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Build Indian-Language LLM Apps with Sarvam AI

A practical guide to choosing Sarvam 30B vs Sarvam 105B, handling code-mixed Indian-language input, and evaluating Sarvam LLMs for production apps.
Sarvam AISarvam 105BSarvam 30BIndic LLMIndian languagesvoice agents

11 min read

Updated todayVerified recentlyInformation score 92

Key insights

Concrete technical or product signals.

  • Sarvam is strongest when Indian-language fidelity and deployment control are product requirements.
  • Sarvam 105B is the quality lane; Sarvam 30B is the efficiency and conversational lane.
  • Code-mixed evaluation is essential because English-only evals hide the hard parts of Indian-language UX.

Use cases

Where this shines in production.

  • Building customer-support chat or voice agents for Indian users.
  • Routing Indian-language documents and queries to an Indic-optimized model.
  • Evaluating whether Sarvam should sit beside GPT, Claude, Gemini, or DeepSeek in a model router.

Limitations & trade-offs

What to watch for.

  • Do not rely only on vendor benchmark tables for procurement decisions.
  • Language coverage, latency, and cost can vary by exact workflow and deployment plan.

Build Indian-Language LLM Apps with Sarvam AI

Sarvam AI is a strong candidate when the product has to work in Indian languages, not just translate English prompts. The practical design problem is language reality: users mix English into local languages, switch between native and Latin scripts, send audio over phone lines, and expect answers grounded in local context.

1. Pick the right Sarvam model

Use Sarvam 105B when you need the highest-quality reasoning path: long-context document analysis, complex coding, multi-step planning, or agentic tool use. Sarvam documents it as a 105B+ MoE model with a 128K context window, OpenAI-compatible chat completions, Apache 2.0 open weights, and strong reported results on Math500, AIME, BrowseComp, and Tau2.

Use Sarvam 30B when throughput, latency, or cost matters more. Sarvam documents it as a 30B MoE model with 2.4B active parameters per token, a 64K context window, and strong performance for real-time chat and voice-agent pipelines.

2. Design for code-mixing from day one

Do not force users to pick a single language or script if the workflow is naturally code-mixed. Store the raw user text, detected language metadata, and normalized version separately. For voice workflows, Sarvam's Indian-language docs distinguish native-script transcription, English translation, romanized transliteration, and natural code-mixed output.

3. Evaluate Indian-language quality separately from English quality

Create a golden set for the actual languages and scripts you support. Include native script, romanized prompts, mixed English-plus-local-language prompts, short commands, long documents, and domain-specific tasks. Track correctness, fluency, script choice, verbosity, citation quality, refusal behavior, and tool-call accuracy.

4. Watch reasoning-token budget

Sarvam docs note that reasoning is enabled by default. Keep enough max_tokens for the visible answer, especially when using Sarvam 105B for hard tasks. For simple routing, classification, or short support replies, evaluate whether a lower reasoning setting or Sarvam 30B is enough.

5. Compare cost in INR and workflow terms

Sarvam's pricing page lists per-token chat pricing for Sarvam 105B and Sarvam 30B, plus separate pricing for speech, translation, transliteration, and vision. If your product combines LLM, speech-to-text, text-to-speech, and translation, estimate the full workflow cost rather than comparing only chat tokens.

6. Decide where Sarvam fits against global frontier models

Use Sarvam when Indian-language fidelity, data residency, local deployment options, and India-specific workflows are decisive. Use a global frontier model when your workload is mainly English, multimodal breadth is more important, or your evals show better quality on a specialized global benchmark. The safest production pattern is routing: Sarvam for Indian-language and local-context lanes, another model where your evals prove it wins.

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