Your AI/cloud bill jumped after launch.
You need to know which workflow, model, service, or automation path moved.
AI cost architecture review
Olive-One traces cloud, LLM, API, vector, observability, SaaS, and automation spend back to the workflows causing margin leakage, then turns it into a CTO/CFO decision memo.
Built using the FinOps Foundation methodology for cloud cost management, with hands-on AWS, AI workflow, and margin diagnosis experience.
Cost surfaces reviewed
Before you accept delivery, scale usage, change pricing, or rebuild, review the cost, ownership, and operating assumptions behind the app. Olive-One reviews cloud, LLM/API, database, vector, automation, observability, and workflow ownership risk so the economics are easier to explain and operate.
Entry offer
Before you accept delivery, scale usage, reprice, or rebuild, check the cost, ownership, and operating assumptions behind the app. Olive-One reviews cloud, LLM/API, database, vector, automation, observability, and workflow ownership risk so the economics are easier to explain and operate.
This is for you if
Olive-One is for operators who need a practical answer before the next cloud bill becomes a strategy meeting.
You need to know which workflow, model, service, or automation path moved.
The answer needs to connect spend movement to product and customer activity.
Teams need a shared language for cost, owner, margin, and action.
You cannot yet calculate cost per customer, ticket, outcome, or AI action.
Optimize, reprice, cap, rebuild, or shut down the risky workflow.
What Olive-One gives you
The output is not another metrics screen. It is a focused report that lets engineering and finance decide what to do next.
01
Spend traced to features, services, workflows, owners, and usage windows.
02
Specific risks such as retry storms, context growth, logging spikes, or uncapped jobs.
03
Who owns the workflow, budget, alert, decision, and remediation path.
04
A clear decision note for engineering, finance, founders, and technical operators.
05
Ranked fixes by risk, effort, expected impact, and confidence level.
Tools and resources
Pattern Library, Insights, Sample Report, and Methodology stay available for buyers who want to inspect the method before booking.
Library
Named cost-failure patterns for AI and cloud workflows.
ExploreResearch
Short field notes on AI app cost diagnosis and workflow economics.
ReadExample
A synthetic report showing structure, findings, and action plan.
View sampleMethod
The diagnostic method inside Olive-One reviews.
ExploreHow data access works
Olive-One can work from exports, scoped read-only access, or incomplete data with confidence levels clearly marked.
Option 01
Work from billing exports, LLM usage exports, screenshots, service lists, and workflow notes.
Option 02
Use scoped read-only access when the team wants stronger attribution and faster validation.
Option 03
Gaps are documented directly in the report with confidence ratings and next evidence needed.
Pricing and scope
Diagnose one specific AI app cost emergency or review your production cost surface.
First step
Best for founders and technical operators who need a quick diagnosis for one app, MVP, automation, or feature with a real bill or margin question.
Larger scope
For teams with multiple workflows, cloud and AI tools, stakeholder reporting needs, and a real margin question.
Send the rough stack, recent spend movement, workflow description, and what decision you need to make. No credentials or secrets are needed for the first call.