Hosting Your Own Open-Source LLM for Audit: The India Cost / ROI Math (Verified 2026 Pricing)
This post is the math people keep avoiding. If you've followed our AI-in-audit series — Multi-agent, RAG, Hallucinations, SA 530 sampling — you'll have noticed a recurring theme: "for DPDPA reasons, you can't paste client data into ChatGPT/Claude." The honest follow-up question is: so what's the alternative? How much does it actually cost to host your own LLM in India?
We pulled together verified May-June 2026 pricing across Indian sovereign clouds (E2E Networks, Yotta Shakti, Cyfuture AI, JarvisLabs, NxtGen, Tata Communications), hyperscalers (AWS Mumbai, Azure South India), and on-premises options (DGX H100 capex). Plus realistic open-source model hardware requirements (Llama 3.3 70B, Mixtral 8x22B, DeepSeek R1/V3, Qwen 2.5 72B). Plus the labour cost of running it.
The result is sobering. For a typical 10-person Indian CA firm, self-hosting an LLM is 9-11× more expensive than ChatGPT Business. Self-hosting wins on DPDPA / ICAI compliance and data sovereignty — NOT on cost.
Here's the math, line by line.
What does it actually cost to rent a GPU in India?
Indian sovereign / domestic clouds (per GPU-hour, on-demand, May 2026)
| Provider | H100 80GB | A100 80GB | A100 40GB | L40S | L4 |
|---|---|---|---|---|---|
| E2E Networks | ~₹241 | ~₹174 | ~₹164 | ~₹100 | ~₹47 |
| Yotta Shakti Cloud | ₹356 (1× VM) / ₹351 (8× bare metal) | n/a public | n/a | ₹137 (1×) | n/a |
| Cyfuture AI | ₹329 on-demand / ₹219 reserved | ₹198 / ₹187 reserved | ₹170 reserved | ₹124 / ₹61 reserved | n/a |
| JarvisLabs | ₹218 (per-minute billing) | n/a public | n/a | n/a | n/a |
| NxtGen SpeedCloud AI | Quoted plans start ₹14,999/mo | offered | offered | offered | offered |
| IndiaAI subsidised pool | ₹92/hr (gov subsidy — CA firms NOT eligible) | included | included | n/a | n/a |
Hyperscalers (per GPU-hour)
- AWS (p5.48xlarge in US baseline): ~₹571 per GPU/hr. Mumbai (ap-south-1) typically priced at parity to 15% premium over US-East — so ~₹571-660/hr.
- Azure ND H100 v5 (Southeast Asia / closest to India): ~₹1,020/hr PAYG. ~₹665/hr at 1-year reserved.
- GCP a3-highgpu H100: ~₹918/hr.
Hyperscalers are 2.5-4× more expensive than Indian sovereign clouds for the same GPU. For Indian CA firm use cases (where data must stay in India anyway), sovereign clouds are the right tier.
On-premises (purchase)
If you're considering buying GPU hardware outright:
- H100 80GB PCIe (single card): ₹25-40 lakh
- NVIDIA DGX H100 (8× H100, 640 GB VRAM): ₹3.5-5 crore
- Annual support: ₹8-41 lakh
- Power infrastructure upgrade: ₹50 lakh+ (700W per GPU)
- Cooling: ₹15 lakh - 1 crore
- Total turnkey 8× H100 cluster: ₹5-8 crore
This is what enterprise Big-4 / large-cap firms buy. For typical Indian CA firms, on-premises is overkill — rented GPU on Indian sovereign cloud is the right path.
What can the open-source models actually do?
The leading open-source LLMs you'd actually run for Indian audit work, with their hardware requirements:
| Model | Quality vs GPT-4o | FP16 VRAM | 4-bit VRAM | Minimum production GPU |
|---|---|---|---|---|
| Llama 3.3 70B | ~85% of GPT-4o | ~140 GB | ~46 GB | 2× A100 80GB (FP16) OR 1× A100 80GB / 2× L40S (4-bit) |
| Mixtral 8x22B (141B total) | ~80% of GPT-4o | ~263 GB | ~66 GB | 4× A100 80GB FP16 OR 2× A100 80GB 4-bit |
| DeepSeek R1/V3 671B | Approaching GPT-4o on reasoning | ~700 GB FP8 / 1.4 TB BF16 | ~376 GB (Q4) | 8× H200 (single node) |
| Qwen 2.5 72B | ~80-85% of GPT-4o | ~144 GB | ~48 GB | 2× A100 80GB OR 2× L40S 48GB |
| Llama 3.2 Vision 11B | Multimodal, smaller | ~22 GB | ~8 GB | 1× L40S 48GB |
For most Indian CA firm use cases, Llama 3.3 70B at 4-bit quantisation on a single A100 80GB is the practical sweet spot. ~85% of GPT-4o quality at a fraction of the cost.
