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3 Latest Announced Rounds

$2,265.93M Raised in 95 Funding Rounds in the past 7 Days - View All

Funding Round Profile

SurrealDB

start up
United Kingdom - London
  • 06/01/2023
  • Seed
  • $6,000,000

SurrealDB is an innovative NewSQL cloud database, suitable for serverless applications, jamstack applications, single-page applications, and traditional applications. It is unmatched in its versatility and financial value, with the ability for deployment on cloud, on-premise, embedded, and edge computing environments. For a hassle-free setup, get started with SurrealDB Cloud in one-click.

With an SQL-style query language, real-time queries with highly-efficient related data retrieval, advanced security permissions for multi-tenant access, and support for performant analytical workloads, SurrealDB is the next generation serverless database.


Related People

Tobie Morgan HitchcockCo Founder

Tobie Morgan Hitchcock United Kingdom - London, England

I am an experienced tech entrepreneur, developer, and software engineer. After finishing a Masters in Software Engineering from the University of Oxford, I have launched a number of products centered around, and powered by, a new real-time web graph-document database named SurrealDB (surrealdb.com).

I have over 15 years’ experience in the software and cloud-computing industries - with a focus on distributed databases, and highly-available architectures. I have experience in a number of different software stacks and development languages, including Rust, Golang, Javascript, Ruby, PHP, HTML, Objective-C, Swift, with extensive database experience with PostgreSQL, MySQL, MongoDB, RethinkDB, Redis, InfluxDB, OrientDB, Firebase, CockroachDB, and TiDB.

I’m intensely interested in software development, and am always looking for ways to simplify the technology stack, whilst focusing on easier-to-use and simpler-to-build tech platforms. I started a Masters in Software Engineering in order to fully understand some of the underlying mathematical issues within the computer science space. My dissertation was focused on the use of versioning within distributed databases, finally cementing my interest in real-time data and immutability, and sparking a fundamental interest in distributed databases with a focus on massive data sets and fast, efficient, and scalable data query access. This extends into how machine learning and artificial intelligence can make use of improved data storage technologies to enhance real-time and continual-learning artificial intelligence.