Product Features

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's semantic pipeline automatically masks variable data, clusters similar messages, and samples or summarizes noisy patterns in real time, reducing log volume by 90% or more without dropping a single message.

Grepr retains all your logs in low-cost storage and backfills exactly what you need into your existing tools the moment an incident hits, so you never lose the data that explains what went wrong.

Grepr uses distributed stream partitioning and a forgetful Bloom filter to guarantee complete log sets for sampled traces, without exhausting memory or disrupting your existing workflows.

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.

Observability demands low-latency queries and flexible schemas that most open-source data lake tools can't deliver. Here are six ways Grepr closes that gap.

Four foundational techniques for reducing log volumes, including severity thresholds, log-to-metrics conversion, uniform sampling, and drop rules, along with the tradeoffs engineering teams should weigh before implementing each one.