How to choose the right financial data API and why it matters more than you think.
Buying financial data sounds straightforward. But whether you are building an investment research platform, training an AI model, or powering internal analytics, picking the wrong provider can set your product back by months.We wrote the guide we wished existed when we were building the Quartr API. It is designed to help you ask the right questions and stress-test provider claims before you commit your engineering team to a contract.Who this guide is for
Written specifically for Heads of Data, Quantitative Researchers, AI/ML Engineers, Product Managers, and Heads of Research evaluating financial data infrastructure.Inside the guide
- The 5 things that actually matter: Moving beyond marketing claims to evaluate global coverage, guaranteed delivery speeds, and unified data architecture.
- Common red flags: Why "best effort" SLAs, opaque sourcing, and legacy FTP delivery signal deeper infrastructure problems.
- Data licensing for the AI era: How to navigate redistribution rights and ensure your contract explicitly permits AI model training and embeddings.
- How to run a proper evaluation: A step-by-step framework to test edge cases, real production data, and integration times instead of relying on sanitized demos.
- The ultimate checklist: A 13-point checklist to run through before you sign any financial data agreement.
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Download the guide
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How to choose the right financial data API and why it matters more than you think.
Buying financial data sounds straightforward. But whether you are building an investment research platform, training an AI model, or powering internal analytics, picking the wrong provider can set your product back by months.We wrote the guide we wished existed when we were building the Quartr API. It is designed to help you ask the right questions and stress-test provider claims before you commit your engineering team to a contract.Who this guide is for
Written specifically for Heads of Data, Quantitative Researchers, AI/ML Engineers, Product Managers, and Heads of Research evaluating financial data infrastructure.Inside the guide
- The 5 things that actually matter: Moving beyond marketing claims to evaluate global coverage, guaranteed delivery speeds, and unified data architecture.
- Common red flags: Why "best effort" SLAs, opaque sourcing, and legacy FTP delivery signal deeper infrastructure problems.
- Data licensing for the AI era: How to navigate redistribution rights and ensure your contract explicitly permits AI model training and embeddings.
- How to run a proper evaluation: A step-by-step framework to test edge cases, real production data, and integration times instead of relying on sanitized demos.
- The ultimate checklist: A 13-point checklist to run through before you sign any financial data agreement.
Enter your email to download the guide.
Download the guide
Business email *