Infrastructure for Answers
The data warehouse built for answers, in SQL or natural language.
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Data Warehouse + AI
Hypertenancy Data Warehouse
Scale per-user compute nodes independently, serving sub-second latency without resource contention.
MotherDuck MCP Server
Turn natural language questions into accurate, traceable SQL queries with fully sandboxed compute.
Who is it for?
Analytics that works for everyone
Who is it for?
Analytics that works for everyone
Use Cases
Data Warehousing
Is your data all over the place? Start making sense of your business by bringing it together for internal business intelligence and analytics. Build pure SQL pipelines, share data and quickly collaborate with your team.
Customer-facing Analytics
Unlike traditional BI, customer-facing analytics is built directly into your product for end users. It delivers near real-time, low-latency insights at scale — think milliseconds, not minutes — and must handle thousands to millions of concurrent queries. MotherDuck's architecture, from per-user tenancy to Wasm support, is designed for the unique requirements of Customer-Facing Analytics to drive increased user engagement directly in your app experience.
How We Scale
Duckling Sizes
MotherDuck’s per-user tenancy model gives each user an isolated
A Duckling is a dedicated DuckDB instance for each user, ensuring optimal performance and scalability in data analytics.
Pulse
Our smallest instance, perfect for ad-hoc analytics tasks
Standard
Built to handle common data warehouse workloads, including loads and transforms
Jumbo
For larger data warehouse workloads with many transformations or complex aggregations
Mega
An extremely large instance for when you need complex transformations done quickly
Per-user tenancy and vertical scaling
MotherDuck employs a per-user tenancy and vertical scaling strategy. Users connect to their own MotherDuck Ducklings (DuckDB instances), which are sized (pulse, standard, jumbo, mega, giga) to meet their specific needs. There is also the option for additional Ducklings, through read scaling (explained below), to ensure flexible resource allocation. Ultimately, each Duckling establishes a connection with the central Data Warehouse storage.
Read Scaling
MotherDuck's read scaling capabilities allow users to connect via a BI Tool to dedicated Ducklings that function as read replicas. These read replicas can be provisioned in various sizes (pulse, standard, jumbo, mega or giga) to accommodate different needs. Ultimately, these read replicas connect to the Data Warehouse storage, enabling efficient handling of read operations.
Ecosystem
Modern Duck Stack
CLOUD DATA WAREHOUSE
Sources
Business Intelligence
Ingestion
Data Science & AI
Reverse ETL
Transformation
Dev Tools
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