The 7 best Mode alternatives for 2026

Mode sits in a specific corner of the analytics stack: BI built around data teams. It combines SQL, R, Python, visual analytics, reports, dashboards, reusable datasets, embedded analytics, and custom data apps in one workspace. That made it popular with teams that wanted analysts to move from ad hoc SQL to stakeholder-facing reports without jumping across too many tools.

Mode defined a real category. It put SQL at the front of the workspace, kept Python and R notebooks alongside for deeper analysis, generated visual reports and dashboards on top of that work, and made the whole thing schedulable and shareable. Analyst teams ran weekly business reviews out of it. That product is now folded into a broader BI platform, which is the right answer for some teams and the wrong answer for others.

But Mode has also changed. ThoughtSpot completed its $200 million acquisition of Mode Analytics and ThoughtSpot later launched Analyst Studio as a creator space for data teams, with SQL, Python, R, spreadsheets, data prep, and advanced analytics tied into the broader ThoughtSpot platform.

The alternatives in 2026 split into a few camps. Some are notebook-first workspaces that have absorbed Mode’s analyst workflow and added stronger AI on top. Some are direct successors in spirit: same shape, modernized. Some are canvas-based products that re-imagined how analysts and stakeholders work together. And some are BI platforms that learned from Mode’s code-first philosophy without being it. Where you land depends on which part of Mode you actually want back.

Quick comparison

The tools below fall into three rough groups: notebook-first analytics workspaces, modern BI platforms that learned from Mode, and the literal successor product.

Deepnote

Deepnote is the right alternative when the part of Mode you want back is the analyst workflow itself, but the rest of the product has changed in ways that don’t fit how your team works now. Mode treated the notebook as a companion to the SQL editor and the dashboard (embeds notebooks inside reports); Deepnote starts from a different assumption, where the notebook is the shared runtime that data, code, execution history, and context all live in, and where humans and AI agents work together on the same artifact. SQL, Python, and R all sit inside the same workspace, with native connectors to Snowflake, BigQuery, Databricks, Redshift, and the rest of the warehouse stack so analysis happens close to the data rather than across tools.

The structural differences from Mode matter for teams thinking about the next five years rather than the last five. Deepnote is open source, with a portable .deepnote YAML format that replaces messy notebook JSON with clean, Git-reviewable structure. The same artifact can be edited collaboratively in real time, executed headlessly through an API, scheduled, connected to Slack, or turned into a data app or autonomous workflow. The @deepnote/mcp server and @deepnote/convert CLI extend that further: notebooks become addressable resources that external tools, agents, and pipelines can interact with directly, and they convert cleanly to and from .ipynb, Quarto, and marimo.

Deepnote Agent operates at the project level. It can edit Python, SQL, and text blocks, execute code, inspect outputs, and adapt across the whole notebook rather than completing one cell at a time. That’s a meaningful shift from Mode’s AI features, which sat as suggestions next to a SQL editor. In Deepnote, the agent is a collaborator on the analysis itself, not a co-pilot for one query.

Pros:

• Better fit when notebooks need to become shared, reviewable, reusable artifacts.
• Stronger Python-first and notebook-first workflow than Mode’s report-embedded notebook layer.
• Deepnote Agent works across notebook/project context, not just isolated prompts.
• Unlimited viewers help teams share analysis broadly without turning every stakeholder into a paid builder.

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
• GPU and higher-spec machines require a paid tier
• The free plan caps at 3 editors and 5 projects; teams grow out of it quickly
• Some teams want deeper native support for building production AI applications outside the notebook surface

Hex

Hex is another close Mode alternative product in this list. It’s a SQL-first analytics workspace with notebooks alongside, charts and reports on top, scheduling and Slack delivery built in, and a two-audience model where analysts work in notebooks and non-technical users get a chat surface (Threads) on top. For a Mode user who wants the same workflow modernized rather than rethought, Hex is the natural first stop.

