Mezmo announced a free trial and a free community plan for Mezmo Telemetry Pipeline.
Mezmo’s Telemetry Pipeline is making it easier for teams to collect, transform, and route telemetry data to control costs and get the most value from their telemetry data. Companies can now unlock the power of their data and achieve the cost savings and control benefits of a telemetry pipeline — without the upfront investment.
“Developers, site reliability engineers, security analysts, and more need access to telemetry data but struggle with too much data, unusable formats, and difficulty in getting data to the right platforms,” said Tucker Callaway, CEO, Mezmo. “We want to lower the barrier for entry and show companies how Mezmo’s Telemetry Pipeline can help them control costs and transform their data to drive action.”
Since the initial launch, Mezmo has added many capabilities to its telemetry pipeline, providing better control and user experience.
- User experience and productivity: Mezmo offers a drag-and-drop builder to design and deploy pipelines in minutes. Users can simulate and test the pipeline before deploying and get visibility into the live data within the pipeline at any time. Out-of-the-box dashboards help with pipeline health monitoring, diagnostics, and alerting.
- Integrations: Mezmo is continuously expanding its integration ecosystem to provide users with more control over their telemetry data. The platform supports integrations to more than a dozen sources and destinations, including Grafana, Datadog, Prometheus, Splunk, and Elastic, and major cloud infrastructures, such as AWS, GCP, and Azure. Mezmo’s Telemetry Pipeline supports data ingestion in OpenTelemetry format as well.
- Enhanced data value: Data transformation within the telemetry pipeline adds value to the data and provides critical context for data processing. Mezmo pipeline comes with a wide variety of processors, including the ability to use grok, regex, and JavaScript snippets to parse and transform data. In addition, users can easily extract metrics embedded in the logs, summarize events and metrics, and send it to downstream systems for immediate insights.
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 ...