A weaker rupee has made dollar-priced artificial intelligence (AI) services such as ChatGPT and Claude 10–15% more expensive for Indian startups, pushing founders to adopt both technical and business strategies to control costs.
One way to reduce expenses is by optimizing context memory, a feature that allows AI systems to retain previously generated information, user preferences, and frequently used responses. Instead of sending every query or prompt back to a large language model (LLM), the system can retrieve relevant answers from stored context, reducing token consumption and lowering API costs.
“For example, if a question has already been answered, the system can pull the response from context memory instead of going back to the LLM. This helps reduce costs and improves efficiency. These are some of the technological measures startups can adopt,” says Vaibhav Vats Shukla, founder and chief executive officer (CEO) of AI startups Quansys AI and Sangrah AI.
API stands for application programming interface, which allows one software or application to talk to and use another software’s data. Tokens are the basic unit of data that an AI model uses to understand and process language. Output tokens, generated in response to prompts, are significantly more expensive than input tokens.
Many AI startups are also focusing on prompt engineering to minimize the number of tokens consumed while interacting with foreign AI models. By designing prompts more efficiently and reusing existing responses wherever possible, companies can reduce the number of API calls made to LLM providers, helping contain expenses.
What makes it harder for founders is that, even though the pricing has changed, the service remains the same. Since most AI services are priced in dollars, a weaker rupee means Indian companies end up paying more for the exact same level of usage.
This directly impacts gross margins. The rupee fell 9 paise to 94.94 against the US dollar in early trade on Monday. It has been depreciating due to foreign fund outflows amid the ongoing .
“The challenge is particularly severe because many startups have not factored such currency movements into their financial planning. For companies operating on thin margins, a 10–15% increase in AI costs can significantly affect profitability and runway,” says Kartik Sharma, founder of RankinLLM.
Apart from optimizing token usage and prompt engineering, some founders are also purchasing AI credits in advance to protect themselves from further currency depreciation.
The strategy is based on the assumption that the rupee could weaken further against the dollar in the coming months, making AI services even more expensive. By locking in credits at current exchange rates, startups can shield themselves from future cost increases.
“We believe the rupee may continue to depreciate. If the dollar reaches ₹110 in the next few months, having credits purchased in advance for the entire year helps us lock in costs. We are not doing this aggressively across all operations, but for projects where demand is relatively predictable, we are buying credits in advance,” says Jagmohan Garg, founder of StyleUAI.
Some startups are also attempting to reduce costs by hosting open-source AI models on cloud infrastructure located in India.
While proprietary models offered by global providers cannot easily be moved to local servers, companies are increasingly deploying open-source alternatives on domestic cloud infrastructure to lower operating expenses.
“Approximately 20–30% of our AI workloads are processed at the edge, that is, on local servers. This reduces recurring cloud infrastructure and storage costs while lowering our exposure to USD-denominated usage fees,” says Neerja Kumar, co-founder and chief operating officer of Enalytix, an AI-powered video analytics firm.
Looking beyond India
Apart from optimizing model usage and buying credits in advance, some are exploring overseas markets where pricing pressure is lower.
Indian enterprises often consider a voice agent charging ₹4 per minute expensive, while customers in the US are willing to pay ₹8-9 per minute for similar services, says a founder who didn’t want to be named.
The price war in India has intensified, with some players offering voice AI services for as little as ₹2-2.5 per minute. Against this backdrop, founders say international markets could offer better margins and help offset rising dollar-linked costs. Quansys AI’s Shukla, for instance, is trying to expand to the USA market.
“We are in talks with a few players in the US. Compared to the rates they are currently paying, we have offered them lower charges, so there is a difference. Let’s see how the conversions turn out,” he adds.
Meanwhile, some founders are looking for home-grown alternatives to the western models. “This is actually the best opportunity for us because we don’t use any models from outside. We are doing inference on Indian GPUs, which is probably among the lowest-cost setups in the world,” says Ganesh Gopalan, CEO & co-founder at Gnani.ai, one of the startups selected by the government to make foundational LLMS under the .
Gopalan says the company has seen a 100-fold increase in demand over the past few months, driven by startups that want to build AI applications. “These companies are reaching out to us and asking if we can provide our models so they can build applications on top of them,” he says.