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Logz.io Launches Distributed Tracing

Logz.io announced the launch of the Jaeger-based Distributed Tracing solution.

Distributed Tracing enhances and completes Logz.io’s ground-breaking observability mission of providing the best-of-breed open source monitoring tools on a fully managed SaaS platform.

Logz.io now enables engineers to collect, analyze and correlate logs, metrics and traces on a single, open source-based platform to make production monitoring and troubleshooting faster and easier.

As engineering teams continue to develop microservices based applications, the complexities of this increasingly distributed architecture have made it harder to identify and isolate production issues. The open source tracing project, Jaeger, governed and supported by the Cloud Native Computing Foundation, was conceived at Uber in response to these monitoring challenges and their business need to build a microservices and cloud native architecture to meet demands for scale. Jaeger responds to the fundamental shift in the development and operational needs of engineers by supporting the monitoring, verification, debugging, and performance of cloud native applications. According to the recent DevOps Pulse 2020 from Logz.io, Jaeger and other tracing solutions are emerging, with 68% of the 1,000+ engineers surveyed stating that the technology “plays an important role in their observability strategy.”

The Logz.io Distributed Tracing solution complements the ELK-based Log Management and open source Grafana-based Infrastructure Monitoring offerings to provide engineers with an easy-to-use, fully managed and integrated platform. Logz.io Distributed Tracing is completely cloud-based, easy to deploy and onboard. Engineers can use Distributed Tracing to gain a system-wide view of their architecture, stay alerted on failed or high latency requests, and quickly drill into end-to-end call sequences of selected requests of intercommunicating microservices. Users can also reduce time to detect and time to resolve by navigating between traces, logs and metrics. The launch of Jaeger-based Distributed Tracing further solidifies Logz.io’s commitment to the open source community by contributing features and fixes to Jaeger.

Key benefits of Distributed Tracing include:

- Unification with Log Management and Infrastructure Monitoring: - Engineers can monitor and analyze logs, metrics, and traces based on ELK, open-source Grafana, and Jaeger on one unified platform. Jaeger, along with Logz.io’s offering, adheres to the open source projects OpenTracing and OpenTelemetry and guarantees that application instrumentation and trace data will remain relevant across frameworks now and into the future.

- Open Source + Advanced Analytics: Distributed Tracing enables seamless alerting and correlation between logs and traces. This workflow unifies monitoring and troubleshooting to facilitate faster root cause analysis.

- Ease of Use and Cost Efficiency: The solution supports the rich ecosystem of instrumented tracing frameworks, databases and programming languages. Engineers can easily deploy Distributed Tracing and avoid the steep costs of leading proprietary APM-based solutions, and the overhead of managing open source components at scale.

“This solution marks a significant milestone in the development of our end-to-end observability platform,” said Tomer Levy, CEO and Co-founder of Logz.io. “Engineers can now quickly and easily deploy an open source-based tracing solution that allows fast and seamless correlation between logs, metrics and traces on one unified SaaS platform.”

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Logz.io Launches Distributed Tracing

Logz.io announced the launch of the Jaeger-based Distributed Tracing solution.

Distributed Tracing enhances and completes Logz.io’s ground-breaking observability mission of providing the best-of-breed open source monitoring tools on a fully managed SaaS platform.

Logz.io now enables engineers to collect, analyze and correlate logs, metrics and traces on a single, open source-based platform to make production monitoring and troubleshooting faster and easier.

As engineering teams continue to develop microservices based applications, the complexities of this increasingly distributed architecture have made it harder to identify and isolate production issues. The open source tracing project, Jaeger, governed and supported by the Cloud Native Computing Foundation, was conceived at Uber in response to these monitoring challenges and their business need to build a microservices and cloud native architecture to meet demands for scale. Jaeger responds to the fundamental shift in the development and operational needs of engineers by supporting the monitoring, verification, debugging, and performance of cloud native applications. According to the recent DevOps Pulse 2020 from Logz.io, Jaeger and other tracing solutions are emerging, with 68% of the 1,000+ engineers surveyed stating that the technology “plays an important role in their observability strategy.”

The Logz.io Distributed Tracing solution complements the ELK-based Log Management and open source Grafana-based Infrastructure Monitoring offerings to provide engineers with an easy-to-use, fully managed and integrated platform. Logz.io Distributed Tracing is completely cloud-based, easy to deploy and onboard. Engineers can use Distributed Tracing to gain a system-wide view of their architecture, stay alerted on failed or high latency requests, and quickly drill into end-to-end call sequences of selected requests of intercommunicating microservices. Users can also reduce time to detect and time to resolve by navigating between traces, logs and metrics. The launch of Jaeger-based Distributed Tracing further solidifies Logz.io’s commitment to the open source community by contributing features and fixes to Jaeger.

Key benefits of Distributed Tracing include:

- Unification with Log Management and Infrastructure Monitoring: - Engineers can monitor and analyze logs, metrics, and traces based on ELK, open-source Grafana, and Jaeger on one unified platform. Jaeger, along with Logz.io’s offering, adheres to the open source projects OpenTracing and OpenTelemetry and guarantees that application instrumentation and trace data will remain relevant across frameworks now and into the future.

- Open Source + Advanced Analytics: Distributed Tracing enables seamless alerting and correlation between logs and traces. This workflow unifies monitoring and troubleshooting to facilitate faster root cause analysis.

- Ease of Use and Cost Efficiency: The solution supports the rich ecosystem of instrumented tracing frameworks, databases and programming languages. Engineers can easily deploy Distributed Tracing and avoid the steep costs of leading proprietary APM-based solutions, and the overhead of managing open source components at scale.

“This solution marks a significant milestone in the development of our end-to-end observability platform,” said Tomer Levy, CEO and Co-founder of Logz.io. “Engineers can now quickly and easily deploy an open source-based tracing solution that allows fast and seamless correlation between logs, metrics and traces on one unified SaaS platform.”

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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 ...

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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 ...

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