10 Best LLM Hosting Providers in India (2026)

A team that wants to serve a 70-billion-parameter model to Indian users has to answer three questions before it signs anything. How much GPU memory does the model need once the cache for concurrent requests is counted? Where will prompts and documents be processed? Who responds when the server stops answering at 2 a.m.? At 16-bit precision (FP16), the weights alone occupy about 140 GB, so the first answer decides whether one GPU, several GPUs or a quantized model is enough, and that decision sets most of the monthly bill.

This guide compares LLM hosting providers in India for CTOs, ML engineers and founders facing that choice. It covers ten providers: Purvaco, which publishes this article, six Indian GPU and cloud companies, and three global hyperscalers. Prices are dated 15 October 2026. Where a provider does not publish a fact, the article says so.

Disclosure: Purvaco publishes this comparison and holds position #1. The ranking is editorial research based on public documentation. It is not independent testing, and no provider was benchmarked or deployed for this article.

Quick comparison of the 10 LLM hosting providers

Pricing units differ (hourly GPU rates, monthly plans, quote only), so the last column is not a like-for-like ranking. Storage, data transfer and tax are extra unless stated.

Provider	Hosting model	GPU or AI infrastructure	India availability	Best suited for	Pricing
1. Purvaco	Cloud GPU servers, managed or self-managed	NVIDIA and AMD GPUs; A100, V100, RTX 6000/8000 listed	Indian data centers; city not stated	Managed, India-hosted GPU servers	Quote only
2. E2E Networks	GPU VMs, Kubernetes, TIR platform	B200 to L4	Delhi NCR, Chennai	INR-billed H100 and H200	H100 80 GB $3.99 per GPU-hour
3. Yotta	VMs, bare metal, clusters, serverless	H100 SXM, L40S	Mumbai, Greater Noida, GIFT City	Sovereign, large-scale AI	Not public
4. Tata Communications	Bare-metal GPU as a service	H100, H200, L40S, L4	India only	Private connectivity	INR rate card
5. AceCloud	GPU VMs, Kubernetes	L40S to H200	Noida	One to a few GPUs, INR monthly	H100 HGX ₹1,80,000 per month
6. Jarvislabs	Per-minute VMs, serverless	H200 to L4	India regions	Experiments, variable use	H100 SXM $3.49 per GPU-hour
7. Cantech	Cloud and dedicated GPU servers	H200 (1x to 8x), L40S, L4	Mumbai	Quoted H200 servers	L4 from ₹40 per hour
8. AWS	EC2 P5, Capacity Blocks	H100	Mumbai	Teams on AWS	Per instance-hour
9. Google Cloud	Compute Engine GPU VMs	A3 (H100, H200), G2 (L4)	Mumbai, Delhi	Teams on Google Cloud	Per hour
10. Microsoft Azure	Azure VMs	NCads H100 v5	[Confirm Central India availability]	Teams on Azure	Per hour

How we evaluated the best LLM hosting providers

Each provider was assessed on eight criteria: suitability for production LLM workloads, GPU availability and VRAM options, deployment flexibility, India availability and data residency, cost visibility and billing flexibility, security and data control, support and operational responsibility, and fit for the intended workload.

“GPU availability” means a provider lists a card. “Usable VRAM” is the memory a model can occupy after the runtime takes its share. “Deployment control” is whether you get root access and can install your own inference software.

Evidence comes from official provider pages retrieved in October 2026, plus a few labelled secondary sources. Prices and GPU stock change often, so confirm before ordering. The order reflects these criteria and the stated audience, not measured performance. Purvaco appears first as publisher. On public pricing and GPU range, E2E Networks, AceCloud and Jarvislabs publish more detail, and the Purvaco section says so.

1. Purvaco

Overview

Purvaco Technology Pvt. Ltd. sells dedicated servers, bare metal servers, cloud hosting, VPS hosting and a GPU Cloud Server product. Its homepage describes 24×7 support, managed services and cloud migration, and states that its data centers are Tier IV and Tier III facilities across India. The certifications page lists ISO 9001:2015, ISO/IEC 27017:2015, ISO/IEC 20000-1:2018, ISO/IEC 27701:2019, ISO 22301:2019 and ISO 27001:2022.

