Product Features

Grepr reduces observability costs by storing all data in low-cost storage and using machine learning to forward only unique or summarized insights to platforms like DataDog, Splunk, or New Relic. Engineers can query retained data, generate reports, power AI, or trigger dynamic backfill during incidents—automatically via webhooks or manually through the Grepr interface. To learn more or request a demo, visit grepr.ai.

Grepr is an intelligent observability pipeline that sits between your agents and observability platform to optimize, analyze, and route data in real time. By using machine learning and a rules engine, Grepr detects patterns in data streams, holds back noisy or repetitive information, and forwards only essential summaries or unique messages. With a simple configuration change, it integrates seamlessly into existing systems—helping teams cut observability costs by up to 90%, retain data long-term, and make observability insights available for business reporting and AI.

Everything we do at Grepr is around making sure we reduce costs with minimal impact to existing workflows. Grepr can automatically parse existing alerts in Datadog (Splunk and New Relic coming up in the next few weeks) and avoid modifying logs that power them. This way, you can roll out Grepr to prod without worrying about having to rewrite all your alerts.

Grepr receives logs from Splunk Heavy Forwarders via S2S, compresses data by 90% while retaining everything in a queryable data lake with SPL support and one-click backfill to Splunk when needed.

Grepr uses machine learning to reduce log volume without losing visibility. It parses and structures logs in real time, groups similar messages, and applies smart sampling to cut noise. Critical logs still get through, and full raw data is stored separately for easy access during incidents—keeping your backend lean and your team in control.

Grepr’s Dynamic Backfill feature lets teams retain all log data at low cost while only sending essential logs to their main logging backend, cutting processing and storage costs by around 90%. Unlike traditional logging that risks missing key data before an incident is detected, Grepr stores everything in affordable storage and allows engineers to selectively backfill detailed logs when issues arise—like turning up log detail after the fact. This ensures engineers have full context for debugging, with no disruption to existing workflows, balancing deep visibility with major cost savings.

Grepr helps companies lower observability costs while keeping engineers' workflows unchanged. A key feature is its ability to ensure full logs for a sampled set of traces, even with log aggregation and sampling in place. Users can specify how much of their data to sample and provide trace ID paths to identify relevant logs. Grepr's system uses intelligent sampling, backfilling, and a rule engine to collect and process trace logs efficiently. To optimize performance and memory, it evolved from a basic set-based approach to a scalable design using Bloom filters and a custom resizing Bloom filter. This allows Grepr to maintain high throughput while managing memory use effectively, ensuring reliable trace log visibility.

Discover how Grepr's intelligent log management solution can reduce your logging costs by 90% without sacrificing visibility. Our two-tier storage system uses machine learning to identify patterns and store less critical logs in low-cost storage, while maintaining immediate access to important data. When incidents occur, Grepr's dynamic backfill feature automatically retrieves relevant logs to your existing tools. Implement smarter logging today without changing your workflows or compromising on troubleshooting capabilities.

Storing logs long-term doesn't have to be super expensive. Using a data lake can reduce storage costs by more than 90% while still keeping the logs queryable and immediately accessible.

Three advanced techniques for reducing log volumes by 90% or more, including automatic pattern sampling, logarithmic sampling, and sampling with automatic backfilling, each designed to scale for enterprise environments without sacrificing critical troubleshooting data.

Grepr's Monitored Objects approach groups metrics by entity, aggregates normal behavior, and only sends detailed data to Datadog when anomalies occur, dramatically reducing billable custom metrics and hosts without sacrificing troubleshooting capability.

This blog discusses 6 ways that the Grepr Data Lake is optimized for logs.