Sarvam’s AI Arsenal

What happened

5 Bn valuation. Just a month later, the startup made it clear that the funding was only the beginning of a much bigger ambition. At its inaugural developer conference, Sarvam Epoch, the startup unveiled its vision that it was no longer content with being known as a builder of Indian-language foundation models.

Instead, it now wants to be a full-stack AI venture, providing everything India Inc needs to build, deploy and scale AI — from foundation models and infrastructure to coding copilots, voice agents, document intelligence and workplace applications. This is a major shift from OpenHathi-v1, the open-source Hindi language model.

Today, the startup is building across every layer of the AI stack, spanning foundation models, infrastructure and applications for speech, vision, coding, cybersecurity and defence. What’s more interesting is that its ambitions just don’t stop at software. The Indian AI giant now has hardware, such as smart glasses (Sarvam Kaze), on the cards. But the real question is why it is betting on the entire AI stack.

Besides, can it become the new operating system for Indian enterprises that are already dependent on global players like OpenAI and Anthropic? Let’s try to answer these questions in this edition of The Outline. At its core, Sarvam knows that just building language models for India won’t cut it, and it has to make a bigger bet.

To realise this, it has launched Sarvam Inference, an India-hosted platform for running frontier open-weight models, for enterprises that want to deploy AI while keeping sensitive data within India. 2 and Gemma 4. Sarvam is also building a trillion-parameter frontier model in India. However, Sarvam’s larger ambition is to capture a significant share of India’s AI consumption.

To pursue this and boost its frontier AI push, the Indian AI startup unicorn has appointed Devendra Singh Chaplot, a founding member at Mistral AI and Thinking Machines Lab, and former xAI pre-training lead. It has also opened a new San Francisco office as part of its global expansion. Besides, the startup is betting on competitive pricing, reliability and data security and sovereignty to lock horns with its global counterparts.

Its voice platform, Samvaad, has already handled 325 Mn minutes of customer conversations over the past year. Not just this, its work agents can process large amounts of company data, while its inference platform can run open models from infrastructure located in India. Upping the ante, Sarvam is also building a trillion-parameter model and is scaling its computing capacity to 10,000 accelerators.

But enterprise buying decisions are rarely made on benchmark scores alone. Benchmarks tell you how a system performed on a particular test. They do not tell you whether a large enterprises will trust it with its most important workflows. This matters particularly for products such as coding agents. An engineering team may not choose between two numbers on a leaderboard.

It has developers who have already built habits around tools such as Claude Code and Codex, which have become part of the way teams write, test and review software. One AI founder put the problem bluntly: Codex and Claude Code are already “absorbed into the workflow. For an established team, moving to something new is not necessarily worth the disruption”.

He added that a developer may be impressed by Sarvam Code due to its lower cost, but a CIO is likely to look beyond the benchmark scores and ask a more basic question: does the product fit into its existing systems and workflows, and offer enough value to justify moving away from tools that employees already use?

Sarvam cannot simply tell enterprises that its models are cheaper or match competitors on benchmark scores. It needs to offer a far more compelling reason for businesses to switch. And that is where Sarvam’s real test begins. Sarvam has advantages that no one can deny. For one, Sarvam Inference allows developers and businesses to run open models from infrastructure hosted in India.

Why it matters

But Sarvam wants to do something that sounds simple on paper and is extremely difficult in practice: convince Indian businesses to switch from OpenAI, Anthropic or Google for their AI stack. At its event, Sarvam showed on various fronts that its technology is competitive. For starters, its coding agent, Sarvam Code, performed strongly on several benchmarks. Then, its speech model, Saaras V4, supports all 22 constitutional Indian languages.

What to watch

It is building its computing capacity within the country and says it wants to produce the largest share of the tokens India consumes right here. This matters for government departments, defence organisations or highly regulated businesses. Sarvam’s Anvaya platform, for instance, is being built for defence, national security and intelligence use cases and can run entirely on-premise. Then, for some organisations, keeping sensitive information within India might be a requirement.

And that’s precisely where Sarvam has an edge. Sarvam is also building products around India’s particular linguistic complexity. Saaras V4 supports all 22 constitutional languages, including languages that have very little data available for training.