Grepr Blog

Grepr lets you keep your raw log data in your own S3 bucket while still getting the benefits of a managed observability platform.

Both Edge Delta and Grepr use AI to process observability data streams, but Grepr's automatic pipeline management delivers faster time-to-value with minimal configuration while Edge Delta requires ongoing manual maintenance.

The logging paradox forces organizations to index everything at massive cost because they cannot predict which fraction of data a future incident will require.

Most observability overspending comes from paying premium prices to store logs nobody queries.

Observability spending hit $28.5 billion in 2025, and 96% of organizations are now actively working to bring costs under control.

First mile log processing with Grepr filters and routes logs before they reach expensive observability platforms, reducing costs by 90% while preserving 100% visibility by sending high-signal data to premium platforms and routing routine logs to low-cost storage.

Grepr's SQL transform enables real-time redaction of sensitive data like passwords from log events before they reach your data lake or monitoring platform, using familiar SQL syntax within your log processing pipeline.

Across KubeCon, AWS re:Invent, and Gartner's IT Infrastructure conference, one message was consistent: observability costs are rising faster than value, and teams need high-signal data that supports AI scale, infrastructure decisions, and budget accountability.

Live View clones your production pipeline so you can test configuration changes against real data streams without any deployment risk.

Grepr was recognized by Gartner as a Cool Vendor in AI for IT Operations for its ability to give AI driven systems cleaner signal, lower cost, and real-time pattern detection that powers advanced LLM workflows.

Grepr cuts Grafana Cloud log costs by up to ninety percent through a simple configuration change that redirects your existing shippers to use semantic machine learning for automatic pattern aggregation while preserving all raw data in low cost storage.

Grok lets teams fill the gaps when automatic field detection misses something, giving Grepr a simple way to extract key details from unstructured logs and keep data accurate.