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Mezmo Updates Telemetry Pipeline

Mezmo unveiled new capabilities to surface critical business insights while reducing observability costs by as much as 70%.

Mezmo’s Telemetry Pipeline now includes more integrations, easy-to-use processors, and enterprise controls that deliver a comprehensive approach to optimize data usage, enabling businesses to make informed, data-driven decisions.

Mezmo transforms observability for SRE and DevOps teams, empowering them to filter out noise, dramatically reduce costs, and unleash their telemetry data's true potential.

“Mezmo’s Telemetry Pipeline addresses challenges that have long plagued enterprises in managing telemetry data efficiently and cost-effectively,” said Tucker Callaway, CEO of Mezmo. “Combined, these new features enable a step-by-step approach to decrease data volume and cost while maximizing signals for business insights. Mezmo continues to innovate and deliver solutions to give our customers a competitive edge.”

Mezmo’s Telemetry Pipeline’s new capabilities help users significantly decrease observability costs, expand the usefulness of the telemetry data and improve the performance of their observability platforms.

- Enabling Business Insights: Mezmo's new Events-to-Metrics Processor identifies and extracts metrics from bulky logs for easy consumption by analytics and visualization tools. This enables better business insights while reducing observability costs. Users can glean insight into metrics, such as abandoned shopping carts, application response times, exposed HIPAA or PII data, failed transactions, network latency, and more, and send such metrics to observability platforms such as Grafana or Datadog.

- Expanded Integrations: As the Pulse Report shows, enterprises connect to dozens of data sources and multiple observability platforms to monitor their systems. Expanding its integration ecosystem, Mezmo helps customers understand and organize telemetry data for a growing range of observability platforms. In addition to the dozens of existing integrations, Mezmo has added integrations to New Relic and Honeycomb.io, while expanding support for data ingestion from Prometheus, Splunk, Kafka, and Azure.

- Enterprise Support: Beyond moving the data, enterprises require telemetry pipelines to support SRE and DevOps workflows. Mezmo has recently added multiple capabilities, such as rollback and redeploy, sequential parsing, error history management, and data sample management, to ensure that multiple teams can take advantage of telemetry data as efficiently as possible.

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If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

Mezmo Updates Telemetry Pipeline

Mezmo unveiled new capabilities to surface critical business insights while reducing observability costs by as much as 70%.

Mezmo’s Telemetry Pipeline now includes more integrations, easy-to-use processors, and enterprise controls that deliver a comprehensive approach to optimize data usage, enabling businesses to make informed, data-driven decisions.

Mezmo transforms observability for SRE and DevOps teams, empowering them to filter out noise, dramatically reduce costs, and unleash their telemetry data's true potential.

“Mezmo’s Telemetry Pipeline addresses challenges that have long plagued enterprises in managing telemetry data efficiently and cost-effectively,” said Tucker Callaway, CEO of Mezmo. “Combined, these new features enable a step-by-step approach to decrease data volume and cost while maximizing signals for business insights. Mezmo continues to innovate and deliver solutions to give our customers a competitive edge.”

Mezmo’s Telemetry Pipeline’s new capabilities help users significantly decrease observability costs, expand the usefulness of the telemetry data and improve the performance of their observability platforms.

- Enabling Business Insights: Mezmo's new Events-to-Metrics Processor identifies and extracts metrics from bulky logs for easy consumption by analytics and visualization tools. This enables better business insights while reducing observability costs. Users can glean insight into metrics, such as abandoned shopping carts, application response times, exposed HIPAA or PII data, failed transactions, network latency, and more, and send such metrics to observability platforms such as Grafana or Datadog.

- Expanded Integrations: As the Pulse Report shows, enterprises connect to dozens of data sources and multiple observability platforms to monitor their systems. Expanding its integration ecosystem, Mezmo helps customers understand and organize telemetry data for a growing range of observability platforms. In addition to the dozens of existing integrations, Mezmo has added integrations to New Relic and Honeycomb.io, while expanding support for data ingestion from Prometheus, Splunk, Kafka, and Azure.

- Enterprise Support: Beyond moving the data, enterprises require telemetry pipelines to support SRE and DevOps workflows. Mezmo has recently added multiple capabilities, such as rollback and redeploy, sequential parsing, error history management, and data sample management, to ensure that multiple teams can take advantage of telemetry data as efficiently as possible.

The Latest

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 ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...