Why teams choose Sigma vs Hex
AI Ecosystem
Sigma is your OS for live data and AI. Securely unify external agents via MCP with warehouse LLMs and Sigma Agents for natural language discovery and action without vendor lock-in.
Sigma Agents
Turn insights into automated work. Sigma Agents read, write, and trigger external workflows while inheriting warehouse security, ensuring every action is fully auditable.
Secure Governance
AI security must be architectural. Sigma Agents and AI Apps automatically inherit your cloud data warehouse Row-Level Security (RLS) and Column-Level Security (CLS).
Closed-loop Execution
Collapse the insight-to-action loop. Safely write governed decisions to the warehouse via Input Tables and instantly trigger enterprise workflows from a single environment.
AI Applications
Move beyond read-only dashboards. Empower all users to build interactive AI Apps so that they can take action and safely write decisions directly back to your cloud data warehouse.
Enterprise SDLC
Get production-grade controls without the engineering overhead. Sigma isolates draft and live states using connection-aware deployment and version tagging.
AI, Apps, and Agents with all the BI that you expect.
We excel in the cloud
Analyze billions of rows of live warehouse data using spreadsheet formulas you already know. No stale extracts, row limits, or proprietary coding languages. Ask Sigma Assistant if you have a question.
Dashboards built the way you’ve always wanted
Use Sigma Assistant to help you build dynamic, interactive dashboards without writing SQL or waiting on data engineering. Drill down to the underlying row level instantly on live, governed data.
Write directly back to your warehouse
If you know how to use a spreadsheet, you can safely capture data, run live scenarios, and trigger downstream workflows. Deploy Sigma Agents to fully automate those actions with a complete audit trail.
Scale with unmatched performance
Securely embed live analytics and writeback capabilities into your customer portals. Automatically inherit warehouse security for strict multi-tenant data isolation without duplicate permission models.
Sigma is the enterprise leader in self-service analytics and operational workflows.
FEATURE COMPARISON
As of March 30, 2026
Sigma
Hex
Architectural Complexity
One architecture: Live query against the cloud data warehouse. Everything from ad-hoc analysis to Sigma Assistant and Sigma Agents run natively on your existing, governed semantic models without duplicating cloud infrastructure.
Relies on a stateful, kernel-driven notebook architecture with linear, fragile execution. Complex analysis requires loading data into memory-constrained dataframes, forcing manual management of kernel states, execution order, and compute bottlenecks.
Required skills
Sigma empowers domain experts to build Sigma Agents and AI Apps using familiar functions in a spreadsheet UI alongside standard SQL and Python. Teams deploy functional agents and closed-loop apps in days without data engineering bottlenecks.
Demands technical fluency. The underlying mechanics require a strong understanding of dataframes, SQL, and Python. Users that do not know how to code must rely on data scientists or developers to build or modify any dashboard.
Multi-Modal Development
Unifies technical and non-technical users by enabling them to co-create across spreadsheets, SQL, Python, and AI in one workspace. No context-switching, no duplicate data, and no broken governance.
Forces a linear, cell-based notebook workflow. Architected for data science narratives, this rigid, top-to-bottom structure intimidates non-technical users and makes multidimensional, ad-hoc exploration incredibly cumbersome.
Collaborative Workflows
Delivers synchronous, multi-user state management and cross-functional collaboration where business users and data teams co-edit live workbooks together. This shared environment natively supports agile approval chains and real-time operational execution.
Multiplayer collaboration exists, but is practically restricted to technical users who understand notebooks. Business users are relegated to being passive consumers of dashboards, breaking the synchronous co-creation loop between technical and domain experts.
Security
Sigma's zero-copy architecture natively inherits warehouse Row-Level Security (RLS) and Column-Level Security (CLS) via OAuth. No extraction or duplicate permission models to manage. Every user, Sigma Agent, and AI App operates safely under your centralized governance model.
Breaks native warehouse security perimeter because data is extracted into Hex’s compute kernels for processing. Admins need to manually rebuild and maintain duplicate security models within Hex since native governance policies (RLS/CLS) do not automatically apply.
Enterprise Scalability
Easily scales to tens of thousands of concurrent business users conducting massive, ad-hoc explorations directly against the cloud data warehouse without compute bottlenecks.
Struggles to scale due to dataframe memory limits, complex compute profiles, and a steep learning curve that inhibits adoption among anyone unfamiliar with code.
AI Model Flexibility
LLM-agnostic architecture lets you directly connect to OpenAI, Azure OpenAI, or Google Gemini and use any Snowflake Cortex or Databricks hosted LLM including Claude from Anthropic. Future-proof your infrastructure and easily switch models using familiar spreadsheet functions to optimize for cost and performance without needing code to rebuild your workflows.
