Grepr operates as an intelligent observability data engine that automatically aggregates, transforms, and routes telemetry data. This process intelligently filters logs to send only high-signal information to existing observability systems, achieving significant volume reduction without requiring retooling or migration. The platform ensures no data loss by storing all events in low-cost storage, accessible via standard query languages for historical analysis and business intelligence use cases.
Funding
$18.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations face escalating costs associated with observability data, often driven by inefficient data management and the need to maintain comprehensive visibility across complex systems. Existing observability solutions can generate excessive noise and lack the intelligence to prioritize critical data, leading to unnecessary storage and processing expenses. This makes it difficult for teams to effectively manage their observability spend without compromising incident response capabilities.
Solution
Grepr offers a machine learning-powered observability data lake that dynamically manages and enriches data in real-time, reducing observability costs by up to 90%. By intelligently adjusting aggregation granularity based on system health and leveraging stateful stream processing, Grepr ensures that the right data is available at the right time. The platform eliminates observability blind spots by enabling seamless querying across multiple observability systems, allowing engineering teams to correlate data and troubleshoot incidents more efficiently. Grepr's real-time data enrichment and standards-based data storage facilitate richer analytics and custom processing using any SQL engine.
Target Audience
Grepr primarily targets engineering teams and organizations seeking to reduce observability costs, eliminate blind spots, and enhance incident response efficiency.
Features
- Dynamic data management that automatically adjusts aggregation granularity based on system health
- Real-time machine learning to summarize and compress data, increasing signal-to-noise ratio
- Query federation enabling users to query data across multiple observability systems
- Support for standards-based data storage using Apache Iceberg for easy querying
- Centralized observability data management to filter, parse, transform, and route data from any agent to any tool
- Autoscaling, serverless data processing to handle data spikes
- SOC2 Type II compliance
- Single Sign-on (SSO) with SAML support