Migrate to OpenTelemetry—on your timeline
Mezmo makes OTel adoption practical. Transform exisiting telemetry in-flight, migrate team-by-team as capacity allows, and process at the edge—without reinstrumenting a single service.

Ingest from any existing resource: Connect your current agents, exporters, and proprietary formats. No application changes required.
Transform to OTel in-flight: Mezmo normalizes data to OTel semantic conventions in the pipeline—schema mapping, field renaming, and type coercion handled automatically.
Route to any OTel-compatible destination: Send clean, standardized telemetry to your existing backends or new OTel-native destinations—with no observability gap during the transition.
Migrate services incrementally: Move teams and services to native OTel instrumentation on their own schedule. Mezmo handles the mixed-signal environment until the migration is complete.

Built for the messy middle of migration
Automated, multi-step analysis plans that travers your infrastructure, validate findings, and converge on a defensible root cause.
Only the most relevant data is analyzed, reducing token usage and improving MTTR up to 80%.
Deduplication and clutering of log data before AI analysis, cutting costs and improving spped.
Receive clear RCA summaries with technical details and step-by-step remediation guidance.
Out-of-the-box support for PagerDuty, Slack, and more with expanded ecosystems coming soon.
Designed to handle petabytes of data, with enteprise-grade security, multi-region deplooyment and robust failover mechanism.
Real results from real teams
From $1-$6 per incident to $0.06 due to prioritized context over excessive prompting.
-Mezmo benchmarking
On a daily basis, troubleshooting tasks that used to take our engineers 10-30 minutes of log spelunking are now resolved in under 5. It directly gives us back engineering days, allowing us to focus on innovation instead of firefighting.
— Senior Software Engineer
Clean context beats clever prompting. Mezmo's context-first pipeline reduced prompt bloat and stabilized outputs, improving quality while cutting per-incident costs.
— AI Engineer
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