Operations | Monitoring | ITSM | DevOps | Cloud

GPUs are no longer a niche component. Gamers know them for immersive graphics, workstation users rely on them for balanced performance, and in the age of AI, GPUs have become one of the most in-demand resources in modern infrastructure. They are also expensive. That reality creates two immediate constraints, for individuals and enterprises alike: GPU-backed instances should be provisioned deliberately, and once provisioned, they should be used efficiently.

India is no longer just a fast-growing cloud market; it is becoming a strategically vital one. What was once a race for cost efficiency and global hyperscaler expansion has evolved. Today, India’s cloud landscape is being reshaped by a new reality: the need for AI infrastructure, true data sovereignty, and the ambition to own its digital future. Following the discussion at Civo Navigate India 2025, one thing is clear: the status quo is shifting.

Gartner has predicted that 2026 will see a 10.6% increase in India’s total IT spend from 2025 (2025: USD 159 billion vs 2026: USD 176.3 billion), with data centres, cloud infrastructure, and AI-enabled technologies driving this growth. This isn’t just a budget increase; it’s a fundamental shift in where innovation happens, who owns the infrastructure, and how we translate AI potential into scalable impact.

Data is the backbone of the modern economy, but if you don’t have control of the infrastructure, you don’t control the data. Historically, data sovereignty was a compliance checkbox, something for the legal team to handle. Today, it’s a strategic national priority. At Civo Navigate, we sat down with industry experts to unpack why India is now placing sovereignty at the centre of its digital strategies.

Accessing high-performance GPUs shouldn’t feel like a bottleneck. Yet, as AI adoption accelerates, many teams are discovering that hyperscaler offerings often come with a hidden price: long wait times, opaque billing, and layers of unnecessary complexity. At Civo, we’ve seen a different way. Our GPUs enable companies to move faster while keeping infrastructure overhead and costs firmly under control.

As organizations increasingly embrace digital transformation, cloud agility has become a critical priority. Yet, the promise of cloud-native speed and flexibility often comes with trade-offs: loss of control, unpredictable costs, and operational complexity. Many companies find themselves stuck between the desire for agility and the reality of legacy infrastructure or regulatory constraints. At Civo, we don't think you have to choose. We’ve spent years helping teams navigate this tension.

Back in 2024, I officially put a name to a concept at Civo we had been developing for many years. I called it cloud parity. When Civo was incepted, two completely different worlds existed, the public cloud dominated by Amazon, Microsoft and Google, and the private cloud dominated mainly by VMware.

When an organization talks about sovereignty it is usually about where its data lives, who can touch it and how it is protected. Adding air‑gap to the discussion often turns the conversation into a binary: either the system is completely cut off from the outside world or it isn’t. In practice the reality sits somewhere in between.

As the Indian cloud market continues to grow, businesses are increasingly looking for ways to manage their cloud costs effectively while ensuring data sovereignty. At Civo, we've seen firsthand how our cloud and AI platform can help companies achieve these goals. In this blog, we'll explore how three of our customers in India - KubeNine, BeezLabs, and OpsMx - have leveraged Civo to improve their cloud costs and data sovereignty.

Josh Mesout (Chief Innovation Officer at Civo) took the main stage at Civo Navigate London 2025 to deliver a critical message: The AI revolution isn't just coming, it's here, and the way companies are built is changing faster than ever before. His session cut through the hype, delivering hard data on what separates the companies that scale AI from the ones that sink money into failed prototypes. The takeaway is blunt: The biggest threat to your AI ambition isn't the model; it’s your infrastructure.