Sarvam AI

India's AI platform — voice and text APIs for Hindi, Tamil, Telugu, Bengali, and 8 more Indian languages

AI & LLMs 4.3 / 5 Made in Bengaluru Free API tier Updated Feb 2026

Quick Verdict

Sarvam AI is the most important Indian AI company building language infrastructure for Bharat — the 800 million+ Indians who are more comfortable in Hindi, Tamil, Telugu, Bengali, Kannada, Gujarati, Marathi, Malayalam, Odia, or Punjabi than in English. Founded in 2023 in Bengaluru by IIT Madras alumni Vivek Raghavan and Pratyush Kumar, Sarvam builds and provides models specifically trained on Indian languages, not general multilingual models with Indian languages as an afterthought. For Indian product teams building for Tier 2 and Tier 3 users — particularly in fintech, agritech, healthtech, and government services — Sarvam's APIs for speech-to-text, text-to-speech, translation, and Indian-language LLM are the foundation for genuinely inclusive Indian products. OpenAI and Google have Indian language support; Sarvam has Indian language expertise.

Indian Language Quality
4.8
Voice / STT / TTS
4.7
API Ease of Use
4.3
English LLM quality
3.2
Pricing Value
4.6

What is Sarvam AI?

Sarvam AI is an Indian AI startup founded in 2023 and headquartered in Bengaluru. It builds and operates large language models, speech models, and translation models specifically optimised for Indian languages. The company received backing from the Indian government's IndiaAI Mission and has positioned itself as India's sovereign AI infrastructure play — models that understand Indian languages natively, trained on Indian data, hosted on Indian servers, with pricing in INR.

Sarvam's API suite covers four core capabilities: speech-to-text (transcribe spoken Indian language audio to text), text-to-speech (convert text to natural-sounding speech in Indian languages and accents), translation (translate between Indian languages and English), and Saaras — their Indian-language LLM for chat and reasoning in Hindi and other Indian languages. All APIs are available via REST, with SDKs for Python and Node.js.

The context that makes Sarvam strategically important for Indian product builders: India has 1.4 billion people, approximately 125 million of whom are English-proficient. The remaining 1.2+ billion people are served by products built overwhelmingly in English, with at best Google Translate-quality language support. Sarvam's bet is that building truly excellent Indian language AI — not translated English AI — unlocks the next phase of Indian tech's growth into Bharat. For Indian fintech, healthtech, agritech, and insurance teams targeting rural and semi-urban India, Sarvam's APIs are the infrastructure that makes multilingual product experiences possible at quality levels that actually work for real users.

Supported Indian Languages

Hindi हिन्दी
Bengali বাংলা
Tamil தமிழ்
Telugu తెలుగు
Kannada ಕನ್ನಡ
Gujarati ગુજરાતી
Marathi मराठी
Malayalam മലയാളം
Odia ଓଡ଼ିଆ
Punjabi ਪੰਜਾਬੀ
English EN
Hinglish Mixed

Key APIs

Speech-to-Text (Saarika)

Transcribe spoken Indian language audio to text — works on phone call recordings, voice notes, voice input in apps. Handles code-switching (Hinglish, Tanglish) and accented speech that global models struggle with. Use case: voice-based KYC where users narrate their details in Hindi, automated transcription of customer support calls in regional languages, voice search in Indian languages for discovery apps.

Text-to-Speech (Bulbul)

Convert text to natural-sounding spoken audio in Indian languages and accents — not robotic TTS but voices that sound like real Indian speakers. Multiple voice options per language. Use cases: IVR systems for rural users, voice notifications for fintech transaction confirmations ("Aapka Rs 5,000 ka transaction successful hua"), audio summaries of policy documents for insurance or government services, accessibility features for low-literacy users.

Translation (Mayura)

Translate between Indian languages and English with domain-specific accuracy — understands financial, medical, and legal terminology in Indian language context rather than literal word-for-word translation. Use cases: translate English-language terms and conditions into Hindi for BNPL products, translate support tickets from regional languages to English for agent handling, translate product descriptions for multi-language e-commerce.

Indian Language LLM (Saaras)

A reasoning and chat model that operates natively in Indian languages — not translation of an English response but reasoning conducted in Hindi or other Indian languages. Use cases: Hindi-language customer support chatbot that understands context and nuance, agricultural advisory chatbot in regional languages for kisan apps, insurance claim guidance in the user's native language without English intermediary translation steps.

