Operations | Monitoring | ITSM | DevOps | Cloud

This blog was released off the back of our white paper, The Cost of Cloud 2025 - India Edition. Research was conducted with over 600 Indian tech professionals to create a comprehensive analysis of the Indian cloud market.

The phrase "cloud first" dominated IT strategy for the better part of a decade. It was gospel, practically unchallengeable, and for a lot of organizations, it was the right call. But something shifted between 2024 and 2026, and it shifted fast. Bills stopped being defensible. Vendor pricing imploded. Sovereignty stopped being a compliance checkbox and became a procurement requirement.

Financial services in the UK operates under one of the most demanding regulatory frameworks in the world. The FCA and PRA between them set expectations for operational resilience, outsourcing, data governance, and concentration risk that shape every infrastructure decision a regulated firm makes. Cloud adoption in the sector has happened, but it's happened under regulatory scrutiny that's grown steadily more pointed over the last several years.

AI workloads have a distinctive infrastructure profile that's exposing the cost and architectural assumptions baked into public cloud over the last decade.

Most organizations know cloud costs are rising. Fewer realize that some of what they're paying isn't for infrastructure at all; it's a penalty for not being in control of it. That penalty has a name: Sovereignty Tax. It isn't a line item on your invoice. It won't appear in your cloud dashboard. But it's accumulating quietly, in egress fees, outage exposure, audit blind spots, and the creeping realization that leaving your current provider would be harder, and more expensive, than you ever anticipated.

Most organizations get the build versus buy decision wrong in the same way. They underestimate the cost of building while overestimating the cost of buying. In the recent Konstruct monthly webinar with M R Rishi (Platform Engineer at Civo), we explored the discussion surrounding whether you should build or buy your platform. If you want to watch the full discussion, watch the recording here.

Here's a scenario that's playing out in engineering teams across the industry right now. A developer uses AI to rapidly prototype a microservice. The code works. They deploy it to production. Six months later, something breaks. The system is under load, a database connection pools, and the service starts failing in subtle ways. The engineer pulls up the code, but here's the problem, they didn't write it. An AI assistant did. They don't understand the flow deeply. They don't know where to look first.

Platform engineering has become one of the most discussed topics in cloud native infrastructure. Yet despite the rising focus, most conversations around platform engineering skip over the uncomfortable truths. What actually works at scale? When should you build versus buy? And how do you avoid the traps that trip up even experienced teams?

Most hybrid cloud strategies fail not at launch but at scale. The architecture works fine for the first year. The team's workloads are modest, the integration points are limited, and the operational overhead is manageable. Then the business grows. Workloads multiply, data volumes climb, the team expands, and the seams between public cloud and private infrastructure start showing.

AI's environmental cost is real, and it's growing. Training a large language model can consume the electricity of hundreds of households for weeks. Inference at production scale runs continuously, with GPU clusters drawing power around the clock. The data centers that house all of this are some of the most concentrated energy consumers in the modern technology stack.