DeepSeek R1/V3 671B is the impressive one — reasoning quality approaching GPT-4o — but the 8× H200 requirement means infrastructure cost ~₹1.6 crore-equivalent in monthly capacity. Not feasible for a 10-person firm.
The 10-person CA firm cost calculation
Let's run the actual numbers for a small/mid-tier Indian CA firm wanting to self-host Llama 3.3 70B for audit work.
Assumptions:
- 10 users (partners + managers + staff using the LLM)
- 1,000 prompts per user per month = 10,000 prompts total
- Average 2K input tokens + 1K output tokens per prompt
- Total monthly volume: 30M input + 10M output = 40M tokens
Throughput requirement:
- Llama 3.3 70B on 1× H100 (4-bit) at ~1,500 output tokens/sec sustained
- 10M output tokens = ~1.85 hours of actual GPU compute time
- But you can't burst-rent at this granularity. You need 24×7 availability for interactive use.
- Reserved GPU cost: 730 hr/month × per-hour rate
Self-hosted on rented GPU (24×7 dedicated H100)
| Provider | Monthly cost |
|---|---|
| Cyfuture reserved 12-mo | ₹1.6 lakh |
| JarvisLabs on-demand | ₹1.59 lakh |
| E2E Networks (monthly committed) | ₹1.51 lakh |
| Yotta Shakti dedicated VM | ₹1.92 lakh |
| AWS Mumbai p5 (estimate) | ~₹4.17 lakh |
| Azure ND H100 v5 PAYG | ~₹7.45 lakh |
Realistic minimum for Indian sovereign cloud: ₹1.5-1.8 lakh / month for one dedicated H100.
Plus operating overhead
- MLOps engineer (part-time) to maintain the deployment: ₹50K-1 lakh / month
- Vector DB (Qdrant self-hosted): ₹3K-5K / month
- Monitoring + logging infrastructure: ₹10K / month
- Backup + disaster recovery: ₹5K / month
Total all-in self-hosted cost: ₹2-3 lakh / month for a 10-person firm.
Equivalent API or SaaS spend
For the same 40M-token workload:
| Option | Monthly cost |
|---|---|
| ChatGPT Business (10 seats) | ~₹17K / month |
| Claude Pro (10 seats) | ~₹17K / month |
| GPT-4o API at 40M tokens | ~₹14.5K / month |
| Claude Sonnet 4.6 API | ~₹20K / month |
| Llama 70B via Together AI API | ~₹550 / month |
The math is stark: self-hosting in India costs ₹2-3 lakh/month. ChatGPT Business costs ~₹17K/month. Llama 70B via API costs ~₹550/month.
Self-hosting is 9-11× more expensive than SaaS and 275× more expensive than API-based open-source models.
When does self-hosting actually make sense?
The math is clear: self-hosting loses on pure cost. So when does it win?
1. DPDPA / ICAI compliance for confidential workflows
The strongest case. When client data MUST stay in India for DPDPA Section 8 + ICAI confidentiality reasons, self-hosting in India is the architecturally correct answer. No public LLM offers contractual India-only data residency in the consumer tier.
For a CA firm processing PAN / Aadhaar / bank statements / payroll data, the choice is:
- Public LLM (ChatGPT / Claude) — cheap, but data crosses to US infrastructure. DPDPA breach risk.
- API-based open-source (Together / Fireworks) — cheap, US-hosted. Same DPDPA risk.
- Self-hosted in India — expensive, but DPDPA-aligned.
- Vendor-provided India-hosted audit AI (CORAA, others) — moderate cost, contractually committed India hosting, audit trail by default.
The fourth option is what most firms should choose — gets DPDPA compliance without the build cost.
2. Volume above ~11 billion tokens / month
Published industry analysis pegs the API-vs-self-host break-even at roughly 11 billion tokens / month. A 10-person CA firm at 40M tokens is 275× below this break-even.