Hex Magic gives analysts AI assistance for SQL and Python, the component library makes reusable analyses easier than Mode’s reports ever did, and the data app publishing model is more capable than Mode’s report builder. The same constraints that send Hex users looking at their own alternatives apply here: per-editor pricing that adds up (editors and viewers both bill), pre-approved Python packages with no path to upgrade core libraries, no open internet access for data pulling, and a proprietary notebook format. Mode users coming from a tool they were also locked into may find this familiar in less good ways.

Pros:

• Strong fit for analysts who like Mode’s SQL-to-report workflow but want a more modern AI analytics surface.
• Hex Magic provides AI assistance for analysts; Threads gives non-technical users a chat interface on top of existing notebooks
• Strong component library and reusable patterns for teams building recurring analyses
• Data app publishing is meaningfully more capable than Mode’s report builder

Cons

• Per-editor pricing that scales editors and viewers both; the per-seat tax grows alongside the audience for any internal report
• Pre-approved Python packages with no path to upgrade core libraries; no open internet access for data pulling
• The two-audience split (notebooks for analysts, Threads for business users) means non-technical users still escalate to the data team when questions go beyond what the agent produces
• Proprietary notebook format makes migration harder than it should be

Count

Count is the canvas-based alternative. Where Mode (and Hex) are vertical scrolls of cells, Count is a 2D whiteboard where SQL queries, Python cells, charts, and notes are arranged spatially with explicit lineage between them. The metaphor maps surprisingly well to how analysts actually think: you can branch off into exploratory tangents, keep multiple lines of inquiry visible at once, and see how one query feeds another without losing context. Stakeholders can join the canvas and add sticky notes or comments rather than waiting for a finished report.

Count has built out the parts that matter for Mode-style analyst work: a governed semantic layer (Count Metrics) for reusable metric definitions, an agent that lays out analysis in the canvas with full auditability, dbt integration for importing and debugging models, and schedules that deliver canvases and cells over email or Slack. The pricing model is unusual in this category: editors are billed but viewer seats are included in every tier, which removes the per-seat tax that grew alongside Mode and now grows alongside Hex.

Pros:

• Canvas-based UI makes branching, parallel exploration, and lineage visible in a way linear notebooks struggle to express
• Real-time collaboration is central to the product; stakeholders can join the canvas rather than receiving finished reports
• Viewer seats are included in every tier; pricing scales editors only, similar to Deepnote’s model
• Local DuckDB execution and in-browser caching reduce warehouse load and cost for exploratory work

Cons:

• Smaller community and ecosystem than Hex or Mode; fewer integrations, less third-party tooling
• Out-of-the-box dashboarding is still maturing; teams whose work is dashboard-heavy may find the canvas paradigm a less direct fit
• Performance with very large datasets can lag; the browser-execution model has limits
• The canvas model is genuinely different from the linear notebook flow Mode users will be used to, and takes some adjustment

Omni

Omni is the modern BI alternative that took Mode’s code-first analyst philosophy seriously rather than dismissing it. Where most BI platforms drag analysts away from SQL and into a drag-and-drop interface, Omni keeps SQL, a modeling IDE, Git integration, and dbt workflows as first-class options for the data team, while giving business users dashboards, spreadsheets with Excel formulas, point-and-click exploration, and AI chat. All of it runs on a shared governed semantic layer, so metrics stay consistent across surfaces.

The piece worth understanding for a Mode user evaluating Omni: where Mode kept SQL and notebooks as the analyst’s domain and shipped reports out the other side, Omni unifies both audiences on the same model. Analysts can build measures during exploration and promote reusable logic to the shared semantic model, so the governed foundation grows through everyday work rather than top-down mandates. The two-way dbt integration pushes changes back into dbt instead of only syncing one way, which is a meaningful upgrade over how Mode handled the BI-to-warehouse loop. Customer logos skew toward organizations that wanted governed BI without losing the analyst-friendly workflow Mode had built its reputation on.

