Case studies
Costs, at every scale
Enterprise cost attribution, and my own products run on invoices I pay myself. Same discipline at every size: find the unit that matters, measure it honestly, then change the system.
My own practice · 2025–2026
4 checks
between a proposed change and production
How I manage AI-assisted delivery without losing quality
The operating model behind three products I build and run solo: scoped work, gates with attribution, no spend in the toolbelt, verification before anything ships, and a written record of every decision.
TendForm · Jun–Sep 2026
11 wks
first commit to a HIPAA-capable SaaS
A HIPAA-capable SaaS, shipped solo, priced against its own cost stack
A form builder with multi-form packets, a markdown DSL, a 27-tool MCP server, and a HIPAA tier, built and run solo in 11 weeks. The cost decisions and the verification habits mattered as much as the code.
TrendVesting · Feb 2024–present
+54.7 pts
stated confidence above the realized win rate
When cheap measurement overturned 2.5 years of beliefs about an AI product
A production AI signals platform I've run since February 2024. When I required every change to show its measurements, the numbers said the confidence scores meant nothing. That's the most valuable finding of the whole project.
TigerMill · Sep 2026
≈ $1
for the first 8 measured image-model calls
An AI content factory in 33 hours, and the metric that actually matters
A webtoon production pipeline with five gated stages, built over a weekend with acceptance rules written before any output was judged. Image generation is cheap per call; what it costs per accepted panel is the number nobody tracks.
Roku · 2020–2021
$10–12M
AWS spend attributed per quarter
Attributing $10–12M a quarter of AWS spend, mid-migration
A cost-attribution pipeline for AI/ads forecasting workloads, built while co-leading the move from ECS/EC2 to Kubernetes. Cost data treated as an engineering artifact, not an accounting afterthought.
Big tech · Big tech
~700K
servers under financial attribution
Making misattributed assets visible across a ~700K-server fleet
Financial attribution for private cloud infrastructure that runs internal AI workloads, plus internal platforms that make livability, deployment state, and cost visible to the people who own them.
Open source · Sep 2026
12–14 pts
disagreement between two metering rules on one tenant's H100 bill
An open-source AI spend ledger, checked on a real H100 before anyone quoted it
unalloc joins OpenCost allocations with LiteLLM, OpenAI and Anthropic bills and reports the AI spend nobody owns. Before trusting its numbers, I pushed six case studies through it, validated the metering results on a rented H100, and gated the paper's own build.
Method
How the work goes
- 01
Attribute
Map spend to owners, systems, and units. Nobody owns a cost they can’t see.
- 02
Instrument
Measure cost inside the system, next to quality and latency, instead of reconciling it after the invoice.
- 03
Verify
Scope the change, review it, and check it live: tests and invariants for the logic, a named human on anything subjective or paid.
- 04
Make it legible
Ship a surface finance, engineering, and executives can all read, and act on.
Want a number you can defend to finance?
Rates & fit check