Active Telemetry
Active Telemetry, offered by Mezmo, is a platform that transforms telemetry pipelines from passive data collectors into living, AI-ready systems.
Publisher review
Active Telemetry, offered by Mezmo, is a platform that transforms telemetry pipelines from passive data collectors into living, AI-ready systems. It is designed for DevOps, SRE, and platform engineering teams managing complex, distributed environments like microservices, Kubernetes, and AI workloads. Unlike traditional observability tools that store and visualize data after the fact, Active Telemetry enriches, filters, and routes data in real time, enabling proactive operations. The platform is built around four pillars: Active Context, which allows AI to operate on context-engineered data sets; Active Analysis, which processes telemetry in-stream to accelerate root cause analysis; Active Engagement, which lets developers access high-context telemetry in their workflows; and Active Routing, which reshapes and normalizes data for human and AI consumption, including seamless migration to OpenTelemetry.
Mezmo's platform reduces millions of raw events into curated, context-rich signals. It uses data profiling and pipeline processors to separate signal from noise, filter low-value data, and apply sampling to reduce chatter. The platform claims to reduce observability costs by up to 80% and cut AI observability costs by 90% through context engineering. Its AURA open-source agent control plane orchestrates agents that get smarter with every incident. The platform supports real-time profiling, context-driven analysis, and automated responses to anomalies, with a Welcome Pipeline that derives business insights from Kubernetes logs in less than five minutes.
Mezmo competes directly with Datadog, Sumo Logic, and Logmanager, as well as Bindplane and EventSentry. While Datadog offers broad observability, Mezmo differentiates by focusing on pre-processing telemetry to control costs and improve AI accuracy. Compared to Splunk, Mezmo provides usage-based pricing without data caps and a simpler setup, but it faces similar challenges in search speed for vast datasets and UI modernity.
Key trade-offs include a complex initial setup that may require specialized training, slower search speeds when querying very large datasets, and an outdated user interface compared to newer competitors. The platform's strength in reducing noise and costs comes at the expense of needing to configure pipelines and context engineering rules, which can be a barrier for teams without dedicated observability expertise. Additionally, while the platform excels at real-time analysis, it may not be ideal for teams that need deep ad-hoc historical analysis on raw, unprocessed data.
How it works
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Active Context
Enables operational AI in minutes by providing context-engineered data sets, allowing AI agents to operate with clarity and precision.
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Active Analysis
Processes telemetry in-stream to accelerate Root Cause Analysis (RCA) with an agentic SRE, extracting key information and spotting anomalies before storage.
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Active Engagement
Allows developers to access and act on high-context telemetry directly in their workflows using AI, without compromising budget or performance.
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Active Routing
Directs data with intent, reshaping and normalizing it for human and AI consumption, including seamless migration to OpenTelemetry.
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Data Profiling
Provides in-depth understanding of telemetry data structure and content, enabling separation of signal from noise and optimization for intended use.
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Log Volume Reduction
Filters, transforms, and routes telemetry data before it reaches observability tools, reducing costs by up to 80% through removal of low-value data.
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AURA Agent Control Plane
An open-source control plane that orchestrates agents, which get smarter with every incident, enabling automated detection and response.
Strengths and trade-offs
Strengths
- Reduces observability costs by filtering noise before ingestion, claiming up to 80% cost reduction and 90% reduction in AI observability costs.
- Improves AI accuracy by feeding it structured, context-aware data, enabling AI agents to reason over curated signals rather than unstructured logs.
- Accelerates mean time to resolution (MTTR) by detecting and responding to incidents faster through in-stream analysis and automated responses.
- Provides a Welcome Pipeline that derives business insights from Kubernetes logs in less than five minutes, reducing time-to-value for new users.
Trade-offs
- Complex setup requires specialized training to configure pipelines and context engineering rules effectively.
- Slower search speeds for vast datasets compared to some competitors, as noted in Splunk alternative comparisons.
- Outdated user interface that may not match the modern design and usability of newer observability tools.
- Need for specialized training to fully leverage the platform's advanced features, such as data profiling and agent orchestration.
Pricing context
Usage-based pricing without data caps, with tiered options from free community access to an all-encompassing Enterprise package.
