Kaspr vs Cube
Side-by-side comparison of AI visibility scores, market position, and capabilities
B2B contact data platform for finding phone numbers and emails of prospects. Paris France, acquired by Cognism, provides LinkedIn-integrated prospecting data for European SDR teams.
About
Kaspr is a B2B contact data platform that helps sales development representatives find phone numbers and email addresses of business prospects, with tight LinkedIn integration that allows data extraction directly from LinkedIn profiles. Founded in 2018 and headquartered in Paris, France, Kaspr was acquired by Cognism to strengthen the combined entity's European contact data coverage and distribution through complementary product channels. Kaspr targets SDR teams seeking fast, LinkedIn-native access to prospect contact data.\n\nKaspr's Chrome extension integrates directly into LinkedIn, allowing SDRs to reveal contact details for prospect profiles with a single click without leaving the LinkedIn interface. The platform's contact data covers mobile phone numbers and professional email addresses for European and international business contacts. Contacts can be pushed to CRM systems and sales engagement platforms directly from the extension, reducing manual data entry in the prospecting workflow.\n\nFollowing its acquisition by Cognism, Kaspr operates as a complementary product in the Cognism portfolio — with Kaspr serving individual SDRs and smaller teams seeking affordable LinkedIn-native prospecting tools, while Cognism's enterprise platform serves larger revenue teams with bulk data exports, intent signals, and GDPR compliance infrastructure. The acquisition strengthened Cognism's coverage of French and broader European prospect data while adding a LinkedIn-integrated product channel to its portfolio.
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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