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Elastic Acquires Prelert

Elastic, the company behind Elasticsearch and the Elastic Stack, has acquired Prelert, a provider of behavioral analytics technology.

Elastic will integrate the Prelert technology into the Elastic Stack, and will offer it as part of its subscription packages in 2017, giving Elastic customers more capabilities to solve complex use cases such as cybersecurity, fraud detection, and IT operations analytics, among others.

Prelert was founded in 2008 to create technology that automates the discovery of anomalies in large, complex datasets, predicts actions and outcomes, and provides enterprises and their end users with a consumable application that doesn’t require them to perform data science. Using unsupervised machine learning techniques applied to a customer’s historical and real-time continuous data, Prelert’s predictive models perform behavioral analytics to understand the probability of failures and events occurring with built-in alerting and notifications for end users to explain 'why' something has happened and 'what' to do with that information.

More companies, from startups to large enterprises, are storing large amounts of structured and unstructured data in Elasticsearch. With search becoming the foundation for many of these companies to address their most complex use cases, Prelert built an Elastic Stack integration to provide Elasticsearch users with an automated way to understand the 'why' in their data and take action on 'difficult to see' insights. Combining Elastic’s Kibana user interface framework and Prelert’s behavioral analytics technology, customers are able to solve common problems in their continuous and ever-growing data, including:

- detecting advanced security threat activities and anomalies in log data,

- discovering hidden fraud patterns in highly sensitive data,

- identifying anomalous systems or metrics and their root causes across IT systems,

- linking together complex series of events in data to expose early warning signals,

- automatically pinpointing where and why critical system outages are occurring,

- detecting unexpected drops in transactional activity, and much more.

“Today’s enterprises need to understand not just 'that' something happened, but 'why', and frequently the source of this data is sitting in Elasticsearch,” said Shay Banon, Elastic Founder and CTO. “We are happy to have Prelert join our family, and by embedding Prelert’s behavioral analytics engine into the Elastic Stack, our customers will benefit from powerful machine learning capabilities that tell them the 'why' and allow their users to react to issues in real time.”

“With the shift to enterprises standardizing on open source solutions like the Elastic Stack for logging and security use cases, we saw a tremendous opportunity to add value on top of the Elastic Stack,” said Steve Dodson, Prelert Founder and CTO. “We are very excited to join the Elastic team and embed our behavioral analytics engine into the Elastic Stack so that Elastic’s customers can gain powerful and proactive insights, and solve new use cases.”

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Elastic Acquires Prelert

Elastic, the company behind Elasticsearch and the Elastic Stack, has acquired Prelert, a provider of behavioral analytics technology.

Elastic will integrate the Prelert technology into the Elastic Stack, and will offer it as part of its subscription packages in 2017, giving Elastic customers more capabilities to solve complex use cases such as cybersecurity, fraud detection, and IT operations analytics, among others.

Prelert was founded in 2008 to create technology that automates the discovery of anomalies in large, complex datasets, predicts actions and outcomes, and provides enterprises and their end users with a consumable application that doesn’t require them to perform data science. Using unsupervised machine learning techniques applied to a customer’s historical and real-time continuous data, Prelert’s predictive models perform behavioral analytics to understand the probability of failures and events occurring with built-in alerting and notifications for end users to explain 'why' something has happened and 'what' to do with that information.

More companies, from startups to large enterprises, are storing large amounts of structured and unstructured data in Elasticsearch. With search becoming the foundation for many of these companies to address their most complex use cases, Prelert built an Elastic Stack integration to provide Elasticsearch users with an automated way to understand the 'why' in their data and take action on 'difficult to see' insights. Combining Elastic’s Kibana user interface framework and Prelert’s behavioral analytics technology, customers are able to solve common problems in their continuous and ever-growing data, including:

- detecting advanced security threat activities and anomalies in log data,

- discovering hidden fraud patterns in highly sensitive data,

- identifying anomalous systems or metrics and their root causes across IT systems,

- linking together complex series of events in data to expose early warning signals,

- automatically pinpointing where and why critical system outages are occurring,

- detecting unexpected drops in transactional activity, and much more.

“Today’s enterprises need to understand not just 'that' something happened, but 'why', and frequently the source of this data is sitting in Elasticsearch,” said Shay Banon, Elastic Founder and CTO. “We are happy to have Prelert join our family, and by embedding Prelert’s behavioral analytics engine into the Elastic Stack, our customers will benefit from powerful machine learning capabilities that tell them the 'why' and allow their users to react to issues in real time.”

“With the shift to enterprises standardizing on open source solutions like the Elastic Stack for logging and security use cases, we saw a tremendous opportunity to add value on top of the Elastic Stack,” said Steve Dodson, Prelert Founder and CTO. “We are very excited to join the Elastic team and embed our behavioral analytics engine into the Elastic Stack so that Elastic’s customers can gain powerful and proactive insights, and solve new use cases.”

The Latest

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...