Mezmo unveiled new solutions to optimize observability costs for Datadog users.
Mezmo Telemetry Pipeline now includes comprehensive insights and optimization workflows for Datadog users, providing SREs and developers with the flexibility needed to profile and reduce large telemetry data volumes, thereby improving cost efficiency and maximizing value from their data.
Mezmo's new optimization workflow is designed to easily understand the data in the stream and make decisions on where to direct the data before it is stored in Datadog. With a clear view of what data is most valuable — and most costly — teams can consolidate common data patterns and make adjustments in storage, easily reducing log volume by as much as 40%. The simple, self-guided workflow ensures faster time to value. Teams can begin reducing data volume and seeing cost optimization in as little as 15 minutes.
“Datadog generates massive amounts of telemetry data, and companies are forced to store it all because they cannot easily determine what is important. Then, at the end of the billing cycle, they are stunned by the ever-increasing costs,” said Lauren Nagel, VP of Product for Mezmo. “Mezmo helps them cut through the noise to understand their data; work smarter, not harder; and, ultimately, identify opportunities for cost optimization that align with business goals.”
Keeping all telemetry data in a full-stack observability tool, like Datadog, is noisy, challenging to manage, and expensive. Mezmo’s new capabilities make it easier for companies to streamline data management while slashing observability costs. With Mezmo, teams get:
- Dedicated Datadog cost optimization workflow: Users can employ Mezmo flow, a guided experience for building telemetry pipelines, to profile Datadog logs, metrics, and tags to better understand operational value and estimate billing impact. This solution allows teams to discern what data is valuable, identify repetitive patterns, and apply optimizations to reduce overall data volume, helping to manage costs and avoid overage charges. Streamlining data processing before it reaches Datadog allows companies to manage Datadog costs predictably and ensure that they’re getting the most value from their data without unnecessary spend.
- Responsive pipelines: Empowering SREs and developers, responsive pipelines enable the dynamic adjustment of telemetry data processing based on triggers such as incidents and deployments, automatically providing high-fidelity data for troubleshooting. At the same time, live tail instantly streams parsed data, allowing teams to quickly spot and resolve issues as they occur, resulting in faster mean time to resolution (MTTR), reduced data costs, and enhanced incident response effectiveness. Teams can leverage a 4-hour “rewind buffer” with full-fidelity information immediately from the time the incident occurred. Available in private beta, this capability ensures that teams have the data needed to answer key questions about what happened pre-incident and facilitate a quicker diagnosis of the root cause.
- Advanced trace sampling for optimal data insight: Users can now choose how they want to sample their trace data — either head-based or tail-based sampling — to reduce noise and accelerate insight discovery. SREs and developers can now be confident that they have the necessary traces for troubleshooting, making them more productive while reducing MTTR. Reducing the mental toil of managing data leads to improved developer experiences, greater opportunities for innovation, and better business outcomes.
The Latest
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
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 ...