To hit 11B tokens, you'd need ~2,500 users or extremely heavy per-user usage. Effectively only Big-4 / consulting firms at scale.
3. Strategic fine-tuning on proprietary data
If your firm has a unique audit corpus (e.g., 10,000 anonymised historical engagements across 20 years) that you can fine-tune a model on for genuine competitive advantage, self-hosting that fine-tuned model is the only way to use it without exposing the IP to a third party.
This is feasible for the ICAI itself, or for a Big-4 India entity with a substantial proprietary data asset. Not feasible for a 10-person firm.
4. Government / sensitive client work
For audits of Defence sector, sensitive PSUs, or government-classified entities — Indian government policy may explicitly require no cross-border data transfer. Self-hosting becomes mandatory.
For everything else (most CA firm work), self-hosting is over-engineering.
The practical hybrid stack for a 10-person CA firm
The recommendation that emerges from the math:
Tier 1 — General productivity (non-confidential)
- Claude Pro + ChatGPT Plus for partners (₹3K-4K / partner / month) — drafting, research, code, brainstorming
- ChatGPT free / Claude free for staff — basic tasks
- Public LLMs, with the 7-rule framework — never paste client data
Tier 2 — Confidential audit workflows
- Vendor-provided India-hosted audit AI (CORAA, others) for ledger analysis, JE testing, vouching, GST reconciliation, Form 3CD pre-fill, working papers
- Contractually committed India hosting, no customer-data training, audit trail
- Cost ~₹2-4 lakh / year for unlimited users
Tier 3 — Long-tail bulk processing
- Open-source LLM via API (Together AI, Fireworks) for non-confidential batch processing — research summaries, narrative generation at scale
- Cost ~₹500-2,000 / month at moderate volume
Total realistic 10-person firm spend
- Tier 1: ₹30K-50K / month
- Tier 2: ₹17K-35K / month (CORAA-style vendor)
- Tier 3: ₹2K-5K / month
Combined: ₹50K-90K / month, all-in, with full DPDPA-compliant handling of confidential workflows.
This is dramatically cheaper than self-hosting (which would be ₹2-3 lakh / month for just the Tier 2 capability with worse general productivity).
What about IndiaAI subsidised compute?
The Government of India's IndiaAI Mission offers GPU compute at ~₹92/hr (~75% discount to commercial rates) — making H100 effectively ~₹67K/month rather than ~₹1.6 lakh.
But CA firms generally don't qualify. The IndiaAI subsidised pool is targeted at:
- AI startups (≤5 years old, Indian-incorporated)
- AI research institutions
- AI-focused MSMEs
- Academic researchers
A CA firm using AI for internal audit purposes doesn't fit the eligible categories. Watch the programme as it evolves — eligibility may broaden over time, particularly with the AI-in-audit push from ICAI.
If your firm has spun off an AI subsidiary (a separate legal entity focused on AI products), that subsidiary might qualify. But the CA firm itself, doing audit work, is outside the current scope.
What about RAG over self-hosted LLM?
Combining self-hosted LLM with RAG (covered in detail in the RAG post) adds modest extra cost:
- Qdrant self-hosted: ₹3K-5K / month additional
- Embedding model: co-locate on the inference GPU at near-zero marginal cost
- One-time corpus embedding: ~₹100-1,000 for embedding all SAs + CARO + Companies Act + your firm methodology
The RAG stack adds ~5% to the self-hosted infrastructure cost while dramatically improving citation accuracy. If you're self-hosting, you must add RAG — without it, the open-source LLMs hallucinate at higher rates than commercial models.
On-premises (DGX H100): when does it make sense?
Buying outright vs renting:
| Approach | 3-year cost (10-person firm, 1× H100 equivalent) |
|---|---|
| DGX H100 on-prem | Capex ₹4.5 cr + opex ₹84 lakh = ~₹5.3 crore |
| Rented H100 (Indian sovereign cloud) | ₹19.2 lakh × 3 + MLOps ₹18 lakh + infra ₹2 lakh = ~₹75 lakh |
| ChatGPT Business 10 seats | $200/mo × 36 = $7,200 = ~₹6 lakh |
DGX H100 on-prem is 7× more expensive than rented for the same capacity. Only makes sense for:
- Multi-year strategic AI investment (Big-4 India scale)
- Sustained 24×7 utilisation at near-capacity
- Government / regulatory mandate to keep hardware in-house
- Scale beyond ~100 users where amortisation works
For typical Indian CA firms, on-premises is over-investment. Rented GPU on Indian sovereign cloud is the right model if self-hosting at all.
