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.  

Companies face a massive challenge in optimizing observability data. The sheer volume of telemetry data generated by modern applications makes it difficult for organizations to know and fully utilize what they have. Enterprise observability tools, such as Datadog, cannot automatically determine which logs and traces are most critical, leaving teams with no alternative but to capture and store all their data. Teams are left missing important insights and face massive overages as they struggle with the cost and complexity of managing their telemetry data. 

Mezmo solves this challenge with a new optimization workflow 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." 

Cutting noise and costs with Mezmo

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.