Tim Urista
AI Infrastructure & Unit Economics. I make compute fleets legible, and I make their costs attributable — from $10–12M/quarter cloud spend to the per-signal economics of the AI systems I run myself.
"Nobody owns a cost they can't see."
I'm a cloud and AI infrastructure engineer who has worked in big tech at Apple, Meta and Roku, including on fleet-scale private cloud infrastructure for internal AI workloads, and I build and run my own products. My through-line for the last decade has been a single discipline: cost attribution. Cost data is an engineering artifact, and most organizations still treat it like an accounting afterthought.
The arc is rare, and it spans three scales. At Roku I built a cost-attribution pipeline handling $10–12M/quarter of AWS spend and co-led the ECS→Kubernetes migration. At Apple I worked on financial attribution correcting misattribution across roughly 700,000 servers, and on internal platforms used weekly by hundreds of people across finance, SRE, and leadership. And as a founder, I carry unit economics down to my own invoices — TrendVesting has run in production since February 2024, on an invoice I pay myself, and measuring it honestly overturned some of my own numbers.
Every product I build has teaching DNA in it: making complex systems legible to the people who have to live with them. FinOps is the community I build in; AI Infrastructure & Unit Economics is the identity I own. Home is Temecula — my wife, our two daughters, and the pets who are the reason the invisible infrastructure has to stay invisible.
What I Do
Cost Attribution & FinOps
- • Fleet-scale financial attribution (~700K servers)
- • Cloud spend pipelines ($10–12M/quarter)
- • AI unit economics: cost per inference / signal / request
- • Showback & misattribution correction
- • LLM-assisted cost-hierarchy labeling
AI & Cloud Infrastructure
- • Private cloud backbone for AI workloads
- • Kubernetes & ECS→K8s migrations
- • Health-check & failure-pattern detection
- • Multi-mode Go / Python backends
- • Production AI reliability & observability
Product & Platform (UX)
- • Internal platforms used weekly by hundreds of people
- • Finished experiences, not one-off features
- • React/Vite, Next.js, Astro, FastAPI
- • Making cost data legible to non-engineers
- • MCP integrations & markdown DSLs
Foundations
- • Cost data as an engineering artifact
- • Teaching: making complex systems legible
- • HIPAA / compliant architecture (BAA chains)
- • Internal tooling as product with users
- • Georgia Tech MS CS coursework
The Arc
Senior Cloud Engineer
Big techApple
Worked on fleet-scale private cloud infrastructure for internal AI workloads: financial attribution correcting misattribution across ~700K servers, and internal platforms that made livability, deployment state, and cost visible to finance, SRE, and leadership.
The Theme Begins
RokuCost attribution as observability for money
Built a cost-attribution pipeline handling $10–12M/quarter of AWS spend and co-led the ECS→Kubernetes migration. Where I learned that cost data is an engineering artifact, not an accounting afterthought.
The Climb
HealthTrio → MetaHealthTrio, Weedmaps, IterateAI, Meta
HealthTrio (Java/SOAP, first HIPAA exposure), Weedmaps, and IterateAI (no-code ML in the Word2Vec era). At Meta, built a CLI tool for upstream service tracing adopted well beyond my team — internal tooling is a product with users, not a script with tolerance.
Teaching Roots
Before TechBASIS Charter School, Tucson · Occidental College
Taught 6th grade and built a Python grammar game for my students — my first real product. Studied philosophy and religious studies. Every product since has teaching DNA: making complex systems legible to the people who live with them.
Values & Operating Principles
Cost is an engineering artifact
Nobody owns a cost they can't see. Cost attribution is observability for money — instrumented into the system, not reconciled after the fact.
The invoice tells the truth
The diagram tells you how the system works; the invoice tells you the truth. Every architecture gets validated against real spend.
Finished experiences, not features
Full-stack surfaces other teams operate themselves. Make the complex legible to the people who have to live with it — that's the teaching DNA.
Own the identity, rent the title
AI Infrastructure & Unit Economics is mine regardless of any job title. FinOps is the community and the moat, not the headline.
Press Kit
Bio Variants
Tim Urista works in AI Infrastructure & Unit Economics — making compute fleets legible and their costs attributable. A cloud infrastructure engineer with past big tech experience at Apple, Meta and Roku, and founder of TendForm and TrendVesting, he has carried cost attribution from $10–12M/quarter cloud spend to ~700K servers to his own production invoices.
Tim Urista's discipline is AI Infrastructure & Unit Economics: he makes compute fleets legible and their costs attributable. At Apple he worked on fleet-scale private cloud infrastructure for internal AI workloads, including financial attribution spanning roughly 700,000 servers. Earlier, at Roku, he built a cost-attribution pipeline handling $10–12M/quarter of AWS spend and co-led an ECS→Kubernetes migration. He carries the same rigor to his own products — TrendVesting, an AI system in production since 2024, and TendForm, a HIPAA-tier SaaS. He writes about cost attribution and is active in the FinOps community. "Nobody owns a cost they can't see."