Sarvam vs OpenAI / Google for Indian Language Needs

FactorSarvam AIOpenAI (Whisper / GPT)Google (Translate / Gemini)
Indian language accuracyBest — purpose-builtGood on major languagesGood — strong index
Code-switching (Hinglish)Native supportPartialPartial
Indian accent STTTrained on Indian speechGood but genericGood
INR pricingYesUSD onlyUSD only
Data hosted in IndiaYesUS serversUS servers (by default)
English LLM qualityLimitedBestVery good
Ecosystem / toolingGrowingLargestLarge
Best forIndian language voice + text productsEnglish-first productsGoogle Workspace + search

Best For

  • Indian fintech building voice-based KYC or support for Tier 2/3 users in their native language
  • Agritech and insurance teams building advisory products for rural India in regional languages
  • Customer support IVR and chatbot teams replacing English-only flows with Hindi / regional language experiences
  • EdTech teams building audio content and assessment in Indian languages
  • Teams that need Indian data sovereignty — models and data hosted on Indian infrastructure

Pricing

Sarvam offers API access with INR pricing — a meaningful advantage over OpenAI and Google's USD-denominated APIs with 18% GST reverse charge for Indian companies.

Free Tier

Rs 0

Limited API calls per month across all Sarvam APIs — sufficient for development and prototyping. Test speech-to-text, text-to-speech, and translation without a credit card. Most Indian teams building a proof-of-concept for a Hindi-language feature start on the free tier and evaluate quality before committing to production usage.

Enterprise

Custom

Dedicated capacity, SLAs, custom model fine-tuning for your domain (medical, financial, agricultural terminology), and on-premises or private cloud deployment options. For large Indian enterprises — banks, insurance companies, government agencies — requiring data sovereignty and custom language models for specialised domains.

Pros and Cons

Pros

  • Purpose-built for Indian languages — not an afterthought
  • INR pricing — no USD conversion or GST reverse charge
  • Data hosted in India — sovereignty and compliance
  • Native Hinglish and code-switching support
  • Covers 10+ Indian languages including less-resourced ones
  • Government-backed — stable long-term infrastructure bet

Cons

  • English LLM quality trails OpenAI and Claude significantly
  • Smaller ecosystem and community than OpenAI
  • API documentation still maturing
  • Less enterprise tooling than Google or AWS AI services
  • Early-stage company — track record still being built

Getting Started with Sarvam AI

  1. Identify your highest-impact Indian language use case before integrating any API — Before calling Sarvam's API, be specific about the problem you are solving. The three highest-impact use cases for Indian product teams are: (1) voice input for users who cannot type comfortably in their language on a mobile keyboard, (2) voice output for notifications and confirmations that feel natural rather than robotic, and (3) translation of essential information — T&C, onboarding instructions, error messages — from English to the user's language. Pick one, build it to completion, measure whether it improves activation or retention for the target user segment, and then expand. Teams that try to "add multilingual support" as a broad initiative without a specific primary use case produce fragmented half-implementations that do not move metrics.
  2. Test all target languages with real users before production, not just linguists — Sarvam's models are strong but language quality varies by domain and dialect. When you have a working prototype of a Hindi or Tamil feature using Sarvam's APIs, test it with actual Tier 2 users — not just your Bengaluru or Mumbai colleagues who speak Hindi as a second language. The vocabulary, phrasing, and accent patterns of a Hindi-speaking user from UP or Rajasthan differ significantly from a Delhi-educated professional's Hindi. Run 5-10 sessions with real target users before production. This is not a Sarvam-specific limitation — it is true of any Indian language AI — but it is especially important for voice features where unnatural output causes immediate trust erosion.
  3. Use Sarvam for voice and Indian language accuracy, OpenAI for English reasoning — The highest-quality Indian language AI product architecture for most Indian startups is not "use only Sarvam" or "use only OpenAI." It is: use Sarvam's STT to transcribe Indian language voice input, use OpenAI or Claude for reasoning and response generation (in English or with translation), and use Sarvam's TTS to speak the response back in the user's language naturally. This hybrid approach gets the best accuracy on Indian language speech while maintaining the reasoning quality of frontier English LLMs. Sarvam's Saaras LLM handles simpler conversational tasks entirely within Indian languages — use it for straightforward FAQ and transactional flows where English LLM reasoning power is not needed.
  4. Instrument Indian language feature usage separately in your analytics — When you launch a Hindi or regional language version of a feature, track it as a distinct cohort in your analytics from day one. Measure: activation rate of language-selection (how many users choose their native language when offered), retention of users who activated the Indian language feature vs those who stayed in English, completion rates of critical flows (KYC, onboarding, transaction) in Indian language vs English. This data tells you both whether the feature is working and which languages have the highest demand — informing which additional languages to prioritise next. Without separate instrumentation, multilingual features are invisible in aggregate metrics and get deprioritised.
  5. Join Sarvam's developer community for early access and product input — Sarvam is an early-stage company actively shaping its product roadmap based on developer feedback. Indian product and engineering teams using Sarvam APIs have disproportionate influence on which languages get prioritised, which domain-specific vocabulary improvements get made, and what new APIs get built next. Join the Sarvam developer Discord or community forum, report issues and accuracy gaps with specific examples, and participate in beta programmes for new models. This community engagement is more valuable with Sarvam than with OpenAI precisely because Sarvam is smaller, more responsive, and explicitly building for Indian use cases that your feedback directly shapes.
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