Honest recommendations by firm size
Solo CA / 1-2 partner firm
- ChatGPT Plus or Claude Pro for the partner (₹1.7K-2K / month)
- Free tier for staff
- A vendor-provided audit-AI subscription if doing 10+ engagements / year (₹30K-60K / year)
- Don't self-host. Cost / benefit doesn't make sense.
5-20 partner mid-tier firm
- Claude Pro + ChatGPT Plus for partners (₹30K-50K / month)
- Vendor-provided India-hosted audit AI (CORAA / equivalent) for client-data work (₹2-4 lakh / year)
- Open-source API for narrow batch tasks (₹2-5K / month)
- Don't self-host. The combined hybrid stack covers the full need at 10-20% of self-hosted cost.
20-50 partner large firm
- Same hybrid stack as mid-tier, larger team subscriptions
- Consider self-hosting a small enclave (1× A100 80GB) for the most-confidential workflows — ₹1-1.5 lakh / month
- Self-hosting only for the regulated workloads, public LLMs for everything else
- Selective self-hosting for the 5-10% of work that requires it.
50+ partner / Big-4 India
- Full hybrid: subscribed SaaS + vendor audit-AI + selective self-hosting + multi-cloud architecture
- Engineering team to operate the stack
- This is the only tier where building substantial in-house AI infrastructure makes sense
The CORAA argument (without being defensive about it)
Why does a vendor-provided India-hosted audit AI (like CORAA) cost ₹2-4 lakh / year while self-hosting costs ₹24-36 lakh / year for the same DPDPA-compliant capability?
Because the vendor amortises the infrastructure cost across all customers:
- The vendor runs the LLM infrastructure for hundreds of firms — fixed cost spread across many users
- The vendor employs the MLOps team — fixed cost spread across many users
- The vendor pre-builds the audit-specific tooling (Form 3CD pre-fill, CARO clause-wise observations, Section 188 calculations) — fixed engineering investment spread across many users
- The vendor maintains the model upgrade path — ongoing engineering effort amortised
For an individual CA firm to replicate this infrastructure internally would require duplicating all of the above at full cost — which is why self-hosting math doesn't work for typical firms.
The vendor model isn't unique to CORAA — every audit-AI vendor (CaseWare, AssureAI, EzAudit, CORAA, others) has the same economics. The choice for a CA firm is "which vendor" — not "vendor vs build."
For the framework to evaluate vendors, see the AI Audit Tool Evaluation Checklist — 46 criteria across India compliance, data security, audit-grade features, integrations, pricing, vendor quality.
Bottom line
The verified Indian numbers (May-June 2026):
- GPU rental on Indian sovereign cloud: ₹1.5-1.8 lakh / month for dedicated H100 (24×7)
- Llama 3.3 70B requirement: 1× A100 80GB at 4-bit, or 2× A100 80GB at FP16
- Self-hosted all-in for 10-person firm: ₹2-3 lakh / month
- ChatGPT Business equivalent capability: ~₹17K / month (10 seats)
- Vendor-provided India-hosted audit AI (CORAA-style): ₹2-4 lakh / year for unlimited users
Self-hosting is 9-11× more expensive than SaaS. The only justifications: DPDPA-mandated workflows, sustained ultra-high volume (>11B tokens / month — effectively only Big-4 scale), strategic proprietary fine-tuning, or government-classified work.
For the typical Indian mid-tier CA firm, the right architecture is hybrid:
- Public LLMs (ChatGPT, Claude) for non-confidential work — ~₹30-50K / month
- Vendor-provided India-hosted audit AI for client-data work — ~₹2-4 lakh / year
- Open-source LLM via API for batch processing — ~₹2-5K / month
Combined cost ~₹5-10 lakh / year. Self-hosting alone would cost ₹24-36 lakh / year for the same DPDPA-compliant capability with worse general productivity.
Next in this series: SA 530 Audit Sampling with AI — what changes when AI lets you test 100% of journal entries.
Try CORAA → Vendor-provided India-hosted audit AI. Per-entity flat pricing, unlimited users. India-hosted, contractually committed. DPDPA-aligned by design. See pricing · AI Audit Tool Evaluation Checklist · Trust Centre.