Introducing Mora
Metrics definitions
and lineage
Build a shared metrics layer on top of your raw data: define revenue segments, custom KPIs, and business logic in one place. Every dashboard, query, and alert uses the same definitions. No more “which MRR number is right?”
Three steps
no engineers required
01
Everything you need
in one single platform
Auto-detected models
Mora scans your connected sources and proposes a starter data model, detecting common SaaS patterns like Stripe subscriptions, Salesforce opportunities, and standard product events. Accept, modify, or override.
Visual modeler
Define relationships between tables, create calculated fields, and build metric definitions using a visual interface. Drag connections between sources, define joins, and see the model update in real time.
Metrics layer
Define KPIs as first-class objects: MRR, NRR, churn rate, CAC payback, LTV, with explicit formulas, filters, and time grain. Every downstream consumer uses the same definition.
Segments and dimensions
Define customer segments (enterprise, mid-market, SMB), product tiers, geographic regions, and any other business dimension once, and slice every metric by those dimensions automatically.
dbt compatibility
Already using dbt? Mora detects and respects your existing dbt models and definitions. Layer Mora’s metrics on top without replacing your transformation pipeline.
Version control and change tracking
Every model change is versioned. See who changed what, when, and why. Roll back to any previous version. Compare metric values before and after a model change.
Built for the moments
that drive revenue
Metric consistency
Your CEO, your VP of Sales, and your CFO all see the same MRR number, because it’s defined once in Mora’s model layer, not calculated differently in three spreadsheets.
Onboarding new data sources
Connect a new tool and model it in minutes. Define how its data maps to your existing metrics and it’s instantly available across all dashboards and queries.
Business logic centralization
“A churned account is one with no active subscription and no payment in 90 days”: define it once, and every churn metric in Mora uses that definition.
Self-serve analytics enablement
Non-technical users can query confidently because the model layer handles the complexity. They ask questions using business terms, not table names.
Model definition formats
Visual modeler, YAML configuration files, or auto-detection from source schemas
Supported source types
Any connected data source: SaaS APIs, databases, warehouses, file uploads
dbt integration
Auto-detects dbt models, sources, and metrics; compatible with dbt Core and dbt Cloud
Metric types
Simple (sum, count, average), derived (calculated from other metrics), cumulative, period-over-period
Caching
Computed metrics are cached with configurable TTL; cache invalidation on source data changes
Access controls
Model-level permissions; define who can view, edit, or administer the data model