Why consider Purvaco for LLM hosting?

The GPU Cloud Server and Cloud GPU Server pages state:

• Dedicated GPU allocation rather than shared GPUs.
• NVIDIA and AMD GPUs, including A100, V100 and RTX 6000/8000 series cards.
• NVMe SSD storage, memory from 32 GB to 1 TB or more, and bandwidth of up to 40 Gbps.
• Linux and Windows Server, KVM virtualization, Docker and Kubernetes support, and CUDA, PyTorch and TensorFlow compatibility.
• Managed or self-managed operation with 24×7 technical support.

Dedicated allocation matters because a shared GPU gives variable token throughput, and a managed option helps teams without GPU operations staff. These are Purvaco’s own claims, and no independent test was run.

Not confirmed in public material, and therefore not claimed: H100 or H200 availability, VRAM per configuration, multi-GPU server sizes, GPU-aware Kubernetes scheduling, a managed inference endpoint and a fine-tuning service. [Confirm available GPU models, VRAM and multi-GPU options with Purvaco.]

LLM workloads to evaluate

• Internal open-weight LLM. A good fit if an 80 GB A100 is available. A 7B to 13B model at FP16, or a 34B model quantized to 4-bit, fits on 40 to 80 GB. Larger models need multi-GPU servers, which need confirmation.
• Chatbot or RAG application. The GPU server can run the model while the vector database and application tier run on Purvaco cloud or VPS products. Confirm private networking between them.
• SaaS inference. Test concurrency and time to first token on the exact configuration, and ask how capacity is added.
• Development and evaluation. Self-managed access with Docker and common frameworks is documented.

Infrastructure and deployment considerations

Documented: dedicated NVIDIA GPUs, NVMe storage, up to 40 Gbps networking, Linux and Windows images, container and Kubernetes support, backup options, monitoring and dashboard or API deployment.

Recommended for LLM work and needing confirmation: 80 GB of VRAM per GPU for 34B to 70B quantized models, 128 GB or more of system RAM, 1 TB or more of NVMe for weights and checkpoints, root access for vLLM or TGI, and private networking to the application tier. [Confirm GPU model and VRAM per configuration.] [Confirm which city hosts GPU servers.]

“RTX 6000/8000” needs care: the Turing RTX 6000 has 24 GB, the RTX 8000 has 48 GB, and the RTX 6000 Ada has 48 GB. Ask which card is installed.

Pricing and cost planning

Purvaco does not publish GPU prices. Its pages state that quotes depend on GPU type, usage hours and configuration. For an accurate quote, share the model and parameter count, quantization plan, concurrent users, tokens per second, context length, availability needs, monthly utilization, and storage, backup and transfer needs.

Limitations and questions to ask before purchasing

Documented gaps: no public pricing, no VRAM per configuration, no GPU newer than the A100 generation listed, no data center city, and no evidence of a managed inference service. The GPU page states a 99.99% uptime SLA while the homepage states 99.9%, and no SLA document was found in the pages reviewed. Ask:

1. Which exact GPU and VRAM will I receive, and what is the lead time?
2. Which uptime figure is in the contract, and what remedy applies?
3. In which city are GPU servers hosted, and where do backups and logs go?
4. Can I install my own inference software with root access?
5. What are the charges for storage, bandwidth and managed support?
6. How is capacity added when traffic grows?

Who should consider Purvaco?

Businesses that want India-hosted, dedicated GPU servers with an optional managed service: internal assistants on small or mid-sized open-weight models, RAG applications, development environments, and teams without in-house GPU operations experience. Teams that need H100 or H200 cards, public price lists or serverless endpoints should compare the options below first.

To discuss your model, traffic and budget, contact Purvaco.

2. E2E Networks

Overview: E2E Networks is an NSE-listed Indian cloud company focused on GPU compute. Its pricing page shows a MeitY empanelment badge and a broad NVIDIA range.

Hosting options: GPU instances with 1, 2, 4 or 8 GPUs, managed Kubernetes, object and block storage, and the TIR platform for training, fine-tuning and inference endpoints.

India availability: The provider lists Delhi NCR and Chennai.

