiry visible at once.

Count is smaller and less mature than Hex, but the canvas-first approach genuinely solves a problem that linear notebooks struggle with: keeping context for multi-part analyses where the steps don’t have a single linear order.

Pros:

• Canvas-based UI makes branching, parallel exploration, and lineage visible in a way notebooks struggle to express
• Good for collaborative analysis sessions where multiple people are exploring different threads
• Good fit for teams that want to combine SQL, Python, visuals, and narrative context in one shared space.

Cons:

• Smaller community and ecosystem than Hex; fewer integrations, less third-party tooling
• Python support is more limited than the SQL workflow
• Less mature data app and publishing surface than Hex or Deepnote

Omni

Omni is the right alternative when the question is really about BI for the whole organization rather than a workspace for the data team. Omni starts from a governed semantic layer and builds dashboards, spreadsheets with live data, SQL, point-and-click exploration, and AI chat on top of it, all in the same workbook. It was built from the ground up so business users can self-serve on the same model that powers everything else.

The most useful contrast against Hex is how AI is wired up. Hex’s AI generates SQL and Python on the fly per query, with optional support for a semantic layer alongside it. Omni’s AI queries the semantic layer directly, so every answer uses the same governed metric definitions, joins, and access controls the dashboards already use. When a business user asks a question, they can inspect the underlying query, drill into fields, switch to a spreadsheet with Excel formulas, or pivot into SQL without losing context. The pricing posture is also different: AI is included for every Omni customer rather than billed as a separate compute layer on top of seats. Omni’s customer list (Conde Nast, BuzzFeed, Perplexity, Mercury, dbt Labs, BambooHR) reads heavily as organizations that needed governed BI at scale.

Pros:

• AI queries a governed semantic layer rather than generating raw SQL/Python, so business users get answers they can inspect and trust
• Spreadsheets, dashboards, SQL, point-and-click, and AI chat all share the same workbook and the same semantic model; no handoff between technical and non-technical surfaces

• MCP server exposes Omni’s model to external AI tools like Claude, ChatGPT, Cursor, and VS Code; strong fit for teams already standardizing on agentic workflows around the warehouse

Cons:

• Not a notebook product. Teams whose work centers on Python, R, or notebook-driven data science will find Omni more constrained than Hex or Deepnote
• Per-user pricing through enterprise contracts; the savings story comes from consolidating onto one tool rather than from a cheaper per-seat price
• Strongest fit when the underlying data already lives in a warehouse Omni connects to natively; less compelling if data is fragmented across operational systems

Julius

Julius is the AI-native alternative for teams whose actual question is “can the AI just do the analysis?” It’s a conversational data analysis tool: connect a database, spreadsheet, or warehouse, ask questions in plain English, and Julius writes the queries, generates the charts, and produces the summaries. It supports SQL Server, MySQL, Databricks, Google Sheets, Google Ads, Meta Ads, and direct file uploads, with MCP support for connecting other tools.

Julius is solving a different problem from Hex. Hex assumes a data analyst writing SQL and Python with AI assistance; Julius assumes a business user (or marketer, founder, ops lead) who shouldn’t have to write either. For teams whose Hex usage is mostly “give me a chart of this,” Julius is a more direct path. For teams doing real exploratory data work, it doesn’t replace the notebook surface.

Pros:

• True natural-language interface; non-technical users can produce charts and reports without SQL or Python
• Direct connectors to databases, warehouses, ad platforms, and spreadsheet
• MCP support (and connectors) lets it pull context from external tools like Notion or Intercom

Cons:

• Teams that want a code-first analytical surface will find Julius too restricted
• Enterprise governance, custom Python environments, and reproducibility features are limited compared to Hex or Deepnote
• The chat-first interaction model is faster for one-off questions than for building structured, reusable analyses

Sigma Computing

Sigma is the strongest option for warehouse-first teams whose analysts think in spreadsheets. It runs natively on top of cloud warehouses (Snowflake, BigQuery, Databricks, Redshift) and exposes the data through a spreadsheet-like interface deliberately familiar to business analysts. There’s no SQL-or-nothing barrier: analysts can pivot, filter, and build reports the way they would in Excel, with the warehouse handling the compute.

Sigma is a different bet from Hex. Where Hex is a notebook-style tool with SQL and some Python, Sigma is a cloud-native BI platform built around tables, formulas, and warehouse compute. Teams that mostly write SQL and build dashboards often find Sigma more direct than Hex; teams that need real Python or a notebook workflow will find it more constrained.

Pros:

• Spreadsheet-driven interface lowers the barrier for business analysts who don’t write SQL
• Warehouse-native: queries push down to Snowflake, BigQuery, etc. without intermediate storage
• Strong governance and access controls for teams operating at enterprise scale

Cons:

• No notebook surface; if you wanted Hex for Python or code-first analysis, Sigma is solving a different problem
• Locked into the warehouse-native model; less flexible if your data lives across multiple sources or needs preprocessing in Python
• Smaller community and integration ecosystem than the BI heavyweights

How to choose the right fit

The fastest way to narrow this down is to start with the actual reason you’re leaving Hex.

For teams that need real Python flexibility, AI agents that work across project context, open formats, and pricing that doesn’t tax the audience, Deepnote is the most direct upgrade. A team from Norges Bank captured the technical case: “Hex is fine for light analytics, but not for power users. You can’t control the Python kernel or upgrade core libraries; it quickly becomes limiting for teams deep in the Python ecosystem.”

For teams that wanted Mode and now need to figure out where to go, ThoughtSpot Analyst Studio is the official successor, with the broader ThoughtSpot platform attached. If business users getting their own answers is the actual problem, ThoughtSpot itself is the more direct fit.

For warehouse-first teams whose analysts think in spreadsheets, Sigma is the cleaner answer. For canvas-style collaborative SQL work, Count is the more interesting bet. For visualization-heavy interactive work, Observable is in a category of its own. For teams whose question is really “can the AI just do this for me?”, Julius is the path.

One observation worth naming: a meaningful fraction of teams looking at Hex alternatives have outgrown the assumption that a notebook should be a personal scratchpad that disappears at the end of a session. The work has become collaborative, recurring, and increasingly run by both people and AI agents. The right alternative isn’t always another notebook tool; sometimes it’s a BI platform, a semantic layer, or an AI analyst.

FAQ

What is the best Hex alternative for Python-heavy data science teams?

Deepnote is the strongest fit when the limit you're hitting is Hex's pre-approved Python packages, lack of internet access, or one-notebook-per-project structure. It supports any data science library, custom Docker images, internet access for data pulling, and treats notebooks as composable executable artifacts that humans and agents can both work in.

What is the best Hex alternative for non-technical business users?

Deepnote works well when non-technical users need to consume analysis through shareable data apps rather than write code themselves. Data teams can build the notebook workflow in Python or SQL, then publish interactive apps for stakeholders to explore. If the primary use case is pure BI, governed metric browsing, or spreadsheet-style analysis, tools built specifically for that audience may be a better fit.

What is the right Hex alternative for teams building with agents?

Deepnote is built around notebooks as executable artifacts that agents work with alongside humans. Deepnote Agent operates at the project level across Python, SQL, and text blocks; notebooks can be scheduled, triggered through APIs, or called by external agents through MCP. That makes it useful when agents need to plan, run code, inspect results, and iterate inside the same environment as the data team, with full execution history and Git-based review.