Pros

• SQL, modeling IDE, Git integration, and dbt workflows are first-class for analysts; spreadsheets, dashboards, and AI chat sit alongside on the same semantic model
• AI queries the governed semantic layer rather than generating raw SQL on the fly, which gives business users answers they can inspect and trust
• Two-way dbt integration pushes metric definitions back into dbt; analysts don’t have to edit logic outside the BI layer
• Branch mode, content validator, and Git-native version control bring software-engineering discipline to the BI layer

Cons

• Not a notebook product; teams whose work centers on Python or R exploration will find Omni more constrained than Deepnote or Hex
• Per-user pricing through enterprise contracts; the savings story comes from consolidating tools rather than from a cheaper per-seat price
• Strongest fit when data already lives in a warehouse Omni connects to natively; less compelling if data is fragmented across operational systems
• Custom visualization options are growing but less mature than what front-end-heavy tools like Observable produce

Metabase

Metabase is the open-source answer when the Mode features you actually use are the SQL editor, dashboards, scheduled subscriptions, and embedded analytics, and the proprietary part of Mode was always the friction rather than the value. Metabase ships a graphical query builder for non-technical users, a native SQL editor with templating and snippets, dashboards with cross-filtering and drill-throughs, and an embedded analytics SDK for putting charts inside customer-facing products. The five-minute self-hosted setup keeps the total cost of ownership well below proprietary alternatives for teams that have the engineering capacity to run it.

Metabase is much less code-heavy than Mode. It gives teams no-code query building, dashboards, SQL, permissions, embedding, and increasingly AI-assisted workflows. The Data Studio adds analytics-engineering tooling (calculated columns, joins, canonical metrics, dependency tracking) that turns Metabase from a pure visualization layer into something closer to a full analytics platform. For teams whose Mode usage was mostly SQL plus dashboards plus subscriptions, Metabase covers that workflow with an open license and a much lower running cost.

Pros

• Open source under AGPL; self-hostable for free with full feature access in the OSS edition
• Metabot AI now ships in the open-source release, with an MCP server for connecting external AI agents
• Strong embedded analytics SDK for putting charts and dashboards inside customer-facing products
• Five-minute setup; the total cost of ownership for technical teams is meaningfully lower than proprietary BI

Cons

• Self-hosting has real overhead; teams without engineering capacity to run it should compare against Metabase Cloud or fully managed alternatives
• Notebook support is limited; this is a BI tool with a SQL editor, not a Python or R analysis environment
• Metabot handles single-level aggregations well, but complex cross-table analysis still requires SQL knowledge
• More fragmented governance model than purpose-built BI platforms with semantic layers like Omni

Sigma

Sigma is the best Mode alternative for warehouse-first teams whose business users still think in spreadsheets. Mode is analyst-led: write SQL, use Python/R when needed, and publish reports. Sigma gives business users a spreadsheet-style interface connected directly to live warehouse data.

Sigma’s spreadsheet product supports formulas, pivots, filters, input tables, writeback to warehouse tables, and audit trails. That is a very different model from Mode: instead of analysts building a report for others to consume, business teams can work directly with governed live data in a familiar interface.

This makes Sigma especially strong for finance, revenue operations, planning, and operational analytics teams that export Mode results into spreadsheets anyway. It is less compelling when the key Mode workload is advanced Python/R analysis or code-first exploration.

Pros:

• Familiar spreadsheet interface for business users.
• Live warehouse data without exports or extracts.
• Input tables and writeback support planning and operational workflows.
• Audit trails help replace spreadsheet chaos with governed collaboration.

Cons:

• Not a notebook environment.
• Python/R-heavy Mode workflows will need another tool.
• Warehouse compute costs still matter because analysis runs against live data.
• Less natural for narrative analytical reports than Mode or Evidence.

Sigma is the right alternative when Mode reports keep turning into spreadsheet workflows after publication.

