Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I currently lead Sales and Customer Success at Amnic, an AI-driven FinOps platform. My focus has been on helping enterprises get real visibility into their multi-cloud costs, fix Kubernetes inefficiencies, and turn financial operations into measurable business outcomes.
Over the years, I have worked across SaaS sales, pricing strategy, and FinOps consulting, mostly with large cloud spenders on AWS, Azure, and GCP. That mix of commercial and technical exposure has helped me see how FinOps maturity evolves inside real organizations, from spreadsheets and dashboards to predictive and automated governance.

Q2. How large is the global FinOps market today, and what CAGR are you seeing for AI-driven FinOps solutions over the next few years?

The global FinOps and cloud cost management market today is valued at around 13 to 15 billion dollars, growing at a roughly 11 to 17 percent CAGR.
The interesting part is how fast the AI layer is growing. AI-driven FinOps tools are expanding closer to 25 to 35 percent CAGR as enterprises move beyond visibility to prediction and automation. With AI workloads, Kubernetes growth, and rising cloud complexity, CFOs and CTOs now want platforms that can tell them not just what is happening, but what is likely to happen next.

Q3. How are predictive analytics, anomaly detection, or unit-economics modeling changing what enterprises expect from FinOps platforms?

A few years ago, FinOps was mostly about reports and dashboards. That has changed completely.
•    Predictive analytics now drives planning, helping teams forecast spend and spot risks early.
•    Anomaly detection has evolved from simple alerts to contextual systems that identify root causes and trigger automated remediations.
•    Unit economics has become the new focus. It shifts the conversation from “What is my EC2 cost?” to “What is my cost per user, per transaction, or per feature?”
The conversation has matured from visibility to actionability and from finance-only to shared ownership across engineering and product.

Q4. Where do you see the biggest untapped opportunities for FinOps adoption by geography, industry, or maturity level?

•    Geography: India and the broader APAC region are at a breakout point. With data-residency rules, AI adoption, and hyperscaler expansion, FinOps is quickly becoming essential.
•    Industry: AI-heavy sectors like ad tech, gaming, and fintech face cost volatility tied to inference and model training. Their FinOps maturity has not yet kept pace with their scale.
•    Maturity band: The biggest whitespace is mid-to-large spenders, roughly 5 to 50 million dollars in annual cloud spend. They have outgrown spreadsheets and native tools but have not yet formalized their FinOps practice.
That is the segment where we see the fastest ROI and most visible impact.

Q5. Across India, the US, and the EU, what differences stand out in how enterprises evaluate FinOps platforms?

•    India: ROI-first mindset. Fast time-to-value, local billing, and automation are key. Teams want precise results without long onboarding cycles.
•    US: Focus on security, integrations, and analytics depth. FinOps is often driven by engineering, so tooling must fit into developer workflows.
•    EU: Data locality and auditability matter a lot. ESG and sustainability metrics are also becoming part of the evaluation.
Each region reflects its cloud maturity curve. India is cost-focused, the US is capability-led, and the EU is compliance-oriented.

Q6. Which companies are shaping the AI-driven FinOps market right now, and what sets them apart?

A few stand out for different reasons:
•    Apptio Cloudability and Kubecost are strong in enterprise allocation and Kubernetes governance.
•    CloudZero is built around unit economics, offering a product-centric view of FinOps.
•    FinOut has focused on governance and anomaly intelligence.
•    Vantage emphasizes simplicity and speed, which resonates with engineering-led teams. And of course, the hyperscalers themselves, such as AWS, Azure, and GCP, are improving their native cost tools quickly, pushing third-party platforms to innovate with AI-based automation, forecasting, and deeper cost attribution.

Q7. If you were an investor evaluating companies in this space, what critical question would you ask their senior management?

“How will you stay differentiated once hyperscalers close the native feature gap and AI becomes default everywhere?”
I would also ask a few follow-ups:
•    What percentage of your new wins replace native tools, and why do customers switch?
•    How much of your value comes from automation and allocation depth rather than reporting?
•    What is your track record on forecast accuracy and anomaly response time?
•    How strong is your marketplace presence and partner-led pipeline?
These questions reveal who is building a true platform advantage versus who is just repackaging cloud billing data with a new interface.