Your team adopted AI coding agents overnight and lost all visibility into what they do; Agent Sessions captures, normalizes, and makes every session searchable, auditable, and analyzable in aggregate.
Grepr now lets you run analytical queries over the data in your data lake directly from the UI, starting with logs, so you can answer questions where the raw data already lives instead of backfilling it or standing up a separate query engine.
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.
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.
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.
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.
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.
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