Scalyr vs Sumo Logic
Last updated: May 08, 2019
Server Log Monitoring Tool. We built the log monitoring tool we've always wished for. Scalyr is server log monitoring and analysis built for engineers. Turn chaotic logs and system metrics into actionable data.
Sumo Logic cloud log management solution processes ALL your production application logs and server log data, analyzes them in real-time and delivers actionable results at a fraction of the cost of on-premise solution.
Scalyr vs Sumo Logic in our news:
2019. Sumo Logic raises $110 million to orchestrate cloud apps with AI
Sumo Logic, a cloud-native, machine data analytics platform delivering continuous app intelligence, has raised $110 million in a series G funding round. The infusion follows a breakout year in which Sumo Logic notched over $100 million in revenue and hit the 2,000-customer mark. It’s now valued at over $1 billion, has more than 500 employees, and counts among its client base Airbnb, Pinterest, The Pokémon Co... Sumo Logic spans 150 apps and integrations — provides analytics and insights to help clients build, run, and secure apps and cloud infrastructures. The funds will be used to expand Sumo Logic’s engineering, sales, and global operations teams, with an emphasis on extending the platform analytics capabilities of its various services.
2015. Server log monitoring tool Scalyr raises $2.1M
Scalyr, a log-monitoring service that gives developers more insight into how their applications are performing, has raised a $2.1 million seed round. The idea behind Scalyr, then, is to get all data from server logs, as well as various metrics, error reports and other performance data and run it through a single tool that you can get actionable data from. Services like Splunk and Loggly offer similar log management services, but those tools make it easy to see anecdotes and it’s hard to roll that up into an overview. Scalyr also doesn’t see New Relic as a competitor either because that company’s focus tends to be on performance, while Scalyr focuses more on errors. Log files can quickly grow very large, and analyzing them takes quite a bit of compute power. The team built a new data management engine for its service to handle all of this data.