Pricing: On 15 October 2026, per GPU-hour excluding tax: B200 180 GB $7.99, H200 141 GB $5.29, H100 80 GB $3.99, A100 80 GB $2.10, L40S 48 GB $1.20, L4 24 GB $0.57. Committed plans are quoted by sales. Data transfer is $0.045 per GB. An India page with INR prices exists.

Advantages: The widest public GPU price list among the Indian providers reviewed, a managed inference layer, and H200 and B200 options.

Limitations: Committed pricing requires a sales conversation, and capacity for the largest GPUs fluctuates with demand.

Best suited for: Teams that want INR billing and transparent hourly rates for H100-class GPUs.

Technical assessment: A 141 GB H200 lets a 70B model quantized to 8-bit (about 70 GB of weights) run on one GPU with room for cache, avoiding multi-GPU communication.

3. Yotta (Shakti Cloud)

Overview: Yotta operates data centers and the Shakti Cloud AI platform. A secondary report in Outlook Business says Yotta will provide 9,216 GPUs under the India AI Mission.

Hosting options: According to Yotta’s site, Shakti Cloud includes VMs on H100 SXM and L40S, single-tenant bare metal, clusters, serverless GPUs, AI endpoints on NVIDIA NIM and a fine-tuning service.

India availability: Yotta lists NM1 in Mumbai, D1 in Greater Noida and G1 in GIFT City. Confirm which facility hosts a given service.

Pricing: Not publicly confirmed. A startup program advertises up to USD 50K in GPU credits.

Advantages: A full stack from bare metal to managed endpoints, large GPU capacity and sovereign-cloud positioning.

Limitations: No public price list.

Best suited for: Enterprises, government-linked programs and funded AI startups.

Technical assessment: Bare metal gives strong isolation and full control of the inference stack, but the buyer handles drivers, patching and monitoring unless a managed service is added.

4. Tata Communications (Vayu AI Cloud)

Overview: Tata Communications offers GPU as a service on Vayu AI Cloud. Its GPU page states that workloads stay within India, aligned with the DPDP Act, and that its AI management system is certified to ISO/IEC 42001:2023.

Hosting options: Dedicated bare-metal GPUs (H100, H200 and L40S named) with InfiniBand networking, pre-installed AI frameworks and multi-cloud connectivity.

India availability: Vayu services are available in India only, per its press release.

Pricing: The rate card PDF lists INR hourly rates by configuration and term. Single-L40S rates range from ₹138 to ₹233 per hour across the columns I could read. [Confirm which column is on-demand.]

Advantages: India-only scope, a published INR rate card and private network integration.

Limitations: Fewer self-serve conveniences than developer-focused clouds, and the rate card needs careful reading.

Best suited for: Enterprises that already use Tata Communications networking.

Technical assessment: For RAG over confidential documents, private connectivity between the document store and the GPU limits traffic on the public internet.

5. AceCloud

Overview: AceCloud sells GPU cloud, Kubernetes, storage and managed services. Its GPU page lists INR pricing for a Noida data center.

Hosting options: GPU VMs with 1, 2, 4 or 8 GPUs and managed Kubernetes. The provider states that each GPU is attached by passthrough and not shared.

India availability: Pricing is shown for Noida. The provider also describes partners in the USA, so confirm the region at order time.

Pricing: Monthly starting plans on 15 October 2026: L40S 48 GB ₹83,000; A100 80 GB ₹90,000; RTX PRO 6000 96 GB ₹1,21,599; H100 HGX 80 GB ₹1,80,000; H200 NVL 141 GB ₹2,22,775. An L40S is also listed from ₹142.12 per hour. Monthly and hourly bases should not be compared line by line.

Advantages: Public monthly INR prices with specifications, dedicated passthrough, and ISO 27001, 27017 and 27018 certifications stated on the page.

Limitations: Larger H100 clusters need a quote, and committed discounts need a sales conversation.

Best suited for: Teams that want predictable monthly INR billing for one to a few GPUs.

Technical assessment: At about 730 hours a month, ₹1,80,000 for an H100 works out near ₹247 per hour, which is worth comparing with hourly clouds only at that utilization.

6. Jarvislabs

Overview: Jarvislabs is an India-founded GPU cloud with per-minute billing. Its pricing page lists H200, H100, RTX Pro 6000, A100 and L4 GPUs.

