Transforming the Economics of Log Management

In this 10-page report, you’ll learn about the following:

› Why today’s observability solutions cost too much

› Why customers must throw away data to control costs

› How data-hungry use cases like AI and cybersecurity are driving companies to find new, low-cost observability options

› How next-generation observability platforms are transforming the economics of observability, making it possible to keep more data for longer

Business leaders are scrutinizing their annual cloud spend due to the high cost of traditional observability solutions and an unpredictable macroeconomic outlook. However, enterprises and observability platforms face a formidable challenge. How can observability costs be brought down when the volume of log data has increased 5x over the last three years on average?

Compounding the issue, major observability platforms and software solutions are built on data management systems that aren’t cost-effective for log data at scale. Unfortunately, without changing the underlying data storage layer, there is no way to avoid passing these costs onto customers.

Modern observability platforms must embrace new, cloud-native approaches to solve data problems at scale while remaining cost-effective. These approaches include many, if not all, of the following design choices: decoupled storage that uses low-cost cloud object stores, stateless ingest, query, and storage components, truly elastic scaling, and advanced data compression.

Transforming the Economics of Log Management with Next-Gen Cloud Data Platforms for Observability Use Cases

    Enter your information below to have the this document emailed to you: