Why teams choose Sigma vs Palantir

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).

Semantic Portability

Protect existing investments. Sigma natively integrates with dbt and Snowflake Semantic Views, allowing externally built metrics to flow directly into Sigma without re-definition.

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

Palantir Foundry

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.

Requires massive, upfront architectural lift. Before any analysis or AI workflows can begin, expensive engineering teams must map your existing data into their proprietary closed shadow environment.

Required skills

Sigma democratizes AI Apps and Sigma Agents by empowering users to build with familiar spreadsheet functions, standard SQL, or Python on a single, governed canvas.

Built for highly technical users and costly Forward-Deployed Engineers (FDEs). Requires writing raw code to define business logic and deploy basic operational workflows.

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.

Enforces a deeply fragmented, siloed development environment. Data engineers are forced to build the backend logic in isolated code repositories, while business users are trapped in rigid, pre-compiled front-end applications. This structural divide destroys agile iteration and completely breaks the feedback loop.

Collaborative Workflows

Sigma's architecture delivers synchronous, multi-user state management, agentic collaboration, and human-in-the-loop governed AI. Multiple users can build, edit, and explore the same live workbook simultaneously without locking files or overwriting work. This shared environment natively supports approval chains, allowing teams to securely review and approve Sigma Agent actions before execution.

Replaces live collaboration with a rigid, onerously slow and expensive ticketing system. Logic is buried in code rather than a shared workspace so business users cannot dynamically explore data or adjust workflows together at all. Modifying an approval chain requires an engineer to completely rewrite and redeploy the app. Innovating at the pace of your own business is impossible.

Security

Sigma's zero-copy architecture natively inherits warehouse Row-Level Security (RLS) and Column-Level Security (CLS) via OAuth passthrough. No data extraction, and zero duplicate permission models to manage.

Forces data extraction into a proprietary environment, breaking your warehouse security boundary. You must manually rebuild and continuously sync your Row-Level and Column-Level Security (RLS/CLS) from scratch, creating a massive, redundant compliance burden and exposing your organization to unnecessary risk.

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.

Operates as a closed, proprietary AI ecosystem. You cannot freely arbitrage LLMs or easily integrate external agent frameworks via open standards. You are locked into their specific model configurations, heavily dependent on their roadmap, and blocked from leveraging emerging best-of-breed AI in the public marketplace.

Semantic Layer Integration

Natively integrate with dbt and Snowflake Semantic Views so centrally defined metrics flow directly into AI Apps and Sigma Agents without re-definition. This shared semantic foundation ensures automated actions execute with governed precision and complete auditability.

Completely ignores your existing semantic investments. You cannot natively integrate dbt or Snowflake models; you are forced to extract data and manually rewrite all your business logic into their proprietary closed shadow infrastructure, creating duplicate maintenance and trapping your IP in their platform.

Schema Resilience

Columns dropped, added, or renamed in a Sigma data model are automatically detected without breaking downstream content.

Highly brittle schema management. If your underlying data warehouse schema changes, the proprietary links within their Ontology break. Fixing these broken downstream operational workflows requires submitting tickets to expensive specialized developers to manually remap and redeploy the affected applications.

Direct Governed Writeback

Capture inputs and trigger workflows safely via Input Tables. Every user writeback and Sigma Agent action automatically inherits your cloud data warehouse’s native permissions and security policies, ensuring absolute enterprise governance.

Does not write natively to your cloud data warehouse. All inputs and decisions are written back to their proprietary system first. Syncing this operational data back to your warehouse requires engineering custom, expensive-to-maintain data pipelines that completely break the governed single source of truth.

Data Caching

Utilizes warehouse caching mechanisms to securely enhance performance and reduce query times.

Forces data extraction into their proprietary environment to achieve performance. This creates stale data silos, violates the single source of truth, and forces admins to manually rebuild and sync warehouse security policies from scratch.

Spreadsheet Interface

Familiar spreadsheet UI for accessible analysis and no-code AI App building without added licenses or waiting on IT.

Replaces accessible spreadsheet exploration with a highly technical, rigid application interface. If a business user wants to pivot data or build a new agentic workflow, they must depend on specialized developers writing raw code.

SQL Editing

Full SQL editor lets analysts write queries, perform ad-hoc analysis, and share results in the same workbook.

Traps SQL logic in the backend. Business applications are pre-compiled and rigid. Analysts cannot dynamically inject or edit SQL on the fly within the app to answer sudden operational questions from stakeholders.

Python Editing

Combine SQL, Python, and spreadsheet formulas in one secure canvas. Technical teams can build advanced Python logic that all users can easily pivot and explore.

Silos advanced analytics. Python logic is buried in proprietary repositories rather than existing on a shared canvas. This breaks the feedback loop, forcing data scientists and business users to collaborate over screenshots and meetings.

Lineage

Visual lineage traces data origin and transformations at data-element level.

Obscures true data lineage beneath their proprietary closed shadow infrastructure. Because data is extracted and mapped to custom objects and links, tracing an operational decision back to the original warehouse schema becomes a complex, engineering-heavy forensics project.

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.

Resolving issues or adapting workflows requires massive reliance on expensive, long-term Palantir Forward-Deployed Engineers (FDEs) or third-party consultancies.

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A book cover that says Microsoft Power BI.

Comparative Insights: Sigma and Microsoft Power BI

True enterprise AI shouldn't require surrendering your data to Palantir or piecing together Microsoft's fragmented BI ecosystem. Download our eBook to learn how Sigma empowers business teams to build governed AI Agents natively on your cloud data warehouse.

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