Olive One is run by Orlando Lopez. I read AI and cloud bills for a living and turn them into decisions.
The practice exists for a specific problem: teams are shipping cloud, LLM, SaaS, observability, and automation workflows faster than their economic controls mature. Olive One turns that spend into decisions a CTO and CFO can act on.
Who is behind it
Founder/operator, not a generic dashboard
I’m Orlando Lopez — a Senior FinOps & IaC Platform Engineer and FinOps Certified Practitioner (FinOps Foundation). I managed enterprise cloud cost at World Kinect and Compass (NYSE: COMP), and previously founded SINAPTIC, a network and cloud infrastructure consulting practice.
The work is operator-led: I read the bill, map the workflow, identify the owner, and turn the finding into a decision memo. No juniors, no handoffs — the person on the fit call is the person who does the review.
Why it exists
AI changed the cost surface
Cloud cost control used to focus on infrastructure waste. Production AI adds model calls, retrieval, retries, observability, agent loops, SaaS automation, APIs, and margin risk that do not fit neatly inside one vendor dashboard.
How Olive One thinks
Cloud and AI spend should be tied to business outcomes, not only services and invoices. A useful AI feature can still damage margin if retries, model routing, context growth, logging, storage, or ownership gaps scale faster than successful outcomes.
Olive One follows the FinOps Foundation methodology for cloud cost management, then applies that discipline to AI app cost diagnosis and margin leak reviews. Orlando is a FinOps Certified Practitioner (FinOps Foundation).
The core question is not only "where can we save money?" It is: what should the business keep, optimize, cap, reroute, reprice, govern, or shut down?
What Olive One does
AI Spend-to-Margin Audit
Olive One starts with an AI Margin Map for founders and operators who need to know what the app will actually cost to operate. The output is a decision memo with cost findings, owner mapping, margin risks, and next actions.
What Olive One does not do
No inflated promises
Olive One is not a prompt-engineering agency, generic AI strategy deck, managed dashboard build, procurement-only cost review, or savings-guarantee offer. It does not claim client logos, certifications, or outcomes that are not public and verifiable.
Sensitive data handling
No credentials, API keys, tokens, service account files, or production secrets are requested in the first call. If there is a fit, Olive One asks for the minimum billing, usage, launch-date, and workflow context needed to answer the economic question.
Preferred inputs are exports, screenshots, sampled usage data, sanitized workflow notes, or scoped read-only reports where available. The goal is data minimization: enough context to make a decision, not broad access to your systems.
Best fit
- B2B SaaS teams with production or near-production AI workflows.
- CTO/CFO teams trying to explain an AI, cloud, SaaS, observability, or automation spend spike.
- Engineering, finance, and product leaders who need one decision memo before building heavier infrastructure or changing pricing.
- Teams willing to share scoped billing and usage context after the fit call.