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
Use Cases
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.
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.
