With apologies to George Orwell. Not all Observability data is salient all the time, some data is required all the time but most data is only germane when investigating an issue.

Vector and Grepr both function as observability data pipelines, but they differ sharply in complexity and automation. Vector, an open-source tool sponsored by Datadog, is powerful and flexible but requires extensive manual configuration, domain-specific scripting (VRL), and careful infrastructure planning. In contrast, Grepr is a fully automated, AI-driven observability platform that dynamically manages thousands of data transformations without requiring custom coding. It reduces observability costs by up to 90%, stores all data in queryable formats like Apache Iceberg on AWS S3, and integrates seamlessly with tools like Datadog and Splunk. With Grepr, organizations can deploy in minutes instead of days—without the operational overhead.

Modern web applications are rich, dynamic, and heavily reliant on frontend frameworks like React and Vue, which makes browser-side logging essential for understanding both code execution and user behavior. The Datadog browser logs SDK allows developers to collect this data, but with high traffic, logging can become expensive due to Datadog’s volume-based pricing. Grepr solves this by acting as an intelligent intermediary: it receives all logs, stores them cost-effectively, and uses AI-powered filtering to reduce the volume sent to Datadog by 90%—without dropping any data. It aggregates and summarizes repetitive logs, maintains full fidelity through semantic understanding, and even retains query access to all original data via a dashboard using the same syntax as Datadog. This approach allows developers to maintain 100% insight with only 10% of the data volume and cost, enabling full visibility into user behavior and app performance without budget concerns.