CloudKeeper empowers fast-growing businesses with guaranteed cloud savings, AI-driven optimization, and expert support, delivering efficiency, visibility, and maximum value without commitments. During an exclusive interaction with Smart Solutions World, Mr. Aman Aggarwal, COO, CloudKeeper, shares insights on AI adoption, FinOps strategies, cloud optimization, and enterprise scalability.
1. Please briefly introduce CloudKeeper and its core offerings.
CloudKeeper is a comprehensive cloud cost optimization and FinOps partner for companies that are scaling fast on the cloud. We help businesses get more value from what they are already spending, without adding complexity or long-term commitments.
Our approach combines guaranteed savings from day one with continuous optimization through our platform-led solutions and expert support. From visibility and automation to commitment management, we cover the full FinOps lifecycle.
We also support customers end-to-end, from consulting and migration to ongoing management and 24×7 support. The idea is simple: help teams move fast on the cloud while staying in complete control of cost and performance.
CloudKeeper has also achieved the AWS AI Services Competency, recognizing our expertise in building and scaling production-grade AI solutions on AWS. This reflects our ability to help organizations move beyond experimentation by integrating AI services into existing environments and deploying them in line with AWS best practices.
We are also equipped to support evolving workloads, including AI, by extending our FinOps and cloud optimization expertise to these environments, helping organizations maintain visibility, control, and efficiency as enterprise AI adoption scales.
2. What does the partnership with Anthropic mean for enterprises?
Our partnership with Anthropic is about making AI adoption more structured and easier to operationalize within existing cloud environments.
Today, many organizations struggle with fragmentation across model access, infrastructure, billing, and governance. This often slows down real progress.
Through this partnership, enterprises can access Claude models directly within Amazon Web Services via Amazon Bedrock, without disrupting their existing setup.
What we bring on top is a FinOps-led approach to AI adoption. We help organizations introduce visibility into usage, align consumption with business needs, and put guardrails around cost and access. This ensures that as teams start using AI, they do so in a controlled and sustainable way.
3. How is demand for Claude AI models evolving in the market?
The conversation has changed completely in the last 12 months. A year ago, most enterprises were asking “should we explore AI?” Today, they’re asking “how do we scale this responsibly?”
Claude is gaining serious traction because enterprises need more than just a capable model – they need reliability, safety, and the ability to handle complex, context-heavy enterprise workflows. Claude delivers that.
What I find interesting is the parallel wave of interest towards cost awareness. Companies are realizing that running AI at scale isn’t cheap, and they’re actively looking for structured consumption models. That’s exactly the intersection where CloudKeeper plays – making sure adoption is not just fast, but financially sustainable.
4. How does CloudKeeper help enterprises adopt AI while optimizing cloud costs?
We don’t treat AI and cost as separate conversations – they go hand in hand.
As organizations begin to run AI workloads, costs can scale quickly without the right visibility and controls. We bring structure into this through real-time insights into usage, cost drivers, and optimization opportunities.
With platforms like LensGPT, teams can interact with their cloud environment in a more intuitive way, quickly identifying inefficiencies and acting on them. CloudKeeper Tuner continuously optimizes resource utilization in the background to reduce waste.
We also help implement guardrails such as usage controls and budget thresholds, ensuring teams can experiment and scale while maintaining control over costs. Through our Generative AI Launchpad, we provide a structured starting point for organizations to pilot and deploy use cases with the right architecture, governance, and cost controls already in place.
Extending FinOps practices to AI workloads is a natural part of this approach, helping organizations manage newer consumption patterns more effectively.
5. What opportunities does AI model resale create for AWS partners?
This is a genuine inflection point for the AWS partner ecosystem. For years, the conversation was mostly about infrastructure – compute, storage, networking. AI model resale changes the game entirely.
Partners can now offer end-to-end AI solutions – combining model access, deployment, cost optimization, and ongoing support. That’s a fundamentally different value proposition, and it creates much deeper customer relationships.
But here’s my honest take – not every partner will succeed at this. The ones who will are those who bring strong FinOps and governance capabilities alongside AI enablement. Without that, AI workloads quickly become a cost problem. With it, they become a competitive advantage.
6. How do you ensure secure and compliant AI adoption for enterprises?
Security and compliance remain central to how we approach any workload.
