Prediction market and sports betting exchange processing billions annually
Smarkets operates a regulated prediction market exchange with $billions in annual volume and over 1 million customers globally. The tech stack—Linux, Kafka, Postgres, Kubernetes, with Rust and C++17 for latency-critical systems—reflects a trading infrastructure company, not a typical fintech. Current hiring and project activity show simultaneous focus on internal developer platforms, Kubernetes scaling, and end-to-end compliance buildout, suggesting maturation from pure trading platform toward institutional-grade operations infrastructure.
Notable leadership hires: Chief Compliance Officer
Smarkets is a prediction market and sports betting exchange founded in 2008, operating under multiple regulatory licenses across jurisdictions. The platform processes billions of dollars in annual volume for over 1 million customers, generating liquidity through proprietary market-making technology and peer-to-peer order matching. The company is UK-based with ~100+ employees split between London and Malta. Core engineering surfaces run on Python and React; performance-critical order matching and risk systems are built in Rust and C++17. The business model centers on exchange infrastructure—connecting traders, capturing small spreads, and maintaining regulatory compliance across multiple jurisdictions.
Core: AWS, Kubernetes, PostgreSQL, Kafka, Python, React. Performance systems: Rust and C++17. Infrastructure: Terraform, Docker, Nix. Observability: Prometheus, Grafana, Elasticsearch. Analytics: Redshift, Tableau, Looker.
London, United Kingdom. Operations also in Malta. ~100+ employees across both locations.
Smarkets's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.