Grepr Blog

Grepr’s Intelligent Observability Engine uses pipelines, clustering, and adaptive sampling to process and reduce log data by up to 90% while preserving full visibility.

FOSSA cut Datadog logging costs by 95% with Grepr, keeping full visibility and dashboards intact while scaling their software supply chain platform.

Grepr reduces observability costs by up to 98% through intelligent data summarisation while preserving complete access to all logs when needed.

Grepr uses machine learning to cut log volume by 90% while keeping every log searchable and recoverable, giving teams lower costs and full control.

Grepr is building the foundation for AI-powered monitoring that understands context, reduces noise, and helps engineers catch issues before they escalate.

Grepr SQL Operators let you reshape live log data into real time insights using simple SQL, giving you full control over metrics, traces, and alerts.

Grepr connects directly with Datadog to reduce log volume and costs by up to 90 percent while keeping every log accessible for analysis and compliance.

Grepr decouples data collection from observability platforms, cutting costs and eliminating vendor lock-in while retaining complete visibility and control.

Grepr uses unsupervised machine learning to reduce log volume by over 90% while preserving important data through smart, configurable aggregation. It passes low-frequency messages through unmodified, allows engineers to retain specific parameters like user IDs, and supports backfilling logs via API triggers when deeper detail is needed—such as during support tickets. For added flexibility, trace sampling can capture full logs for a subset of users, and all original logs are archived in a searchable data lake. This gives teams control, reduces noise, and enables cost-effective observability without sacrificing access to critical information.

Grepr helps teams keep full visibility while reducing observability data volume and costs through intelligent summarization and instant backfill.

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.