They track AI adoption.
We measure quality.
Swarmia tells you how many engineers are using AI tools and how fast PRs are merging. That answers "are they using AI?" Every team's answer is now yes. The harder question — the one that predicts incidents, identifies who's upleveling, and tells you whether your AI standards exist — is whether they're using it well. That's where Maestro looks.
Adoption is table stakes. Quality is the differentiator.
Swarmia Tracks Who Uses AI.
Maestro Measures How Well.
Swarmia has a genuine insight: tracking AI tool adoption alongside traditional engineering metrics gives you a cleaner picture of how your team operates. The dashboards are well-designed. The data is accurate. It answers "are they using AI?"
But "are they using AI?" is now the easy question. The hard question — the one that predicts who's going to cause an AI-generated incident, who's quietly becoming your best engineer, and whether your quality standards exist at all — is "how well are they using it?" That lives inside the work, in the conversation between engineer and agent where quality is made or broken. Maestro was built to answer it.
Adoption Dashboards vs. Quality Intelligence
Swarmia sees the output layer: PR velocity, commit frequency, cycle time, and which AI tools are being used by which engineers. It's useful context. It's just not where the quality decisions are made.
Maestro sees inside the work: how engineers are directing AI tools, whether they're prompting with discipline or accepting suggestions blindly, who is developing craft and who is stuck. That's where the quality divergence between your best and worst AI users is actually visible.
Two Philosophies
What Swarmia Tracks
- PR velocity, cycle time, focus time metrics
- AI tool adoption rate per engineer and team
- Research-backed engineering health dashboards
- Who is using AI tools, and how often
Clean dashboards. Stops at "are they using AI?"
What Maestro Measures
- How your engineers use AI tools — quality, not just volume
- Who is developing AI craft vs. stuck in copy-paste loops
- Whether your team's skills are improving or plateauing
- What your best engineers do — made visible and teachable
Maestro answers "are they using AI well?"
Read the full argument.
Eng metrics are dead. →Go beyond adoption metrics
Join engineering leaders who moved past "who is using AI" and started measuring "how well." See how Maestro surfaces intelligence across your org — who's upleveling, who needs coaching, and what your AI standards actually are.
Trusted by engineering leaders at Runway, AimLabs, and more