Case Study
Architecture
FinOps-Aware
AI Systems
Sustainable AI Architecture with OtaskuAI
A practical look at keeping AI experimentation lightweight: selecting infrastructure that matches the maturity of the problem, not the hype level of the week.
Type
Architecture & cost governance
Scope
Deployment tradeoffs + spend visibility
Outcome
Prefer minimal viable infra for exploration
Context
OtaskuAI is exploratory — which means the architecture should be, too. The case study evaluates how to run early-stage AI experiments without accidentally launching a disastrous monthly bill.
Key question
What is the minimum viable infrastructure that still supports meaningful AI experimentation?
Option A — Serverless inference
- Near-zero idle cost
- Cold starts acceptable for low-traffic exploration
- Easy to iterate and roll back
Option B — Persistent endpoints
- Always-on cost regardless of usage
- Good for steady traffic / strict latency needs
- Often premature early in a project
Decision and rationale
For exploration phases, serverless is favored: it supports fast iteration and keeps spending aligned to actual usage. Persistent endpoints can come later — when the problem and demand justify them.
- Infrastructure maturity should follow problem maturity
- Spend visibility prevents “silent budget creep”
- Define infrastructure via IaC to keep changes intentional