In the dynamic world of enterprise cloud, strategic partnerships are the bedrock of innovation and efficiency. We sat down with Dominic Jainy, an IT professional with deep expertise in AI and cloud infrastructure, to dissect the recent Strategic Collaboration Agreement between CloudBolt and Amazon Web Services. Our conversation explores the tangible impacts of this deepened alliance, from the front lines of sales engagement to the complex art of Kubernetes optimization. We’ll delve into how this collaboration addresses the persistent challenges of hybrid cloud governance, accelerates the journey from financial insight to decisive action, and charts a future where AI-driven automation is not just an ambition, but a practical reality for platform engineering and FinOps teams alike.
Your new Strategic Collaboration Agreement with AWS deepens a relationship that included the ISV Accelerate program. How does this change the day-to-day engagement between your sales teams and theirs, and what practical benefits should a joint customer expect from this closer commercial alignment?
It’s a significant evolution from a programmatic relationship to a truly strategic, multi-year commitment. The ISV Accelerate program was an excellent framework for co-selling, but this new agreement embeds our teams much more deeply. On a day-to-day basis, this means our sales and leadership functions are no longer just coordinating, they are collaborating on a shared vision for customer success. For a joint customer, the experience becomes incredibly streamlined. Imagine having a single, cohesive team from both CloudBolt and AWS approaching your challenges, from initial architecture discussions to procurement through the AWS Marketplace. The friction disappears, and you get a unified solution that feels less like two vendors and more like one dedicated partner focused on solving your operational and financial goals.
You’ve highlighted StormForge’s “intelligent rightsizing” for Kubernetes. Could you walk us through how this process complements native AWS services? Please provide an example of the specific efficiency gains or cost savings a platform engineering team might see when managing containerized workloads at scale.
This is where specialized intelligence really shines. AWS provides a phenomenal foundation with its container services, but managing the resources for thousands of constantly changing microservices at scale is a monumental task. That’s where StormForge’s “intelligent rightsizing” comes in as a complementary layer of optimization. Think of a large platform engineering team running a critical e-commerce application on Kubernetes on AWS. They often over-provision resources to avoid performance issues during peak traffic, which creates a huge amount of waste. StormForge doesn’t just look at static configurations; it analyzes the application’s actual behavior to find the perfect balance. It provides precise, actionable recommendations to reduce CPU and memory allocation without risking performance, achieving smarter Kubernetes efficiency at scale. We’re not just guessing; we’re using intelligence to safely and continuously eliminate waste.
The collaboration is said to cover hybrid and multi-cloud operations. How does a deeper partnership specifically with AWS help an enterprise gain better governance and cost control over their entire IT estate, including infrastructure that may not be on AWS?
It’s a fantastic question because it gets to the heart of modern enterprise complexity. Very few large organizations are 100% on a single cloud; they have a mix of public clouds and private infrastructure. By forging a deeper, more integrated relationship with the leading cloud provider, AWS, we establish a powerful center of gravity for governance. When you can master cost control, billing, and optimization on your largest and most complex cloud environment, you create a blueprint for the rest of your estate. Stronger governance on AWS sets the standard. The tools and best practices we develop through this collaboration for AWS environments give teams the clarity and control they need, which they can then apply philosophically and programmatically across their entire hybrid footprint, leading to more predictable spending everywhere.
A key promised outcome is “faster time from cloud insight to action.” Could you describe a common scenario for a FinOps team using your tools? What specific insight might they uncover about their AWS environment, and what does the step-by-step workflow to action that insight look like?
Absolutely. This is about closing the loop between seeing a problem and fixing it. Imagine a FinOps analyst looking at their CloudBolt dashboard. The platform flags a specific business unit’s AWS bill as having a sharp, unexpected increase tied to their containerized workloads. That’s the insight. Instead of just a report, the platform immediately provides the context: StormForge has identified that a recent deployment is inefficiently configured. The workflow to action is seamless. The FinOps team can see the specific rightsizing recommendation and, depending on their policy, can either automatically trigger the change or instantly notify the platform engineering team with all the data needed to confidently apply the optimization. The final step is our BillOps tooling automatically adjusting the internal chargeback for that department, reflecting the savings. The entire cycle, from detection to resolution and accounting, is cut down from weeks of meetings to mere hours, or even minutes.
Your roadmap is now more aligned with enterprise priorities like AI-driven automation. Beyond the current capabilities of StormForge, what are some tangible, AI-powered features customers can expect, and how will these tools further simplify operations and reduce waste across their AWS environments?
We’re incredibly excited about what’s next. While the specifics are still on the roadmap, you can see the direction in what Brendan Dolan, our CFO, said about focusing on “practical improvements that simplify operations.” We are moving beyond just recommendations to true AI-driven automation. Imagine a system that doesn’t just find existing waste but uses machine learning to predict the cost and resource needs of a new application before it’s even deployed to AWS. Think about AI that can detect subtle performance anomalies in your workloads and proactively adjust resources to prevent issues. The goal is to create a self-optimizing environment where AI handles the complex, continuous task of ensuring efficiency, freeing up brilliant engineering and finance teams to focus on innovation rather than just chasing down waste.
What is your forecast for the evolution of FinOps as it intersects with the growing complexity of containerized workloads and AI-driven automation?
My forecast is that FinOps is on the verge of a profound transformation, moving from a reactive, reporting-centric discipline to a proactive, predictive, and automated function embedded directly into the engineering lifecycle. The sheer scale and dynamic nature of containerized workloads make manual cost analysis an impossible task. AI will become the indispensable partner for every FinOps team. It will be the engine that powers continuous optimization, forecasts future spending with startling accuracy, and provides the intelligence needed to make cost-aware decisions in real-time. The future of FinOps isn’t about generating better spreadsheets; it’s about creating intelligent, self-governing systems where financial accountability and operational efficiency are built-in, not bolted on.