Evidence

Evidence is the Mode alternative for teams that want reports as code. Instead of building reports in a hosted BI interface, Evidence lets teams build polished data products with SQL, Markdown, and AI development tools. Its website highlights dashboards, data apps, embedded analytics, business reviews, AI chat, and version control/testing as part of the development workflow.

The open-source framework works by generating a website from Markdown files. SQL statements inside Markdown run against data sources, charts and components render from those query results, and templated pages can generate many pages from a single template. Evidence can also be self-hosted on platforms like Netlify, Vercel, or internal infrastructure.

That is not a one-for-one Mode replacement. It is less flexible for business users who want to click around and explore. But it is much stronger for data teams that want version-controlled, reviewable, reproducible reporting artifacts.

Pros:

• Best fit for SQL-first teams that want BI-as-code.
• Reports live in Markdown and Git instead of a proprietary report builder.
• Strong for recurring business reviews, embedded customer analytics, and polished internal data products.
• Open-source foundation gives teams more control over deployment.

Cons:

• Less natural for ad hoc exploration than Mode.
• Requires coding comfort.
• Not a Python/R notebook workflow.
• Business users may need a more interactive BI layer alongside it.

Evidence is the right alternative when Mode reports should behave more like software: versioned, reviewed, deployed, and maintained in code.

How to choose the right fit

The fastest way to narrow this down is to start with the actual reason you’re leaving Mode (or the actual reason you don’t want to follow Mode into ThoughtSpot).

For teams that use Mode as a SQL + Python/R analysis workspace, start with Deepnote. Deepnote is the better fit when the notebook should become a durable, reviewable, schedulable artifact for people and agents. Hex is the closer fit when the team wants a hosted SQL/Python analytics workspace with data apps and AI agents.

For teams that use Mode as a self-service BI layer, start with Omni, Sigma, Metabase, or Preset/Superset. Omni is strongest when governed metrics and semantic-layer AI matter most. Sigma is strongest when stakeholders work best in spreadsheet-like interfaces. Metabase is the simpler open-source BI path. Preset/Superset is the more extensible open-source dashboarding path.

For teams that use Mode as a reporting and data product surface, start with Evidence. It is the cleanest fit when reports should live in Git, move through code review, and deploy like software.

For teams that use Mode for collaborative exploration and stakeholder alignment, start with Count. The canvas model is more useful when analysis branches, decisions need context, and the path to the answer matters as much as the answer itself.

FAQs

Does Mode still exist?

Not as a standalone product. ThoughtSpot acquired Mode in July 2023 and absorbed its features into ThoughtSpot Analyst Studio, which became generally available in early 2025. Existing Mode customers were migrated; new buyers can’t sign up for “Mode” as a separate product anymore.

What is the best Mode alternative for turning analysis into apps?

Deepnote is a good fit when analysis needs to become an interactive output that others can use. Teams can turn notebooks into data apps, dashboards, scheduled jobs, or API-triggered workflows while keeping the code, context, and execution history connected. That is useful when the goal is not just to publish a report, but to make the underlying work reusable.

What's the best Mode alternative for collaborative analyst work?

Deepnote is the strongest fit for teams that loved Mode's code-first SQL and Python notebook surface. It adds real-time collaboration, project-aware AI, Git-friendly file sync, scheduling, API execution, data apps, and integrations with external systems. That makes it useful when the notebook needs to be more than a one-off analysis — when it needs to be reviewed, rerun, automated, or handed off to agents or scheduled jobs.

What is the best Mode alternative if I want to avoid platform lock-in?

Deepnote is a strong fit if portability matters. Notebooks can live in a Git-friendly format, connect to external data systems, and run across workflows without forcing the team into a single BI or analytics platform. Mode was acquired by ThoughtSpot in 2023, and ThoughtSpot launched Analyst Studio in 2025 as the broader path for data teams, so teams evaluating a Mode replacement should decide whether they want to move deeper into a BI platform or adopt a more open notebook runtime.