Is your observability bill growing faster than your engineering team can say "log volume"? You're not alone. FOSSA, a leader in software supply chain management, faced a similar challenge. Their reliance on Datadog, while providing essential visibility, was becoming a significant financial burden as their platform scaled. Instead of a painful, time-consuming overhaul of their entire logging strategy, FOSSA found a smarter way. They discovered a solution that allowed them to dramatically reduce their Datadog costs without sacrificing the crucial insights they needed to monitor and troubleshoot their systems. Want to know how FOSSA achieved a whopping 95% reduction in log volume and kept their observability costs in check? Click to read the full story and discover their secret!

Observability tools are vital for troubleshooting, but their high operational cost, driven by data volume, creates a tension between DevOps teams needing more data and businesses seeking lower bills. This dilemma stems from platforms treating all data as equally important, leading to an "impossible situation." Grepr breaks this conundrum by acting as a shim between log shippers and backends, using semantic machine learning to summarize frequent, noisy messages while passing critical, unique ones straight through. This innovative approach reduces log volume by 90-98% for significant cost savings, yet all data remains accessible in low-cost storage via the Grepr dashboard, REST API, and familiar query syntaxes (Splunk, Datadog, New Relic). This ensures that while you pay only for the 2-10% of data actively used, the rest is available on demand for queries or backfilling during incident investigations, solving the operational versus cost challenge and allowing you to pay only for the data you truly need, when you need it.

This blog post introduces a revolutionary approach to observability, addressing the long-standing "AI-in-a-Haystack" problem in log analysis. Traditional methods struggle with the sheer volume and lack of context in modern telemetry data, making AI analysis financially and technically unfeasible. Grepr offers a unique solution built on three core principles: intelligent telemetry reduction, which de-noises log volumes by over 99% before storage; a stateful stream processing engine, providing AI with the necessary memory and context to understand data trends; and dynamic pipeline control, enabling the AI to reconfigure data streams on the fly to "zoom in" on specific issues. These capabilities transform monitoring from a reactive chore into a proactive, conversational partnership, allowing AI to intelligently flag issues, suggest causes, and dynamically adjust its focus, ultimately leading to faster incident resolution and more efficient operations.