Case Study: Large American Retailer achieves 50% infrastructure cost reduction and faster incident resolution with Observo AI

Observo AI Enables Enriched Data, Reduced Infrastructure Costs and Faster Incident Resolution for Retail Giant on Splunk

Large American Retailer faced rapidly rising Splunk storage, egress, and compute costs—driven largely by exploding VPC Flow log volumes that accounted for nearly half of their ingestion—and slower query times. Their security team’s manual sampling created blind spots, so they chose the observability pipeline Observo AI to get immediate control of log growth and optimize their data intake.

Observo AI ingested raw data into a full-fidelity AWS S3 data lake (Parquet) searchable with Observo AI natural language queries, and deployed data-transform pipelines to filter and summarize VPC Flow and Firewall logs—cutting optimized log volume by more than 80%. By keeping a lean Splunk index (dropping older data after 48 hours and rehydrating on demand), the Large American Retailer flattened ingest spikes, added new data types, sped queries, and reduced total infrastructure spend by over 50% while improving incident prioritization via stream anomaly detection and sentiment-enriched security events.