Elastic entered into a definitive agreement to acquire Optimyze, an "always on" continuous profiling platform for infrastructure, applications and services, to accelerate the company's vision for unified, actionable observability and enhance the ability for customers to detect and find root cause faster in complex distributed environments.
With deep expertise in large-scale distributed systems, Optimyze provides a simpler way to get insights into the entire IT ecosystem and eliminate blind spots with Prodfiler. Leveraging eBPF technology, Optimyze delivers innovative, whole-system continuous profiling of systems and code with low performance overhead.
Together with the recent acquisitions of Cmd and build.security, Optimyze will expand Elastic's vision to enable customers to both observe and protect their data on one unified platform, the Elastic Search Platform. Elastic intends to integrate the Optimyze and Cmd innovations as well as the Open Policy Agent (OPA) capabilities from build.security into the Elastic Agent to deliver a simple deployment process and a unified approach to data collection for observability and security.
Optimyze provides frictionless continuous profiling, while the Elastic Search Platform delivers analytics and machine learning capabilities with the ability to correlate and contextualize profiling data with metrics, logs, and traces. The ability to unify the three pillars of observability—metrics, logs and traces—with emerging continuous profiling capabilities delivers actionable insights to customers, leading to improvements in service quality and performance while reducing MTTD (mean-time-to-detect) and MTTR (mean-time-to-resolution).
"Continuous profiling across systems, applications, and services with zero instrumentation, no code changes, and little performance overhead is by itself a game changer. The value increases exponentially when this data can be easily combined and cross-referenced with metrics, traces, logs, and other operational data. We look forward to being part of the Elastic team and making this vision a reality." said Thomas Dullien, CEO and Co-founder, Optimyze.
"Elastic continues to make major advances in our cloud-native observability capabilities by investing in innovative teams that have built differentiated capabilities leveraging open technologies like eBPF," said Shay Banon, Founder and CEO, Elastic. "With deep expertise in large-scale distributed systems, Optimyze overcomes the limitations of traditional profiling techniques to provide whole-system continuous profiling of systems and code, improving developer productivity, accelerating innovation, and delivering rich customer experiences..."
The acquisition is expected to close during Elastic's fiscal second quarter, subject to customary closing conditions.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
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 ...