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LogDNA Agent 3.3 Released

LogDNA announced the general availability of LogDNA Agent 3.3, which introduces Linux and ARM64 support to Rust Agent.

The new support in Rust Agent provides improved performance and enables a few features previously only available for Kubernetes customers, such as various configurations within the Agent and the ability to run as a non-root user.

Additionally, LogDNA have added in Prometheus Metrics that help provide insights into the Agent.

Finally, given the newest Linux support, support matrix has been updated to reflect the changes.

A few tests were ran with Node and Rust Agents and it was found that the memory usage of the Rust Agent was significantly more efficient than that of the Node Agent.

Given the lower, more predictable memory usage, introducing Linux support with Rust Agent takes advantage of these performance gains.

With the new Linux support, customers can now utilize features previously only available for user ingesting Kubernetes logs.

Specifically, Linux customers can now do the following:

- Run their Agent as Non-Root for enhanced security.

- Configure regex redaction rules to allow for peace of mind that Personally Identifiable Information (PII) is not exposed.

- Configure stateful lookback to guarantee that logs are never dropped, even while the Agent is being restarted or upgraded.

Metrics related to the Agent are exposed, such as the number of log files currently tracked or the number of bytes parsed, along with process status information. Through integrating with Prometheus, customers can have a better understanding of how the Agents are behaving and even alert on when metrics are abnormal.

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LogDNA Agent 3.3 Released

LogDNA announced the general availability of LogDNA Agent 3.3, which introduces Linux and ARM64 support to Rust Agent.

The new support in Rust Agent provides improved performance and enables a few features previously only available for Kubernetes customers, such as various configurations within the Agent and the ability to run as a non-root user.

Additionally, LogDNA have added in Prometheus Metrics that help provide insights into the Agent.

Finally, given the newest Linux support, support matrix has been updated to reflect the changes.

A few tests were ran with Node and Rust Agents and it was found that the memory usage of the Rust Agent was significantly more efficient than that of the Node Agent.

Given the lower, more predictable memory usage, introducing Linux support with Rust Agent takes advantage of these performance gains.

With the new Linux support, customers can now utilize features previously only available for user ingesting Kubernetes logs.

Specifically, Linux customers can now do the following:

- Run their Agent as Non-Root for enhanced security.

- Configure regex redaction rules to allow for peace of mind that Personally Identifiable Information (PII) is not exposed.

- Configure stateful lookback to guarantee that logs are never dropped, even while the Agent is being restarted or upgraded.

Metrics related to the Agent are exposed, such as the number of log files currently tracked or the number of bytes parsed, along with process status information. Through integrating with Prometheus, customers can have a better understanding of how the Agents are behaving and even alert on when metrics are abnormal.

The Latest

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...