Edelbyte Raises $70 Million to Unify Data for AI Agents

By Amara Osei

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, according to the company.

Edelbyte says one system handles search, analytical queries, vector, graph and geospatial work. The consolidation pitch removes the need to assemble separate specialized databases. Edelbyte says the unified design is intended to serve both established analytics and agent-driven access. Its product overview also places tools for AI agents and governed access to detailed company data alongside those functions.

Comprehensive indexing is designed to handle ad hoc queries without requiring customers to anticipate every access pattern, Edelbyte’s product overview says. A built-in knowledge base gives agents context for their searches. The company says the design keeps detailed history accessible even when query volumes are high.

Who is building Edelbyte?

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

Who backed the $70 million financing?

Sequoia and Quiet Capital led the financing, the company said. The wider investor roster includes Greenoaks, Swish Ventures, QP Ventures, Cerca Partners and Startpoint Capital.

Sequoia partner Bogomil Balkansky framed the demand Edelbyte is targeting: “AI agents will ask far more questions of a company’s data than people ever did.” Edelbyte’s architecture responds with comprehensive indexing and a knowledge base for agent searches.

Where can customers deploy it?

Customers can deploy Edelbyte in public or sovereign clouds, self-hosted environments or on-premises, the company says. They can query data where it resides.

Edelbyte lists the following data connections and user integrations:

• Data connections: Apache Iceberg, Parquet, S3, Kafka, PostgreSQL and MySQL.
• User and agent integrations: Claude, Codex, Grafana, Tableau and Jupyter.

What can customers run?

Edelbyte markets millisecond queries on petabytes of data and a single platform spanning hundreds of petabytes. Its product materials list these example workloads:

• Spotting suspicious logins and transactions as they happen.
• Serving live figures in customer-facing applications.
• Analyzing streaming device telemetry.
• Querying complete logs and traces for observability.

How does usage-based pricing work?

Edelbyte offers pay-as-you-go service without a commitment, according to 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. Networking and I/O operations are charged separately at cloud cost.

Hot cache keeps data close to compute for millisecond queries, the company says. Long-term storage retains full history at object-storage cost and remains queryable on the same engine. The model meters compute, storage and I/O separately.

Which security controls are included?

Edelbyte’s pricing page lists encryption in transit and at rest, granular role-based access control and single sign-on through SAML and OIDC. The page displays compliance badges for SOC 2 Type II and ISO/IEC 27001.

What must Edelbyte prove next?

Edelbyte’s product claims leave the company with an execution test at very large scale. Its pay-as-you-go service must deliver the advertised speed and cost efficiency across public, sovereign, self-hosted and on-premises environments.

Customers will judge whether one engine can support search, analytics and agent access without sacrificing specialized capabilities that separate databases provide. Edelbyte’s pitch links breadth to operating economics: agent speed, storage cost and deployment flexibility.