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November 15, 2017

Splunk Competitor Raises Cash, Rolls New Tools

George Leopold

Logz.io, the log analysis startup, has raised additional cash that will be used to apply artificial intelligence techniques to help reduce the amount of stored and analyzed data while improving the quality of business insights.

Logz.io and others also are making greater use of machine learning to advanced IT analytics tools used to detect an fix costly errors in the delivery of critical applications.

The Tel Aviv-based startup said this week it has raised an additional $23 million in venture funding as it adds new AI-based log analysis features such as incident detection tools. So far, the Splunk competitor has raised $47 million in financial backing.

OpenView led the Series C funding round along with early participants 83North, Giza and new investor Vintage Investment Partners, Logz.io said Wednesday (Nov. 15).

The startup also said it is releasing two new capabilities designed to squeeze more value from machine data. An “application insights” tool uses machine learning to speed incident detection within enterprise applications. A “data optimizer” capability is intended to reduce the cost of data retention by determining the value of log data and how long they should be stored.

The second capability is aimed squarely at established competitors such as Splunk (NASDAQ: SPLK), which Logz claims has yet to address to cost of storing data over time while the value of data remains the same or may even decline.

For its part, San Francisco-based Splunk has introduced analytics packages over the last year intended to “operationalize” machine data. Along with machine data, Splunk’s platforms also promise tighter integration with Hadoop in order to shift historical data to existing Hadoop platforms for applications such as hybrid searches to analyze machine and historical data on the Splunk machine- learning platform. That, the company asserts, would lower on-premises total cost of ownership.

By comparison, Logz.io’s log analytics platform combines machine learning with the open-source ELK (Elasticsearch, Logstash, Kibana) log analyzer. The Logz.io platform synthesizes machine data with user behavior and “community knowledge” to derive insights while limiting the amount of data stored and analyzed.

While Splunk’s technology is proprietary, the startup’s open-source approach to scalable log management would allow users to take advantage of existing tools. “We see a massive opportunity to help businesses of all sizes derive far more value from machine data than is currently possible under existing log analytics business models,” Tomer Levy, Logz.io’s CEO and co-founder, noted in a statement announcing its latest funding round.

Logz.io and its competitors also are making greater use of machine learning tools to speed incident detection. The startup said its app insights tool uses machine learning to create a model for “normal operations,” then isolates errors that don’t fit the model.

The requirement to quickly detect and repair IT incidents is growing with the introduction of micro-services and other agile approaches to continuous delivery of enterprise applications. According to a Splunk-sponsored study released this week, the average organization logs about 1,200 IT incidents per month, of which five will be deemed critical.

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