I
About
Mezmo is an AI-powered telemetry data platform built for agentic operations, helping Site Reliability Engineers, platform engineers, and developers move from millions of noisy signals to a single actionable root cause—fast. The platform ingests logs, metrics, and traces in real-time, applying in-stream parsing, enrichment, and 99.98% data reduction so that AI agents reason over clean, structured insight rather than raw noise. At the core of Mezmo's offering is AURA, an open-source (Apache 2.0, Rust) agentic control plane that orchestrates AI-driven workflows across your entire stack. AURA is MCP-native, LLM-agnostic, and self-correcting through a plan → execute → synthesize → evaluate loop. It supports custom agentic runbooks and runs investigations in under one minute for less than $1—compared to $30+ with traditional approaches. Mezmo supports 100+ integrations, one-click OpenTelemetry (OTel) migration, and curated context delivery via MCP. Whether you're triaging production incidents, engineering AI-ready context pipelines, or monitoring new releases, Mezmo and AURA together provide the data intelligence and orchestration layer that modern production AI demands. A free trial is available with no credit card required.
Key Features
- Active Telemetry Pipelines: Ingests and processes logs, metrics, and traces in real-time with in-stream parsing, enrichment, and 99.98% data reduction—delivering only curated, context-rich signals to AI agents.
- AURA Open-Source Agent Control Plane: Apache 2.0 Rust-based agentic harness that orchestrates AI workflows using a self-correcting plan → execute → synthesize → evaluate loop, with MCP-native connectivity and LLM-agnostic design.
- Automated Root Cause Analysis: Orchestrates AI-driven incident investigations that complete in under 1 minute, processing fewer than 1,000 curated signals instead of millions of raw events, for less than $1 per investigation.
- 100+ Integrations & One-Click OTel Migration: Connects easily to your existing observability stack with over 100 integrations and streamlined OpenTelemetry migration to centralize all telemetry data.
- Custom Agentic Runbooks: Allows platform engineers and SREs to define repeatable, trustworthy AI agent workflows that run consistently across environments with the same configuration and outcome every time.
Use Cases
- Automated production incident triage: SREs use Mezmo and AURA to automatically detect anomalies, gather context, and identify root causes in under one minute—without manually sifting through millions of log lines.
- AI-ready context engineering: Platform engineers build telemetry pipelines that structure and enrich observability data so it is immediately usable by AI agents for downstream reasoning and action.
- OpenTelemetry migration: Teams consolidate fragmented observability stacks into a unified OTel-compatible pipeline using Mezmo's one-click migration and 100+ pre-built integrations.
- Release health monitoring: Developers connect production telemetry to AURA runbooks to get instant answers about how a new deployment is performing, what errors emerged, and what requires immediate attention.
- Agentic SRE automation: Organizations use AURA's custom runbooks and self-correcting agent loops to build repeatable, trustworthy AI workflows that prevent incidents rather than just react to them.
Pros
- Massive Signal Reduction: Achieves 99.98% data reduction before agents process anything, slashing inference token costs and dramatically speeding up investigations.
- Open-Source AURA Component: AURA is forever open source under Apache 2.0, giving teams full transparency and the freedom to self-host the agentic orchestration layer on their own infrastructure.
- Cost-Effective at Scale: Investigations cost less than $1 each compared to $30+ with competing solutions, making AI-driven observability economically viable at enterprise scale.
- LLM Agnostic Architecture: AURA works with any large language model, preventing vendor lock-in and letting teams choose or swap AI providers as their needs evolve.
Cons
- Enterprise-Oriented Complexity: The platform's depth and feature set may be overkill for small teams or simple applications that don't generate high telemetry volumes.
- Opaque Full Pricing: While a free trial is available, detailed pricing beyond the trial tier is not publicly listed, requiring a demo or sales contact to understand costs at scale.
- Infrastructure Responsibility for AURA: Running AURA on your own infrastructure provides control but also requires your team to manage, maintain, and scale the agentic harness itself.
Frequently Asked Questions
What is AURA and is it really open source?
AURA is Mezmo's open-source agentic control plane, built in Rust and licensed under Apache 2.0. It is designed to run on your own infrastructure, is LLM-agnostic, and is intended to remain open source permanently. You can start using AURA for free without a credit card.
How does Mezmo reduce telemetry noise before agents process it?
Mezmo uses active telemetry pipelines to ingest raw observability data and apply in-stream parsing, enrichment, and filtering—reducing millions of runtime events by up to 99.98% before any AI agent ever sees them. This ensures agents reason over curated insight rather than raw noise.
What integrations does Mezmo support?
Mezmo supports over 100 integrations across logging, metrics, and tracing sources, including one-click OpenTelemetry (OTel) migration. This makes it straightforward to consolidate existing observability stacks into the Mezmo platform.
Who is Mezmo designed for?
Mezmo is built for three primary personas: Site Reliability Engineers (SREs) who need faster incident triage and root cause analysis, platform engineers who want to define and enforce reliable AI agent workflows, and developers who need clean production data to understand release health instantly.
How much does it cost to run an AI investigation with Mezmo and AURA?
Mezmo and AURA are designed to deliver AI-driven investigations for less than $1 each, compared to $30 or more with other observability solutions. This cost efficiency is achieved by drastically reducing the token count agents need to reason over before returning a root cause.