Lacks a no-code LLM routing layer. To change models, a data scientist must manually rewrite the API integrations within the Python backend, stalling agility and driving up costs. Business users are locked into technical administrator configurations, unable to switch models within workflows to optimize for cost and performance.
Primary User Paradigm
Built for enterprise. Sigma's warehouse-native spreadsheet UI scales to billions of rows, empowering all users to conduct ad-hoc analysis, build AI workflows and agents, and execute decisions without code.
A cloud-native notebook for data teams and coders that excels at Python modeling but relies on a linear, cell-based paradigm. It fundamentally alienates business users, keeping them dependent on data teams for answers.
Democratized Exploration
Every Sigma Agent and AI Application is a starting point. All users can freely drill down, pivot, extract underlying data, and add calculations to shared workbooks without altering the original model or writing code.
Published dashboards are rigid with restricted to predefined filters and input parameters. Users cannot dynamically pivot or drill infinitely into the data without retreating to the code-heavy notebook view.
Schema Resilience
Columns dropped, added, or renamed in a Sigma data model are automatically detected without breaking downstream content. All users can map changes visually so enterprise workflows remain operational without developer intervention.
Notebook architecture creates cascading pipeline failures. Schema changes in the warehouse break the initial SQL extraction cell and halt all downstream Python models. Business users are locked out of the app until developers manually refactor.
Direct Governed Writeback
Native writeback in an approachable spreadsheet UI for data builders and consumers. Input Tables let teams enter decisions, adjust forecasts, and trigger workflows that write instantly back to the warehouse schema without writing a single line of code.
Writeback is a custom engineering project. It requires analysts to write bespoke Python scripts or complex SQL statements within notebook cells. There is no native, governed, no-code equivalent for business users to securely enter data.
Data Caching
True zero-copy architecture. Pushes compute directly down to your data warehouse, leveraging its infinite scale to process billions of rows without memory limits or extraction.
Relies on extracting and loading data into in-memory dataframes to run Python, introducing rigid memory limits. Scales poorly for massive datasets, and creates unnecessary compute costs.
Spreadsheet Interface
Familiar spreadsheet UI for accessible analysis and no-code AI App building without added licenses or waiting on IT.
Operates primarily as a SQL/Python notebook, alienating users that are not technically-fluent, skilled coders.
SQL Editing
Full SQL editor allows data teams to write custom queries, which instantly become explorable elements. All users can pivot, filter, and build AI workflows on top of those SQL results without writing a single line of code.
Offers a SQL cell editor, but it functions as a developer tool. Once the SQL is written, non-technical users cannot dynamically manipulate or pivot the underlying data without returning to the data team to adjust the query.
Python Editing
Combine SQL, Python, and spreadsheet formulas in one secure canvas. Engineers can build advanced Python logic that all users can leverage.
Python notebook environment in Hex isolates advanced analytics from the end-consumers that need to review and act on the data.
Lineage
Visual lineage traces data origin and transformations at data-element level. Business users can instantly audit and verify the underlying math using familiar spreadsheet logic, ensuring complete trust in AI outputs without needing a developer to translate.
Offers a graph of cell dependencies, but true mathematical lineage is trapped in complex Python scripts and SQL blocks. When business users need to audit how a metric was calculated, they hit an intimidating code wall and must rely on developers to understand the data.
In-Product Customer Support
All users have access to live, in-product chat support averaging a 23-second initial response time from a real human, ensuring zero lost momentum.
Level of support depends on pricing plan or licensing tier.
Sigma
Hex
Architectural Complexity
One architecture: Live query against the cloud data warehouse. Everything from ad-hoc analysis to Sigma Assistant and Sigma Agents run natively on your existing, governed semantic models without duplicating cloud infrastructure.
Relies on a stateful, kernel-driven notebook architecture with linear, fragile execution. Complex analysis requires loading data into memory-constrained dataframes, forcing manual management of kernel states, execution order, and compute bottlenecks.
Required skills
Sigma empowers domain experts to build Sigma Agents and AI Apps using familiar functions in a spreadsheet UI alongside standard SQL and Python. Teams deploy functional agents and closed-loop apps in days without data engineering bottlenecks.
Demands technical fluency. The underlying mechanics require a strong understanding of dataframes, SQL, and Python. Users that do not know how to code must rely on data scientists or developers to build or modify any dashboard.
Multi-Modal Development
Unifies technical and non-technical users by enabling them to co-create across spreadsheets, SQL, Python, and AI in one workspace. No context-switching, no duplicate data, and no broken governance.
Forces a linear, cell-based notebook workflow. Architected for data science narratives, this rigid, top-to-bottom structure intimidates non-technical users and makes multidimensional, ad-hoc exploration incredibly cumbersome.
Collaborative Workflows
Delivers synchronous, multi-user state management and cross-functional collaboration where business users and data teams co-edit live workbooks together. This shared environment natively supports agile approval chains and real-time operational execution.
