Mezmo announced the general availability of the Mezmo Agent 3.6, which introduces Windows support to our Rust Agent.
The new version of the agent supports file logging on Windows, which means customers can now upgrade to an agent that runs on Rust and take advantage of many new customization options.
As part of the company name change, from LogDNA to Mezmo, the company will be changing the name of the agent to mezmo-agent. This will happen over a series of releases. Starting in 3.6, environment variables will begin with the prefix MZ, but will be backwards compatible with LOGDNA_. Starting with 3.7, "mezmo" will be used in the names of the agent's binaries and yaml files. The "logdna" name will be backwards compatible until version 4.0, when we will fully remove all references to "LOGDNA_" and "logdna".
After the release of Agent 3.6, Mezmo will remove the “latest” and “stable” tags from the docker images.
Immediately going forward, Mezmo will use a semantic versioning scheme (MM.mm.pp). Minor and patch releases will happen on an ongoing basis. Minor versions will be for adding functionality in a backwards compatible manner; patch versions will be for making backwards compatible bug fixes.
Major releases will happen when there is a breaking change (for instance, the shift from “logdna” to “mezmo” in 4.0 is a breaking change). After a major release, the previous major version will receive security updates for the next year, but not updates containing new features or general bug fixes.
To bring the agent into this support matrix, Mezmo will be discontinuing support of agents that are below version 3.6 starting with the release of 4.0. These agents will still be able to send logs to our service and logs will be ingested normally, but these versions of the agent will no longer be updated for any reason.
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 ...