Goldsky, a Web3 realtime data platform, partnered with Grepr to significantly reduce their log management costs while maintaining observability performance. Initially facing misalignment between logging spend and value, Goldsky deployed Grepr, led by Lead Engineer Paymahn Moghadasian, who quickly integrated it using Terraform and Datadog's dual-shipping feature. Over four weeks, they successfully filtered noisy logs and transitioned their production environment with zero disruption. The result: a 96% reduction in indexed logs, 93% less data ingested, and over 85% savings in Datadog costs—without any negative impact on Mean Time to Resolution (MTTR). Additional benefits included improved log readability, faster searches without rehydration, and white-glove support from Grepr.

Grepr and Cribl both offer data pipelines for observability, but they differ in complexity and approach. Cribl is a powerful, flexible platform requiring significant setup, ongoing management, and learning its custom query language. Grepr is the newer, simpler option, using AI to automate data filtering and reduce manual configuration by 90%. While Cribl offers more integrations, Grepr supports common sources, uses familiar query languages, and enables faster, lower-maintenance deployment. Cribl suits large enterprises with dedicated teams, while Grepr is ideal for organizations seeking a faster, more automated solution.

Grepr reduces observability costs by storing all data in low-cost storage and using machine learning to forward only unique or summarized insights to platforms like DataDog, Splunk, or New Relic. Engineers can query retained data, generate reports, power AI, or trigger dynamic backfill during incidents—automatically via webhooks or manually through the Grepr interface. To learn more or request a demo, visit grepr.ai.