Getting started with Active Telemetry
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Sign up for Mezmo
Go to the Mezmo website and create an account. Choose a tier that fits your needs, from free community access to an Enterprise plan. Complete the registration and verify your email to activate your account.
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Connect your data sources
Install the AURA agent on your infrastructure, such as Kubernetes clusters or servers. Configure the agent to collect telemetry data from your applications, microservices, or AI workloads, and point it to your Mezmo pipeline.
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Configure pipeline processors
Use the Mezmo console to set up data profiling and pipeline processors. Define rules to filter low-value logs, apply sampling, and enrich data with context. This reduces noise and prepares telemetry for AI consumption.
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Run the Welcome Pipeline
Activate the Welcome Pipeline for Kubernetes logs. This pre-built pipeline derives business insights within five minutes, giving you immediate value. Review the output to verify that data is flowing and context is applied correctly.
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Set up active analysis alerts
Configure Active Analysis to process telemetry in-stream. Define anomaly detection rules and automated responses. Test the alerts by triggering a known event, then adjust thresholds to balance sensitivity and false positives.
Frequently Asked Questions
What is Active Telemetry and how does it work?
Active Telemetry by Mezmo transforms passive telemetry pipelines into AI-ready systems. It enriches, filters, and routes data in real time for proactive operations. The platform uses four pillars: Active Context, Active Analysis, Active Engagement, and Active Routing to enable AI-driven observability.
How does Active Telemetry reduce observability costs?
Active Telemetry reduces costs by filtering and transforming telemetry data before it reaches observability tools. It removes low-value data and applies sampling to cut noise. Mezmo claims up to 80% cost reduction for general observability and 90% for AI observability through context engineering.
What are the four pillars of Active Telemetry?
The four pillars are Active Context for AI-ready data sets, Active Analysis for in-stream root cause analysis, Active Engagement for developer workflow integration, and Active Routing for data reshaping and OpenTelemetry migration. These pillars enable proactive, AI-driven observability across distributed environments.
How does Active Telemetry compare to Datadog and Splunk?
Active Telemetry focuses on pre-processing telemetry to control costs and improve AI accuracy, unlike Datadog's broad observability. Compared to Splunk, it offers usage-based pricing without data caps and simpler setup. However, it has slower search speeds for large datasets and an outdated UI.
What is the AURA agent control plane in Active Telemetry?
AURA is an open-source control plane that orchestrates agents, which get smarter with every incident. It enables automated detection and response to anomalies. This helps teams manage telemetry pipelines more efficiently, reducing manual effort and improving incident response times.
What are the main weaknesses of Active Telemetry?
Active Telemetry has a complex setup requiring specialized training for pipeline configuration. It also has slower search speeds for vast datasets and an outdated user interface compared to newer tools. These factors can be barriers for teams without dedicated observability expertise.
Alternatives
How Active Telemetry compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Active Telemetry
- Pricing
- Usage-based pricing without data caps, with tiered options from free community access to an all-encompassing Enterprise package.
- Target
- Active Telemetry, offered by Mezmo, is a platform that transforms telemetry pipelines from passive data collectors into living, AI-ready systems.
- Strength
- Reduces observability costs by filtering noise before ingestion, claiming up to 80% cost reduction and 90% reduction in AI observability costs.
- Watch for
- Complex setup requires specialized training to configure pipelines and context engineering rules effectively.
Datadog
- Pricing
- $15/host/month base, +$5/log/million events
- Target
- Enterprises with cloud-native stacks
- Deployment
- SaaS
- Strength
- Unified dashboards for infra+apps
- Watch for
- Costs escalate with volume
Grafana Labs
- Pricing
- $299/user/month (Pro), Custom for Enterprise
- Target
- Open source-centric teams
- Deployment
- Self-hosted or Cloud
- Strength
- Flexible visualization with OSS roots
- Watch for
- Alert fatigue management required
Honeycomb
- Pricing
- $99/service/month starter, volume discounts
- Target
- DevOps debugging workflows
- Deployment
- SaaS
- Strength
- High-cardinality trace analysis
- Watch for
- Learning curve for event queries
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Sources
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