Multiplayer collaboration exists, but is practically restricted to technical users who understand notebooks. Business users are relegated to being passive consumers of dashboards, breaking the synchronous co-creation loop between technical and domain experts.
Security
Sigma's zero-copy architecture natively inherits warehouse Row-Level Security (RLS) and Column-Level Security (CLS) via OAuth. No extraction or duplicate permission models to manage. Every user, Sigma Agent, and AI App operates safely under your centralized governance model.
Breaks native warehouse security perimeter because data is extracted into Hex’s compute kernels for processing. Admins need to manually rebuild and maintain duplicate security models within Hex since native governance policies (RLS/CLS) do not automatically apply.
Enterprise Scalability
Easily scales to tens of thousands of concurrent business users conducting massive, ad-hoc explorations directly against the cloud data warehouse without compute bottlenecks.
Struggles to scale due to dataframe memory limits, complex compute profiles, and a steep learning curve that inhibits adoption among anyone unfamiliar with code.
AI Model Flexibility
LLM-agnostic architecture lets you directly connect to OpenAI, Azure OpenAI, or Google Gemini and use any Snowflake Cortex or Databricks hosted LLM including Claude from Anthropic. Future-proof your infrastructure and easily switch models using familiar spreadsheet functions to optimize for cost and performance without needing code to rebuild your workflows.
Lacks a no-code LLM routing layer. To change models, a data scientist must manually rewrite the API integrations within the Python backend, stalling agility and driving up costs. Business users are locked into technical administrator configurations, unable to switch models within workflows to optimize for cost and performance.
Primary User Paradigm
Built for enterprise. Sigma's warehouse-native spreadsheet UI scales to billions of rows, empowering all users to conduct ad-hoc analysis, build AI workflows and agents, and execute decisions without code.
A cloud-native notebook for data teams and coders that excels at Python modeling but relies on a linear, cell-based paradigm. It fundamentally alienates business users, keeping them dependent on data teams for answers.
Democratized Exploration
Every Sigma Agent and AI Application is a starting point. All users can freely drill down, pivot, extract underlying data, and add calculations to shared workbooks without altering the original model or writing code.
Published dashboards are rigid with restricted to predefined filters and input parameters. Users cannot dynamically pivot or drill infinitely into the data without retreating to the code-heavy notebook view.
Schema Resilience
Columns dropped, added, or renamed in a Sigma data model are automatically detected without breaking downstream content. All users can map changes visually so enterprise workflows remain operational without developer intervention.
Notebook architecture creates cascading pipeline failures. Schema changes in the warehouse break the initial SQL extraction cell and halt all downstream Python models. Business users are locked out of the app until developers manually refactor.
Direct Governed Writeback
Native writeback in an approachable spreadsheet UI for data builders and consumers. Input Tables let teams enter decisions, adjust forecasts, and trigger workflows that write instantly back to the warehouse schema without writing a single line of code.
Writeback is a custom engineering project. It requires analysts to write bespoke Python scripts or complex SQL statements within notebook cells. There is no native, governed, no-code equivalent for business users to securely enter data.
Data Caching
True zero-copy architecture. Pushes compute directly down to your data warehouse, leveraging its infinite scale to process billions of rows without memory limits or extraction.
Relies on extracting and loading data into in-memory dataframes to run Python, introducing rigid memory limits. Scales poorly for massive datasets, and creates unnecessary compute costs.
Spreadsheet Interface
Familiar spreadsheet UI for accessible analysis and no-code AI App building without added licenses or waiting on IT.
Operates primarily as a SQL/Python notebook, alienating users that are not technically-fluent, skilled coders.
SQL Editing
Full SQL editor allows data teams to write custom queries, which instantly become explorable elements. All users can pivot, filter, and build AI workflows on top of those SQL results without writing a single line of code.
Offers a SQL cell editor, but it functions as a developer tool. Once the SQL is written, non-technical users cannot dynamically manipulate or pivot the underlying data without returning to the data team to adjust the query.
Python Editing
Combine SQL, Python, and spreadsheet formulas in one secure canvas. Engineers can build advanced Python logic that all users can leverage.
Python notebook environment in Hex isolates advanced analytics from the end-consumers that need to review and act on the data.
Lineage
Visual lineage traces data origin and transformations at data-element level. Business users can instantly audit and verify the underlying math using familiar spreadsheet logic, ensuring complete trust in AI outputs without needing a developer to translate.
Offers a graph of cell dependencies, but true mathematical lineage is trapped in complex Python scripts and SQL blocks. When business users need to audit how a metric was calculated, they hit an intimidating code wall and must rely on developers to understand the data.
In-Product Customer Support
All users have access to live, in-product chat support averaging a 23-second initial response time from a real human, ensuring zero lost momentum.
Level of support depends on pricing plan or licensing tier.
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