
Logz.io announced the introduction of its new Open 360™ platform.
With the launch of Open 360™, Logz.io delivers numerous innovations that measurably reduce the complexity of cloud applications monitoring and troubleshooting, while optimizing related resources.
Open Open 360™ addresses the most significant and pervasive challenges faced by today’s observability teams - the inability to efficiently monitor complex applications while controlling related data volumes and costs. By directly targeting the requirement to process huge volumes of available data to gain insight into popular systems such as Kubernetes - Open 360 is purpose-built to help observability teams focus on, and pay for, only the most critical data.
“Observability is an absolute essential, but today’s model is broken,” says Tomer Levy, CEO and co-founder of Logz.io. “Organizations need an open platform that addresses the explosion of noisy, low-value data, while aligning with the maturity of their existing teams by enabling them to benefit from the unmatched innovation and flexibility of open source. The days of ‘let’s process and pay for every ounce of data’, and proprietary vendor lock-in, are over.”
Open 360™ introduces new capabilities ranging from centralized, cross-stack Kubernetes observability to an expanded data collection agent, representing a significant step forward in the delivery of full stack observability that provides rapid time to value, at any scale.
Among the key capabilities delivered in Open 360™ are new offerings including:
- Logz.io Kubernetes 360 announced last month, delivers unified observability for Kubernetes environments, providing a single interface across open source tools including OpenSearch, Prometheus and Jaeger, among others.
- Logz.io Telemetry Collector extends Logz.io agent-based data collection to accelerate and streamline onboarding and immediately surface key insights.
- Logz.io Data Optimization Hub provides a centralized dashboard interface to inventory all incoming observability data and provides insights to identify and manage noisy data.
- Logz.io LogMetrics Index speeds conversion of log data into key metrics for monitoring related trends while simplifying indexing and substantially reducing costs.
- Logz.io Trace Sampling Wizard simplifies configuration of the popular open source OpenTelemetry Collector to simplify related data onboarding.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.