Databento Analysis: $10M Raised
What is Databento?
Product Features & Capabilities
- Real-time market data APIs
- Historical market data APIs
- Data access for equities
- Data access for futures
- Data access for options
How much Databento raised
Funding Round - $10.0M
RecentOther Considerations
Gtm Strategy
Reported Clients
- Architect - Focused on reimagining trading infrastructure.
- Double River - Engaged in machine learning for algorithmic trading.
- Fenrir - Powers a proprietary trading firm.
- Cybersyn - Provides analytics-ready economic data on Snowflake.
- Lime Trading - Works on real-time execution and data integration for quant workflows.
- Temple Capital - Expanding into futures markets.
- Tickblaze - Empowers portfolio-level quant trading with institutional-grade data.
- UC Berkeley - Partners with their top-ranked MFE program. The nature of the relationships includes providing market data solutions, enhancing trading infrastructure, and supporting educational programs.
Tech Stack
Databento employs a diverse technology stack that reflects its focus on providing APIs for real-time and historical market data. The company primarily uses **Python** for backend development, alongside **C/C++** and **Rust** for high-performance server applications. In terms of frameworks, they favor established web frameworks such as **Django**, **Flask**, **FastAPI**, and **Starlette**.
For infrastructure, Databento utilizes **Docker**, **Docker Swarm**, and **Kubernetes** for containerization and deployment. Their data stack includes databases like **MySQL**, **Redis**, and **ClickHouse**, indicating a robust approach to data management.
On the sales side, the company uses **HubSpot** as its CRM system, which aids in record-keeping and automating sales processes. This combination of technologies illustrates Databento's commitment to leveraging modern tools and frameworks to enhance their product offerings and operational efficiency.
