Optimize Before You Observe
with Active Telemetry

"We pay all this money for Davis AI, but I still end up grepping log files at 2 AM because the good data is buried in garbage."

— Every Dynatrace Admin, Ever

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Transform thousands of custom metrics into high-value aggregates (p95, p99, averages) to slash DDU costs while improving signal quality for Davis AI.

Perfect for: High-cardinality Kubernetes metrics

Prevent DDU cost explosions by controlling, filtering, and reducing unique labels (pod_id, customer_id) before they reach Dynatrace.

Perfect for: Container-heavy environments

Automatically drop noisy traces while guaranteeing 100% capture of traces with errors and latency issues that Davis needs for accurate root cause analysis.

Perfect for: Microservices architectures

Bypass Dynatrace ingestion delays entirely during incidents. View retention-less, raw log streams in real-time to find root causes in seconds.

Perfect for: Emergency troubleshooting

Auto-adjust data filtering based on conditions with a 4-hour buffer for risk-free processing. Never lose critical data.

Perfect for: Dynamic workloads

Use ML to automatically classify data and identify opportunities to remove redundancies and low-value telemetry before it hits your DDU quota.

Perfect for: Data discovery & optimization

"Mezmo helped us reduce our overall telemetry data volume by 50% by filtering and parsing data to ensure we only indexed the fields we actually needed."

— Netlink Voice

"Teams using Mezmo consistently reduce mean time to resolution by 30-50% through cleaner, more relevant data feeding into their observability platform."

— Platform Engineering Team

"Using Data Profiler, we discovered we were sending massive amounts of verbose health check logs. Mezmo helped us create cost metrics and optimize our pipeline."

— Schier Engineering