Key Takeaways
- • Sequoia Capital led the seed round for Edelbyte, a data infrastructure company founded by the team that built Kusto, the data engine originally incubated inside Microsoft.
- • Kusto became the data engine underneath Microsoft Sentinel, Microsoft Defender and Azure Monitor, and was offered to customers as Azure Data Explorer.
- • Edelbyte is positioned as a data platform designed for AI agents, which Sequoia argues generate far more queries than human users and are constrained by current cost and speed trade-offs.
- • The platform claims to keep detailed history queryable at interactive speed, unify search and analytics, and run in cloud or on-premises environments; Sequoia cites customers seeing tenfold increases in queries with no budget increase.
Summary
Sequoia Capital has announced its partnership with Edelbyte, a data infrastructure startup founded by four former Microsoft engineers: Avner Aharoni, Evgeney Ryzhyk, Alexander Sloutsky and Yoni Leibowitz. According to Sequoia, the team left Microsoft recently and was introduced to the firm by Roy Reznik, co-founder of Wiz. Sequoia led Edelbyte's seed round.
The founders previously built Kusto, a data engine that began as a small incubation project inside Microsoft more than a decade ago. Designed to return answers from large volumes of fast-arriving data such as logs, telemetry and security events in seconds rather than hours, Kusto used a query language accessible to non-specialists. It became the data engine beneath Microsoft Sentinel, Microsoft Defender and Azure Monitor, and was later offered to customers as Azure Data Explorer. Sequoia says customers, from large enterprises to fast-growing startups, described the technology as game-changing and the team as among the best they had worked with. Sequoia also notes Evgeney Ryzhyk invented the Kusto engine and rebuilt it three times, and that Avner Aharoni spent about a quarter century at Microsoft.
Sequoia frames Edelbyte as a further rethinking of data platforms for the AI era rather than a simple rebuild. The firm argues that existing platforms were designed for human users, whose query volume is naturally limited, and that their trade-offs, including limited history retention, restricted query volume to control costs, and single-cloud product placement, become constraints when AI agents run many concurrent queries. Sequoia cites an example of one agent firing fifty queries where a person would run one.
According to the article, Edelbyte keeps detailed history queryable at interactive speed, scales with data volume and query demand, provides governed access to the same data for people, applications and AI agents, and combines search and analytics in a single platform that can run in the cloud or on premises, managed or self-hosted. Sequoia states that customers are seeing tenfold increases in queries with no increase in budget. The firm also argues that data platforms are durable, high-value businesses because data accumulates and rarely moves. The article does not disclose the seed round size or valuation.
The founders previously built Kusto, a data engine that began as a small incubation project inside Microsoft more than a decade ago. Designed to return answers from large volumes of fast-arriving data such as logs, telemetry and security events in seconds rather than hours, Kusto used a query language accessible to non-specialists. It became the data engine beneath Microsoft Sentinel, Microsoft Defender and Azure Monitor, and was later offered to customers as Azure Data Explorer. Sequoia says customers, from large enterprises to fast-growing startups, described the technology as game-changing and the team as among the best they had worked with. Sequoia also notes Evgeney Ryzhyk invented the Kusto engine and rebuilt it three times, and that Avner Aharoni spent about a quarter century at Microsoft.
Sequoia frames Edelbyte as a further rethinking of data platforms for the AI era rather than a simple rebuild. The firm argues that existing platforms were designed for human users, whose query volume is naturally limited, and that their trade-offs, including limited history retention, restricted query volume to control costs, and single-cloud product placement, become constraints when AI agents run many concurrent queries. Sequoia cites an example of one agent firing fifty queries where a person would run one.
According to the article, Edelbyte keeps detailed history queryable at interactive speed, scales with data volume and query demand, provides governed access to the same data for people, applications and AI agents, and combines search and analytics in a single platform that can run in the cloud or on premises, managed or self-hosted. Sequoia states that customers are seeing tenfold increases in queries with no increase in budget. The firm also argues that data platforms are durable, high-value businesses because data accumulates and rarely moves. The article does not disclose the seed round size or valuation.
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