Hosting options: On-demand VMs with root access, serverless workers, managed endpoints and hosted model APIs. H200 InfiniBand clusters go through sales.

India availability: The page says India regions are available and that CPU VMs run in Noida. A blog post states H100 SXM is available in India at ₹217.89 per hour. [Confirm which GPU models are in India regions.]

Pricing: On 15 October 2026, per GPU-hour: H200 SXM $4.59, H100 SXM $3.49, RTX Pro 6000 $2.19, A100 80 GB $1.49, L4 $0.44. Storage is $0.10 per GB-month.

Advantages: Per-minute billing with pause and resume, free in-region egress and a spot tier.

Limitations: Customers outside India are billed in USD per the page, and large reserved clusters need sales.

Best suited for: Model evaluation, short fine-tuning runs and irregular usage.

Technical assessment: Per-minute billing suits experiments, but a 24/7 production service pays for every minute, so run the monthly arithmetic first.

7. Cantech

Overview: Cantech is an Indian hosting company with GPU cloud and dedicated GPU server lines. Its cloud GPU page lists configurations and data center partners.

Hosting options: H200 servers in 1x, 2x, 4x and 8x configurations (the 1x has 141 GB GPU memory, 30 vCPU, 375 GB RAM and 3,000 GB storage), plus L40S, RTX Pro 6000 and L4 plans. A B200 is listed as coming soon.

India availability: The page shows Yotta NM1 and L&T NMP-1, both in Mumbai.

Pricing: L4 is listed from ₹40 per hour. Other plans route to chat or contact. The page states 99.97% uptime.

Advantages: A published H200 ladder up to 8 GPUs and Mumbai-hosted infrastructure.

Limitations: Most prices require contact, and the uptime figure is a marketing statement until it appears in a contract.

Best suited for: Buyers who want multi-GPU H200 servers and accept quote-based pricing.

Technical assessment: An 8x H200 server has 1,128 GB of GPU memory, but interconnect and runtime overhead must be checked with the provider.

8. AWS

Overview: AWS offers H100 GPUs through EC2 P5 instances. An AWS announcement says single-GPU P5.4xlarge instances are available through Capacity Blocks in Asia Pacific (Mumbai).

Hosting options: EC2 GPU instances, Capacity Blocks for scheduled capacity, and managed services such as SageMaker.

India availability: Mumbai is the relevant region. H100 availability in Hyderabad was not confirmed.

Pricing: Per instance-hour, varying by purchase model. Storage, load balancing and data transfer out are billed separately. Use the AWS pricing calculator.

Advantages: Single-GPU H100 sizing, mature security tooling and integration with existing AWS data and identity.

Limitations: Capacity Blocks require planning, and quotas may need increases.

Best suited for: Teams whose application and data already run on AWS in Mumbai.

Technical assessment: Keeping the model in the same region as the application avoids cross-region transfer fees and extra latency.

9. Google Cloud

Overview: Google Cloud offers Compute Engine GPU VMs. Its GPU regions documentation lists India locations.

Hosting options: A3 families on H100 and H200, G2 machines with L4, plus GKE and Vertex AI.

India availability: The documentation lists Mumbai zones with A3 Edge, G2 and T4, and a Delhi zone with A3 Ultra, and says some A3 types have limited capacity. I read a partly rendered table, so verify in the console.

Pricing: Per hour by machine type, plus storage and egress. Use the Google Cloud pricing calculator.

Advantages: Mumbai and Delhi regions, managed Kubernetes and a managed ML platform.

Limitations: Limited capacity for some A3 types, and quota requirements.

Best suited for: Teams already on Google Cloud.

Technical assessment: L4 machines suit quantized models up to roughly 13B parameters at modest concurrency, while larger models need A3.

10. Microsoft Azure

Overview: Azure offers H100 virtual machines. Microsoft’s documentation describes the NCads H100 v5 series with up to 2 H100 NVL GPUs of 94 GB each and up to 640 GiB of memory.

Hosting options: Azure VMs, Azure Kubernetes Service and Azure Machine Learning.

