Sumo Logic this week announced it will no longer charge a fee for ingesting log data into its observability platform to encourage DevOps teams to apply analytics more deeply.
Michael Cucchi, vice president of product marketing for Sumo Logic, said the Sumo Logic Flex Licensing plan eliminates what amounts to a tax for using an observability platform. Flex Licensing is being made available immediately to new customers and will be offered to existing Sumo Logic customers later this year. There are no hidden monthly charges, feature restrictions, performance tradeoffs or user limitations being applied under the plan.
The licensing plan only applies to log data, but DevOps teams will be able to ingest all structured, semi-structured and unstructured data for a lower cost that Sumo Logic is working to store more efficiently, said Cucchi. The overall goal is to make available a disruptive approach to licensing that lowers the total cost of observability, he added.
Storing log data is going to be increasingly critical as organizations begin to apply multiple types of artificial intelligence (AI) models to further automate IT operations, he added. Those AI models promise to make observability platforms more accessible because algorithms will be able to surface issues that need to be addressed. Historically, the value of an observability platform has been tied to how proficient a DevOps team became when using its query language.
IT teams that have adopted observability platforms have been, to varying degrees, limiting the amount of data they collect and retain to keep storage costs from exploding. The challenge is that as DevOps teams deploy more microservices-based applications, the amount of log data generated has exploded. Determining the root cause of any issue involving those applications can be problematic if log data isn’t readily available. Log data is, after all, the atomic unit for observability, noted Cucchi.
In general, IT environments are becoming too complex for humans to manage without the aid of observability platforms augmented by AI models, noted Cucchi. Each application environment is fairly unique, so it is pivotal for those AI models to be exposed to as much data as possible to ensure optimal results.
It’s not clear whether observability platforms might one day replace the need for many of the monitoring tools that IT teams rely on today to track a set of pre-defined metrics. However, one thing that is certain is that observability platforms, at the very least, present an opportunity to rationalize some of those tools. The biggest challenge is finding ways to fund the acquisition of an observability platform in the first place, which then creates an opportunity to consolidate those tools later.
Given the complexity of highly distributed computing platforms, it’s only a matter of time before most IT teams have some type of observability capability in place. Of course, observability has always been a core DevOps tenet. The trouble is that the tools being used to achieve it are legacy monitoring platforms that don’t provide the level of depth required to successfully manage modern application environments.
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