Build, Buy, Or Rent? How Indian CTOs Are Rethinking The AI Stack

The AI stack is the set of layers behind an AI product or workflow. It can include data, business context, models, applications, workflows, and the infrastructure used to develop and serve them. The key question is not simply whether to build or buy. It is which layer creates lasting value and which layer can be sourced faster or more cheaply.

For example, a company may rent intelligence from a frontier-model provider while owning its customer data, operating rules, and workflows. It could also buy a ready-made solution for fulfilment or customer support, then build the integration and controls itself. Buddharaju said development workloads can differ greatly from customer-serving workloads at scale.

These choices can change as technology evolves. Rai argued that most companies should rent models unless regulation requires ownership, because model leadership can change within weeks. Kuropati, however, said companies should own their data and context, which can become a durable moat even when the underlying technology changes.

Indian CTOs are deciding what to own because AI adoption is moving from experiments into core business processes. The choice affects cost, speed, control over data, operational risk, and the ability to create a lasting advantage. A ready-made tool may accelerate deployment, while an internal capability may better fit a company’s domain and workflows.

The roundtable showed why the answer differs by use case. Soni said systems handling customer money need accuracy built on 10-year-old operational context. Singam said every deployment should be linked to a business metric, such as fulfilment, returns to origin, users, or technology costs. Rai preferred renting models when flexibility mattered more than ownership.

The reported 87% adoption figure adds urgency, but it does not settle the ownership question. It indicates active use of AI solutions, not that every company builds its own models or captures financial returns. As frontier models evolve, leaders must repeatedly reassess what creates value internally and what is better obtained externally.

The 87% figure comes from a December 2025 government backgrounder citing Nasscom’s AI Adoption Index. It describes the share of enterprises reported as actively using AI solutions. That makes it an adoption measure: it shows whether organisations are using AI, rather than simply discussing or testing it.

The figure does not, by itself, explain what kind of AI each enterprise uses. It does not say whether companies built models internally, bought ready-made tools, rented model access, or embedded AI into products. It also does not state how deeply AI is integrated, how much companies spend, or whether deployments improve margins.

Its significance is that AI use is already widespread in Indian enterprise settings. Leaders therefore face a more complex next step: choosing the right layers to own or source, protecting valuable context, and proving business value. The article connects this challenge to the CTO Summit roundtable, where technology leaders discussed AI-led systems and organisations.

A model is a general source of intelligence that many companies can access. Business data, context, and workflows are more specific. They reflect how a company serves customers, manages risk, and solves domain problems. Kuropati argued that this company-specific layer can become the moat, while much of the surrounding technology remains similar across providers.

Soni offered a practical example. When customer money is involved, operations must be accurate and built on 10-year-old context. A new model alone cannot automatically reproduce that history, business knowledge, or operating discipline. The model may provide capabilities, but the company’s context determines how those capabilities are safely applied.

This advantage matters as frontier models evolve rapidly. A model that leads today may be overtaken soon, while proprietary workflows and context remain connected to the company’s domain. Enterprises can therefore rent or switch models while preserving the information and processes that make their products distinctive.

Renting intelligence means accessing models from an external provider instead of developing and maintaining a proprietary model. It can suit companies that need advanced capabilities quickly, lack the resources to keep pace with model improvements, or want flexibility to change providers. It also avoids making model ownership the centre of the product strategy.

Rai said an open-source model that leads today could leave a company behind within weeks. Building internally would involve ongoing development and maintenance, while a provider can offer access to changing frontier models. Buddharaju added that developing a product and serving customers at scale can require very different workloads.

The choice is not universal. Rai made an exception where regulation requires companies to run their own models. Businesses may also need stronger internal control when data, accuracy, or domain context is especially sensitive. The article’s broader argument is to own the context and workflows that differentiate the business, while renting rapidly changing intelligence when practical.

AI costs affect more than an engineering budget. They can change a product’s contribution margin, pricing, rollout decision, and ability to serve users profitably. Singam said BigHaat checks which metric an AI deployment moves and whether the effect is positive or negative for contribution margin. That links technical choices directly to business economics.

For example, an AI system might improve fulfilment or reduce returns to origin, but it may also increase technology costs. Leaders must compare those effects rather than celebrate usage alone. If each task or customer interaction costs too much, expanding the system can worsen margins even when adoption rises. If the value created is greater, broader deployment can make economic sense.

This is why serving workloads matter as much as development. Buddharaju noted that building a product and serving customers at scale can require different workloads. Companies must measure costs and outcomes continuously, especially as model providers, usage levels, and business needs change.

In standard machine-learning usage, a model learns patterns from examples and then produces predictions, classifications, or generated outputs from new inputs. Training costs cover the data, computing, and engineering used to create or adapt it. Serving costs cover running the model for users or business tasks. The article does not detail these mechanics, but they explain why AI economics varies.

A larger or more capable model may improve accuracy but require more computing and time per request. A smaller model may be faster and cheaper but perform less reliably on difficult tasks. Data requirements also matter: a company may need extensive, high-quality, domain-specific information to train or adapt a system. These trade-offs shape product design.

The roundtable connects this issue to scale. Buddharaju said product development and customer serving can have different workloads. Singam said deployments must be judged by contribution margin and business metrics. Therefore, accuracy alone cannot decide a product; speed, cost, data, and measurable value must be considered together.