India availability: Azure has Central, South and West India regions, but the page I retrieved lists no regions. [Confirm H100 VM availability in Central India before publication.]

Pricing: Per hour by size and region. Use the Azure pricing calculator.

Advantages: 94 GB per GPU and strong integration with Microsoft identity and enterprise agreements.

Limitations: Generation 2 VMs are required, live migration is not supported per Microsoft, and regional availability needs checking.

Best suited for: Microsoft-centric enterprises.

Technical assessment: 94 GB per GPU allows a 70B model quantized to 4-bit (about 35 GB of weights) on one GPU with generous cache.

What to check before choosing an LLM hosting provider

GPU memory and model size

A model loads only if weights, attention cache and runtime overhead fit in VRAM. Weight memory is roughly parameter count times bytes per parameter: about 2 bytes at FP16 and 0.5 bytes at 4-bit. That gives about 14, 26, 68 and 140 GB for 7B, 13B, 34B and 70B at FP16, and about 3.5, 6.5, 17 and 35 GB at 4-bit.

The cache grows with context length and concurrency. Illustrative assumption: a 70B model with 80 layers, 8 key-value heads and a head dimension of 128 uses about 327,680 bytes of cache per token at FP16. At 20 users with 8,000 tokens each, that is 160,000 tokens, or about 52 GB, on top of the weights. Test your exact configuration.

Inference versus fine-tuning

Inference needs weights and cache. Full fine-tuning also stores gradients and optimizer states, which can multiply memory needs several times. Methods such as LoRA reduce this. A server that serves a model well may not fine-tune it.

Dedicated GPU versus shared GPU infrastructure

A dedicated GPU gives consistent throughput and isolation. A shared or time-sliced GPU costs less but throughput varies with neighbours. Dedicated is the safer default for production inference.

India data residency

A sales office or billing address does not establish where data is processed. Verify the deployment region, and where backups, logs, monitoring and managed-service components run.

Networking and latency

Time to first token depends on routing, prompt size and concurrency. Place the GPU in the same region as the application and measure from your users’ networks.

Security and access control

Check SSH key access, identity management, encryption in transit and at rest, firewall rules, patching responsibility, audit logging and tenant isolation.

Total cost of ownership

Add compute, storage, transfer, orchestration, monitoring, backups, support and engineering time. Example, assuming an H100 at E2E’s $3.99 per hour and 730 hours a month: full utilization costs about $2,913; 200 hours costs about $798. Run the same arithmetic with your own quote.

Real-world deployment scenarios

These scenarios are illustrative. They are not customer case studies.

Scenario A: An Indian SaaS company running an AI assistant

Assume a 13B to 34B open-weight model and 20 to 50 concurrent sessions at peak. Work out the cache per session, then test throughput under load. Compare an on-demand cloud against a monthly dedicated plan using expected hours, and ask how capacity is added during a launch.

Scenario B: A company processing confidential internal documents

The workload is RAG over contracts and HR files. Restrict access with network isolation and identity controls, set retention for prompts and logs, and verify that vector databases, backups and monitoring stay in the chosen region. Self-hosting does not guarantee compliance by itself, so have legal counsel review DPDP Act obligations.

Scenario C: An AI startup testing an open-weight model

Early experiments favor per-minute billing because usage is irregular and the best model is not yet known. Once the model and traffic are stable, move to a dedicated or monthly plan, and budget the effort of rebuilding images, storage and monitoring at a new provider.

Which LLM hosting provider should you choose?

• Dedicated infrastructure: Purvaco (confirm GPU), Tata Communications, Yotta bare metal, AceCloud.
• Managed cloud services: AWS, Google Cloud, Azure; E2E Networks and Yotta for Indian managed AI platforms.
• Strict data residency: Tata Communications, Yotta, E2E Networks, Cantech; verify backups and logs.
• Irregular GPU usage: Jarvislabs and E2E Networks.
• Sustained inference traffic: AceCloud monthly plans, Cantech H200 servers, Purvaco after confirming specifications.
• Fine-tuning: E2E Networks TIR, Yotta, or H100 and H200 clusters from Jarvislabs.