Since models like Claude are accessed through Amazon Web Services, enterprises can continue leveraging their existing security frameworks, identity controls, and compliance structures.
On top of that, we add governance layers such as role-based access, detailed usage visibility, and continuous monitoring. This helps organizations maintain control over how services are being used and how data flows across systems.
The focus is on enabling adoption while ensuring that security, compliance, and risk management are not compromised.
7. How can FinOps help manage the cost of AI workloads?
AI workloads introduce new and often unpredictable consumption patterns, which makes cost management more challenging.
FinOps provides the structure needed to manage this effectively. It enables real-time visibility into usage, clear cost allocation, and the ability to implement guardrails around consumption.
By extending FinOps practices to AI workloads, organizations can better understand what is driving costs across model usage, supporting infrastructure, and data processing layers, and take proactive steps to optimize them.
CloudKeeper supports this through a combination of platforms and expertise. LensGPT allows teams to interact with their cloud and cost data conversationally, helping them quickly identify inefficiencies and optimization opportunities. CloudKeeper Tuner continuously optimizes infrastructure usage in the background, ensuring resources remain right-sized and cost-efficient.
At the same time, our Generative AI Launchpad helps organizations adopt AI in a structured way from the outset, with the right architecture, governance, and cost controls already in place. Backed by our AWS AI Services Competency, we bring proven expertise in building and scaling production-grade AI solutions, ensuring cost optimization is built into the foundation itself.
Overall, FinOps helps ensure that evolving workloads, including AI, remain efficient, controlled, and aligned with business outcomes.
8. Can you share a recent success story or case study?
We work with both traditional enterprises and new-age AI-powered platforms, helping them scale efficiently while keeping infrastructure costs under control.
In one case, we supported an AI-driven document processing platform on Google Cloud that was facing cost spikes and limited visibility across APIs, BigQuery, and compute. By implementing a structured FinOps framework with real-time dashboards and granular cost attribution, we helped them bring clarity and predictability to their cloud spend.
We also worked with an AI-powered energy intelligence platform running on Kubernetes, where infrastructure instability was impacting operations. By redesigning their logging architecture and improving workload resilience, we eliminated system failures and reduced operational overhead – while keeping the setup cost-efficient.
In another engagement, with an AI-native platform scaling ML workloads, we addressed deep infrastructure inefficiencies – from slow GPU startup times to upgrade bottlenecks. By optimizing their cluster architecture, we significantly improved performance, reduced costs, and enabled faster experimentation cycles.
Across these engagements, applying FinOps principles to evolving AI workloads helped drive better efficiency and control.
9. What key trends do you see in AI-driven cloud adoption?
AI adoption is becoming more structured. Several converging trends are shaping how enterprises approach it.
FinOps for AI is now mainstream – cost governance, token optimization, and financial accountability are no longer afterthoughts. Equally interesting is the rise of AI for FinOps using AI itself to manage cloud costs, which is exactly what LensGPT does.
Multi-cloud AI strategies are growing, with organizations distributing workloads across AWS, GCP, and Azure based on model availability, latency, and cost – making cross-cloud visibility critical.
Multi-model approaches are becoming standard, with teams selecting models based on task complexity and cost profile rather than defaulting to one.
We’re also seeing agentic AI move into production, data sovereignty becoming a procurement filter, and above all a relentless push toward measurable business outcomes over experimentation.
10. What is CloudKeeper’s future roadmap?
Our focus is on becoming the go-to partner for AI and cloud – not as separate disciplines, but as one integrated capability.
We’re expanding the Generative AI Launchpad to support more industries and faster time-to-production, bringing together AI architecture expertise, AWS best practices, managed services, and FinOps governance in one structured engagement.
On the platform side, LensGPT will evolve to include deeper AI cost intelligence — model-level spend analysis, proactive anomaly detection, and token consumption optimization to help teams manage prompt efficiency, context window usage, and model-tier selection at scale. CloudKeeper Tuner will take on increasingly complex workload optimization patterns alongside this.
Our AWS AI Services Competency and Anthropic reseller partnership underpin everything. But ultimately, sustainable AI adoption requires continuous optimization and a partner accountable for outcomes over time. That’s exactly the model we’re building toward.
If you have an interesting Article / Report/case study to share, please get in touch with us at editors@roymediative.com roy@roymediative.com, 9811346846/9625243429
