MetricFlow may suit data teams using dbt who want to define reusable metric logic and generate SQL across dimensions.
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Best Semantic Layer Software 2026
Semantic layer software helps teams work with shared definitions for business metrics and data relationships in analytics. Compared on certified models, governed metrics, and dimensions and joins, the entries also include differences in row-level security, deployment model, and query API. Free-plan availability and paid-from pricing help you consider access and cost. Cube, Starburst Enterprise Context Layer, and dbt Semantic Layer are among the software shown. Consider how your team defines metrics, which access controls it needs, and how users query data within your environment. The listed capabilities provide points of comparison for assessing how each option may fit your analytics workflows.
Starburst Enterprise Context Layer
It is aimed at teams that need governed data products and controlled access across analytics and AI use.
WebAPISelf-hostedCube
Cube may suit teams building internal business intelligence or embedded analytics, particularly those seeking governed definitions shared by dashboards and AI agents.
WebLinuxAPI+1dbt Semantic Layer
It suits data teams that need governed metrics across analytics, embedded applications, or AI use cases.
WebBrowser extensionAPISema
Sema describes itself as built for enterprise and regulated teams, including organizations with warehouses containing thousands of tables.
WebAPISelf-hostedTableau suits teams that need to build and share dashboards, visualizations, or data apps.
It is presented for data engineers, developers, analysts, decision makers, and enterprises that need centralized metric definitions.
Malloy fits SQL users who need reusable semantic models, governed metrics, nested analysis, and database-specific SQL generation.
Holistics is intended for data teams building governed models and business users who need to explore datasets or create reports without SQL.
It suits teams that want connectors, data pipelines, modeling, BI, and AI-assisted analysis in one platform.
Looker may suit organizations that want governed data exploration, dashboards, embedded analytics, and natural-language queries.
Verdonz suits teams that want to investigate business data and workspace documents with governed metrics and approval before actions that change something.
Strata is aimed at teams opening warehouse data to non-technical users and AI agents.
It suits organizations in financial services, manufacturing, and retail that need shared business metrics and analytics across BI tools.
It suits organizations seeking shared metric definitions and row-level access controls that can run a self-hosted project.