Should an Indian business build on an Indian LLM or a global one? It’s the wrong question, slightly — the better one is which model for which task, and how to keep your options open as a fast-moving landscape shifts. Here’s a grounded take. (dgm implements osFoundry, a separate company’s platform — dgm is an independent integration partner, not osFoundry, and isn’t tied to selling any one model.)
At a glance
| Indian LLMs (e.g. Sarvam) | Global LLMs (OpenAI, Anthropic, Google, etc.) | |
|---|---|---|
| Strength | 22 Indian languages, voice, translation | English reasoning, breadth, coding |
| India context | Built/hosted in India; INR billing; Sarvam = IndiaAI sovereign model (Apr 2025) | Variable India data residency by vendor |
| Data residency | Can simplify in-country hosting | Depends on the vendor’s India options |
| Cost | Transparent INR pay-per-use (Sarvam) | USD, varies by model/tier |
| Best for | Vernacular CX, transcription, translation | General assistants, analysis, code |
(Sarvam facts per sarvam.ai and its IndiaAI sovereign-model selection; Krutrim pivot per TechCrunch, May 2026. Confirm current status with each vendor.)
Where Indian LLMs win
Indian models are tuned for Indian languages. Sarvam, for instance, builds LLMs plus speech-to-text, text-to-speech and translation around 22 Indian scheduled languages, bills in INR, and was selected by MeitY in April 2025 to build India’s first sovereign foundational LLM under the IndiaAI Mission. For vernacular customer support across Hindi/Tamil/Telugu/Bengali, transcription of Indian-language calls, or translation, Indic-tuned models can clearly outperform general ones — and Sarvam is candid that Indic tasks, not English general-knowledge benchmarks, are its strength.
Where global LLMs win
Global frontier models from OpenAI, Anthropic and Google generally lead on English reasoning, broad world knowledge, and coding. For an English-first analytical assistant or a developer-productivity use case, a global model is often the better engine. India data residency varies sharply by vendor, though — see osFoundry vs ChatGPT Enterprise and osFoundry vs Amazon Q for how differently vendors handle it.
The landscape moves fast
Treat the Indian model scene as fast-changing. Krutrim — India’s first AI unicorn and an early model-maker — pivoted in May 2026 toward AI-cloud services and paused foundation-model work, per reporting. That’s not a knock; it’s a reason to base decisions on current status, not last year’s announcements, and to avoid hard-wiring your business to a single model.
The pattern that usually wins: route, don’t pick
The strongest approach is rarely “choose one model.” It’s routing: Indian-language and voice tasks to an Indic-tuned model, English reasoning and code to a global model — inside one workspace. That needs a model-neutral platform. osFoundry is bring-your-own-key and provider-neutral, so you can plug in both Indian and global models and switch per request, while controlling data residency by self-hosting where required. (osFoundry is a younger platform with limited independent coverage, so validate the model integrations against your use cases.)
How dgm helps
Because dgm implements a model-neutral platform, it isn’t incentivised to push one model. It evaluates Indian and global models against your actual tasks, sets up routing so each task uses the best engine, and handles India residency where it’s required. Pricing is transparent: $399 assessment, $3,999/month implementation, no per-seat fees (INR approximate; model API usage billed at provider rates; 18% GST for domestic clients). Explore the platform at osFoundry, or talk to dgm about the right model mix for your business.
General information. Model capabilities, pricing and company status change quickly — verify with each vendor at the time you decide.