> ## Documentation Index
> Fetch the complete documentation index at: https://docs.factory.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Usage, Cost & Productivity Analytics

> How to measure Droid adoption, control LLM spend, and understand impact using OpenTelemetry and optional Factory dashboards.

Enterprise adoption requires more than a good developer experience—you need to understand **who is using Droid, on what, and at what cost**.

Factory is built around **OpenTelemetry (OTEL)** so you can plug Droid directly into your existing observability stack, with optional cloud analytics for organizations that want a hosted view.

***

## OTEL‑native metrics and traces

Droid emits OTEL signals that capture how it is used across your org.

### Key metric families

Examples of metric categories include:

* **Session metrics**
  * Counts of interactive and headless sessions.
  * Session duration and active engagement time.

* **LLM usage metrics**
  * Tokens in/out per model and provider.
  * Request counts and latencies.
  * Error rates and retry behavior.

* **Tool usage metrics**
  * Tool invocations and execution time.
  * Success/failure rates.
  * Command risk levels proposed and executed.

* **Code modification metrics**
  * Files and lines modified, created, or deleted.
  * Distribution across repositories and teams.

### Traces and spans

Traces can show the lifecycle of a session or automation run:

* Session start → prompt construction → LLM call → tool execution → code edits → validation.
* Spans capture timing and metadata for each step, including model choice, tools invoked, and error conditions.

These signals allow you to build dashboards in systems like **Prometheus, Grafana, Datadog, New Relic, or Splunk** without depending on a proprietary analytics service.

To export these metrics directly to your own OTLP-compatible collector, see [Telemetry Export (OTEL)](/enterprise/telemetry-export).

For a deeper look at the telemetry schema and how it supports compliance and audits, see [Compliance, Audit & Monitoring](/enterprise/compliance-audit-and-monitoring).

***

## Factory cloud analytics (optional)

In cloud‑managed deployments, Factory can provide a **hosted analytics view** for platform and leadership teams.

Typical views include:

* Adoption metrics by org, team, and repository.
* Model usage and performance trends.
* High‑level cost estimates for LLM usage.
* Top workflows and droids by frequency.

These dashboards are built on top of the same signals Droid emits via OTEL; enabling them does not change the underlying telemetry model.

Hybrid and fully airgapped deployments commonly rely solely on **customer‑owned OTEL pipelines** and disable hosted analytics entirely.

***

## Cost management strategies

LLM cost control is a combination of **model policy**, **usage patterns**, and **observability**.

Recommended practices:

<AccordionGroup>
  <Accordion title="Constrain the model catalog">
    Use org‑level policies to limit which models are available.

    * Prefer smaller models for everyday tasks; reserve large models for complicated refactors or design work.
    * Disable experimental or high‑cost models by default.
    * Enforce model choices per environment (for example, cheaper models in CI).
  </Accordion>

  <Accordion title="Tune autonomy and context usage">
    Higher autonomy and larger context windows consume more tokens.

    * Set reasonable defaults for autonomy level and reasoning effort.
    * Use hooks to cap context size or block unnecessary large prompts.
    * Encourage teams to iterate with tighter scopes (for example, specific directories instead of entire monorepos).
  </Accordion>

  <Accordion title="Use OTEL for cost monitoring">
    Feed token and request metrics into your observability stack.

    * Build per‑team and per‑model dashboards.
    * Alert on unusual spikes in usage.
    * Compare cost curves before and after policy changes.
  </Accordion>
</AccordionGroup>

***

## Measuring productivity impact

Cost only matters in the context of outcomes. With OTEL, you can correlate Droid usage with **software delivery and quality metrics** you already track.

Common approaches:

* Link OTEL traces for Droid sessions with CI builds, test runs, and deployment pipelines.
* Measure how often Droid is involved in changes that reduce incidents, resolve alerts, or improve test coverage.
* Use code modification metrics to estimate automation impact (for example, lines of code refactored or migrated).

These analyses are done entirely in your existing observability and analytics stack; Factory’s role is to provide clean, structured signals from Droid.