Telemetry data is the raw material of the entire observability business, and it is the quiet foundation of the Motley Fool’s “One Top AI Agent Stock” pitch. The teased company, Datadog, makes its money by ingesting and analyzing telemetry, so understanding what telemetry actually is, is the fastest way to understand the whole trade. Here is the plain-English explanation. There is more on this in our AI Workloads: What You Need to Know article.
What Telemetry Data Is
Telemetry is data that software and hardware emit about their own operation, the way a car sends its speed and fuel level to the dashboard. Instead of a person opening the hood to inspect the engine, the machine reports on itself continuously. For software, that self-reporting comes in three standard formats: metrics, logs, and traces.
Each format answers a different question. Metrics are numbers, like how many requests a service handled or how long the average one took. Logs are time-stamped text records of specific events, like an error message or a user login. Traces follow a single request end to end, showing every service it touched and how long each step took. Together, the three formats give engineers a complete picture of what a system is doing.
Why the Volume Keeps Growing
The amount of telemetry in the world has exploded for a structural reason: software got more distributed. A decade ago, an application might run on one server and produce a modest stream of data. Today, that same application might be hundreds of small services spread across a cloud, each one emitting metrics, logs, and traces, and all of them talking to each other.
Every one of those interactions is a new source of telemetry, and the volume grows faster than the number of services, because each service now has to report on its conversations with every other service. That growth is the economic engine behind observability companies, which charge based on how much telemetry they ingest and analyze.
What AI Adds to the Equation
The AI agent boom is the newest accelerant. An AI agent is not a passive chatbot; it takes actions. It calls APIs, retrieves data, writes data, and spawns sub-tasks, and every one of those actions generates telemetry. A fleet of agents running continuously produces dramatically more of it than a human clicking through a workflow ever did.
There is also a new kind of telemetry specific to AI. Models report their own behavior: what prompt they received, what they generated, how confident they were, and how long and how much each call cost. That is the data layer that model observability watches, and it is brand new relative to the metrics, logs, and traces of traditional software. Our what is AI observability explainer covers how that layer is monitored.
Who Sells the Tools
The companies that collect and analyze telemetry form the observability market. Datadog is one of the leaders, and it competes with Dynatrace, New Relic, Splunk under Cisco, Grafana Labs, and the native monitoring tools of the big cloud providers. The common thread is that all of them are selling the same thing: the ability to turn an overwhelming stream of telemetry into something a human can act on.
That common thread is also the business model. These companies charge by volume and by module, so as telemetry grows, their revenue grows with it. The AI angle in the Fool’s pitch is essentially a bet that the volume of telemetry keeps climbing as agents multiply. Our AI agent stock teardown checks whether that growth is already reflected in the teased company’s price.
The Bottom Line
Telemetry data is the raw fuel of observability, the metrics, logs, and traces that modern software emits about itself, and AI is adding both volume and a brand-new layer to it. It is the underlying commodity of a growing market, and understanding it is the entry point to understanding the stock story built on top of it.
The Storage and Cost Question
Telemetry is valuable, but it is not free, and that creates a second layer to the business. Collecting metrics, logs, and traces at modern volumes produces a staggering amount of data, and storing and querying all of it is expensive. As a result, almost every company does some form of triage: it keeps high-value data, samples or summarizes the rest, and throws away what it cannot justify keeping. That is why observability vendors do not just sell collection, they sell the intelligence to decide what is worth keeping. The storage question also matters for the economics of the category, because the vendors’ costs scale with the data they ingest, and their pricing has to cover it. Understanding that constraint is the difference between seeing observability as a simple growth story and seeing it as a business with real, data-driven costs underneath the revenue line.
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