Edelbyte Raises $70M to Build Data Infrastructure for AI Agents

Edelbyte emerged from stealth on November 11, 2026 with $70 million in funding led by Sequoia and Quiet Capital, the company said in its official news listing. The startup is introducing a unified data platform for search, analytics and AI applications.

Edelbyte’s core pitch is consolidation: one platform handles search, analytical queries, vector, graph and geospatial work, removing the need to assemble separate specialized databases. Its product overview places those functions alongside tools for AI agents and governed access to detailed company data.

Founders and Investor Backing

The four co-founders are CEO Avner Aharoni, CTO Evgeney Ryzhyk, VP Engineering Alexander Sloutsky and VP Platform Engineering Yoni Leibowitz. All four are former Microsoft data-infrastructure experts; the company’s about page describes years spent creating and operating systems for extremely large data workloads.

The wider investor roster includes Greenoaks, Swish Ventures, QP Ventures, Cerca Partners and Startpoint Capital in addition to the lead investors named above.

Sequoia partner Bogomil Balkansky described the demand Edelbyte is targeting: “AI agents will ask far more questions of a company’s data than people ever did.”

Architecture and Deployment

That argument shapes the product design. Comprehensive indexing is intended to handle ad hoc queries without requiring customers to anticipate every access pattern, while a built-in knowledge base gives agents context for their searches. The company says this approach keeps detailed history accessible even when query volumes are high.

Customers can deploy Edelbyte in public or sovereign clouds, self-hosted environments or on-premises, and query data where it resides. Listed connections range from Apache Iceberg, Parquet and S3 to Kafka, PostgreSQL and MySQL. For users and agents, integrations include Claude, Codex, Grafana, Tableau and Jupyter.

Edelbyte markets millisecond queries on petabytes of data and a single platform spanning hundreds of petabytes. Its example workloads include spotting suspicious logins and transactions as they happen, serving live figures in customer-facing applications, analyzing streaming device telemetry and querying complete logs and traces for observability.

Usage-Based Pricing

The commercial model is pay as you go without a commitment. On its pricing page, compute for queries and ingestion is billed per vCore-hour, while hot cache and long-term storage are billed per GB-month. Hot cache keeps data close to compute for millisecond queries; long-term storage retains full history at object-storage cost and remains queryable on the same engine. Networking and I/O operations are charged separately at cloud cost.

Security features listed on the same page include encryption in transit and at rest, granular role-based access control and single sign-on through SAML and OIDC. The page also displays compliance badges for SOC 2 Type II and ISO/IEC 27001.

Edelbyte is asking customers to consolidate several kinds of data work on a system designed for both established analytics and agent-driven access. The central test for the new company is whether its usage-based service can deliver the advertised speed and cost efficiency at very large scale while supporting that breadth across cloud and customer-controlled environments.