Questions to ask before buying LLM hosting

1. Which exact GPU models and VRAM are available? Names such as “RTX 6000” cover cards with different memory.
2. Where will inference and customer data be processed? Region determines legal exposure and latency.
3. Are GPUs dedicated, shared or time-sliced? This sets throughput consistency.
4. What are the charges for compute, storage and transfer? Transfer and storage can change the ranking.
5. Can the team install its preferred inference software? Root access allows vLLM, TGI or TensorRT-LLM.
6. What support is available during incidents? Ask for response times in writing.
7. What uptime commitment and remedies appear in the SLA? Marketing and contract figures can differ.
8. What happens to data, logs and backups when the service ends? Confirm deletion timelines.
9. Can the infrastructure handle your concurrency and context length? Test with real prompts.
10. How is capacity increased? Capacity queues delay launches.

Frequently asked questions

What is LLM hosting? LLM hosting means running a large language model on servers you rent so applications can send prompts and receive responses. The provider supplies GPUs, storage and networking, and you or the provider run the model software. It can be a dedicated server, a cloud GPU instance or a managed endpoint.

Which LLM hosting providers are available to businesses in India? This guide covers ten: Purvaco, E2E Networks, Yotta, Tata Communications, AceCloud, Jarvislabs, Cantech, AWS, Google Cloud and Microsoft Azure. Availability of specific GPUs varies by provider and region, so confirm the exact configuration and location with the provider before ordering.

Can I host an LLM on an Indian GPU server? Yes. E2E Networks, Yotta, Tata Communications, AceCloud and Cantech list GPU services in Indian facilities, and AWS and Google Cloud list GPU instances in Indian regions. Check the GPU model, VRAM and stock for your needs, because capacity for the newest cards can be limited.

How much does LLM hosting cost in India? It depends on GPU, billing basis and utilization. Published examples on 15 October 2026 range from ₹40 per hour for an L4 at Cantech to ₹1,80,000 per month for an H100 plan at AceCloud. Add storage, data transfer, tax and support, and compare on your own usage.

What GPU memory do I need to run a 7B, 13B, 34B, or 70B model? Weights alone take about 14, 26, 68 and 140 GB at FP16, and about 3.5, 6.5, 17 and 35 GB at 4-bit. Add the attention cache and overhead, which grow with context length and users. Test your configuration, since these are starting estimates.

Is dedicated GPU hosting suitable for production inference? Often yes, because dedicated GPUs give consistent throughput and isolation. Suitability depends on whether the GPU has enough VRAM, how the provider handles failures, and whether capacity can grow with traffic. Check the SLA and the process for adding GPUs before committing.

Can businesses host private LLMs for internal use? Yes. Open-weight models can run on dedicated or cloud GPUs inside a controlled network. Privacy depends on access controls, logging, retention and where data is stored, so self-hosting alone does not guarantee compliance. Review the model licence and your legal obligations too.

What is the difference between GPU cloud hosting and dedicated GPU servers? GPU cloud hosting usually offers on-demand, often hourly instances that you can start and stop. A dedicated GPU server is rented for a fixed period, often monthly, with the hardware reserved for you. Cloud suits variable use; dedicated usually costs less for continuous workloads.

Can an LLM hosting provider guarantee that data stays in India? Only if the contract says so. A provider may host GPUs in India while backups, logs, monitoring or support tools run elsewhere. Ask for the processing and backup regions in writing, and confirm what happens during support access and service termination.

How should a business choose an LLM hosting provider? Start with the model’s memory needs, expected concurrency and data residency requirement. Then compare GPU availability, billing basis, support, SLA terms and exit conditions. Run a short test with real prompts before signing, and calculate cost at your expected monthly utilization.

Final recommendation

The right provider depends on four things: how much VRAM the model and cache need, where data must stay, how steady the traffic is, and how much operational work your team can take on. Hourly clouds suit experiments, monthly plans suit steady inference, and hyperscalers suit teams already on those platforms.

Purvaco holds first position under the stated criteria, which weight operational responsibility, deployment control and fit for businesses that want a managed, India-hosted partner. That rests on documented facts: dedicated GPU allocation, Docker and Kubernetes support, managed or self-managed operation, 24×7 support and a published list of ISO certifications. It does not rest on price or GPU generation, where other providers publish more. Several details await confirmation from Purvaco.