Steep vs Cube
Side-by-side comparison of AI visibility scores, market position, and capabilities
Copenhagen Denmark BI platform for modern data teams connecting to Snowflake and BigQuery; metric-centric analytics with fast warehouse-native query execution and clean opinionated UI designed as an alternative to legacy BI paradigms for analytics...
About
Steep is a business intelligence and analytics platform founded in 2021 and headquartered in Copenhagen, Denmark. The company was founded by former product and engineering leaders to build a BI tool optimized for the modern data team workflow — fast, warehouse-native query execution, a clean and opinionated UI, and first-class support for the metric-centric analytics workflows that analytics engineering teams are building. Steep positions itself as an alternative to legacy BI tools that carry the weight of decade-old UI paradigms and to overly complex enterprise platforms.\n\nSteep has raised pre-seed funding and operates as a lean, product-focused startup primarily targeting analytics engineering teams in Europe and growing technology companies. Its platform connects directly to Snowflake, BigQuery, and Redshift as the query engine, ensuring that all analysis runs against live warehouse data without intermediate caching layers that can serve inconsistent results. Steep's metric layer allows teams to define business metrics centrally and build dashboards around those metrics rather than one-off SQL queries, promoting consistency in how the company measures performance.\n\nSteep's dashboard experience is designed for both analysts building data products and business stakeholders consuming them, with a clean viewer mode that removes technical noise for non-technical audiences. The platform supports scheduled email and Slack delivery of dashboard snapshots, data alerting for metric threshold monitoring, and embedding for sharing dashboards in internal tools. Steep's European roots and GDPR-compliant data architecture make it a strong fit for European organizations with data residency requirements.
San Francisco CA semantic layer and headless BI platform; raised $100M+; API-first data access layer that sits between warehouses and any BI or AI consumer.
About
Cube is a semantic layer and headless business intelligence platform founded in 2019 and headquartered in San Francisco, California. The company was founded by Artyom Keydunov and Pavel Tiunov to solve the problem of metric proliferation in data-driven organizations: when every BI tool, internal application, and data consumer defines its own metrics independently, companies end up with different answers to the same business question depending on where they look. Cube provides a single semantic layer — a governed data model layer — that defines all business metrics and dimensions once, then serves them consistently to any downstream consumer via REST, GraphQL, or SQL APIs.\n\nCube raised $100 million across multiple funding rounds from investors including Bain Capital Ventures, Decibel Partners, and 468 Capital. Its platform is built on an open-source core (Cube.js) with hundreds of thousands of community users and deployments. The commercial Cube Cloud product adds managed infrastructure, a development environment, testing tools, query caching for performance optimization, and access controls. Cube's API-first, headless architecture allows it to serve metrics to traditional BI tools, embedded analytics applications, internal data apps, and increasingly AI assistants and large language model (LLM)-powered analytics tools.\n\nCube's caching and pre-aggregation engine is a significant technical capability: it automatically builds materialized aggregates from frequently run queries and serves them from a high-performance cache layer, dramatically reducing warehouse query latency and costs for dashboards and embedded analytics applications. This performance layer makes Cube a practical choice for public-facing embedded analytics where end users expect sub-second response times that direct warehouse queries cannot reliably deliver.
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