What is the most useful mlops benchmark?

The useful benchmark is the one tied to a decision. Start with coverage, material risk, unit cost, recovery performance, and exception age rather than copying an external average without matching its population.

Can these public statistics be used as targets?

Not automatically. Public studies describe their own samples. Use them for context, then establish a comparable internal baseline and document the denominator.

Updated 2026-10-04 · AI infrastructure

MLOps
cost and efficiency statistics.

A source-first view of unit economics, waste and the engineering effort needed to operate the capability. Published findings provide context; the measurement framework shows what to calculate inside your own environment.

01 / Published evidence

What the public data says.

Each figure links directly to the organization that published it. Vendor telemetry and community surveys are labelled rather than blended into one invented industry average.

63%

of FinOps respondents managed AI spending

The 2025 State of FinOps report showed AI cost responsibility expanding quickly.

State of FinOps 2025 · 2025 ↗

4.3M

AI projects on GitHub

GitHub recorded rapid growth in repositories using LLM SDKs and AI-related projects.

GitHub Octoverse 2025 · 2025 ↗

82%

of container users ran Kubernetes in production

The CNCF annual survey reported production Kubernetes use rose from 66% in 2023.

CNCF Annual Cloud Native Survey · 2026 ↗

02 / Your baseline

Measure the system you actually run.

Use these formulas consistently for at least one complete operating cycle. Keep scope, exclusions and collection dates beside the result.

Unit operating cost

Monthly attributable cost ÷ active workloads or services

Use one stable denominator and record exclusions.

Avoidable spend ratio

Verified waste ÷ total attributable spend × 100

Use one stable denominator and record exclusions.

Engineering effort

Monthly engineer hours spent on routine operation and remediation

Use one stable denominator and record exclusions.

03 / Interpretation

A number without a denominator is decoration.

Segment results by environment, workload owner and business criticality. A falling average can hide one critical service getting worse; a rising count can simply reflect better discovery.

Record the source system and collection window.
Separate discovered, in-scope and actively managed populations.
Keep medians and tail values where averages hide risk.
Annotate architecture, policy and tooling changes.
Recalculate after remediation instead of closing on intent.

Related benchmarks

Look at the adjacent signal.

Want these numbers from your cloud?

Gritt can map the data sources, calculate a defensible baseline and implement the changes behind it.

Build the baseline →

What the public data says.

of FinOps respondents managed AI spending

AI projects on GitHub

of container users ran Kubernetes in production

Measure the system you actually run.

Unit operating cost

Avoidable spend ratio

Engineering effort

A number without a denominator is decoration.

Look at the adjacent signal.

adoption and scale

security risk

operations and reliability

Want these numbers from your cloud?