01
Monetization Systems
Usage-based billing, prepaid commitments, subscriptions, pricing, rating, invoicing, and financial correctness.
AI MONETIZATION · DISTRIBUTED SYSTEMS · BACKEND ENGINEERING
Engineering the monetization infrastructure behind AI products.
I’m Shuai Zhang, a backend engineer specializing in enterprise monetization systems. Across seven years in software engineering, I’ve worked on subscription orchestration, usage-based billing, consumption systems, and the infrastructure that turns product activity into reliable commercial records. I’m now applying that experience to the monetization challenges behind AI products.
Illustrative example — not a production system
- Illustrative metering flow from usage event through metering, rating, budget decision, and ledger record
Rated Cost
- $0.0068
Select a stage to see how usage becomes a reliable commercial record. Keyboard and touch supported.
Software Engineer at ZuoraPreviously at ByteDanceM.S. in Computer Science, SMU
WHAT I BUILD
Systems where technical correctness directly affects revenue.
02
Distributed Infrastructure
Stateful orchestration, event-driven systems, high-volume processing, idempotency, reconciliation, and observability.
03
AI Usage Infrastructure
Token metering, per-customer cost attribution, real-time budget enforcement, and consumption controls for AI products.
SELECTED IMPACT
Enterprise-scale monetization engineering.
Selected impact from enterprise monetization systems.
- subscriptions supported
100K+
subscriptions supported
- usage transactions annually
100B+
usage transactions annually
- reduction in state-flow failures
60%
reduction in state-flow failures
- processing performance improvement
75%
processing performance improvement
Metrics reflect work completed in professional roles; details are limited to non-confidential information.
FEATURED WORK
Selected systems, architecture, and experiments.
- 01
Enterprise Monetization Systems
Professional Experience · System Design
Designing reliable usage, commitment, subscription, and billing workflows at scale—with an emphasis on financial correctness, idempotency, reconciliation, and operational resilience.
View engineering case study - 02
AI Usage Infrastructure Exploration
Independent Project · Open Source
An independent open-source exploration of token metering, cost attribution, and real-time consumption controls for AI applications.
FluxMeter is an independent open-source project developed in my personal capacity. The views and work presented here are my own and do not represent my employer.
View exploration
FEATURED WRITING
Essay · AI Monetization
Building Better Monetization Systems
Lessons from enterprise billing infrastructure — charge models, rating pipelines, observability, and why flexibility compounds over years.
Read articleContact
Let’s build the commercial infrastructure behind AI.
I’m interested in engineering roles and technical conversations involving AI monetization, usage infrastructure, billing platforms, distributed systems, and developer-facing products.
Illustrative example — not a production system
- Illustrative metering flow from usage event through metering, rating, budget decision, and ledger record
- Usage Event
- Metered Quantity
- Rated Cost
- Budget Decision
- Ledger Record
Ledger Record
- usage_record_id
- ur_8f21
- status
- reconciled