Product

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 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.

This blog post shows how to reduce log volume by up to 90% by integrating New Relic with Grepr. Using a simple Docker-based microservices demo, we walk through configuring Fluent Bit to ship logs to New Relic, then show how easily Grepr can be inserted into the pipeline to intelligently filter out noise. The result is cleaner, more actionable log data, reduced observability spend, and no disruption to existing workflows. All raw data is retained in low-cost storage and can be backfilled on demand—helping teams stay in control of both their visibility and their budget.

Goldsky, a Web3 realtime data platform, partnered with Grepr to significantly reduce their log management costs while maintaining observability performance. Initially facing misalignment between logging spend and value, Goldsky deployed Grepr, led by Lead Engineer Paymahn Moghadasian, who quickly integrated it using Terraform and Datadog's dual-shipping feature. Over four weeks, they successfully filtered noisy logs and transitioned their production environment with zero disruption. The result: a 96% reduction in indexed logs, 93% less data ingested, and over 85% savings in Datadog costs—without any negative impact on Mean Time to Resolution (MTTR). Additional benefits included improved log readability, faster searches without rehydration, and white-glove support from Grepr.

Grepr and Cribl both offer data pipelines for observability, but they differ in complexity and approach. Cribl is a powerful, flexible platform requiring significant setup, ongoing management, and learning its custom query language. Grepr is the newer, simpler option, using AI to automate data filtering and reduce manual configuration by 90%. While Cribl offers more integrations, Grepr supports common sources, uses familiar query languages, and enables faster, lower-maintenance deployment. Cribl suits large enterprises with dedicated teams, while Grepr is ideal for organizations seeking a faster, more automated solution.