The Economics of Intelligence: Why Smaller Models Win in Production
The Indian cloud story, for most of the last decade, has been about software.
We wrote the apps, and microservice, and the infrastructure was owned by players in the west.
When the LLM wave hit late 2022 – the same arrangement carried forward: teams in Bengaluru, Pune, Gurugram, and Mumbai built on top of providers like OpenAI and Anthropic, and accepted that the compute happened somewhere in the US.
That arrangement has started to break, though, and it is worth understanding why, because the answer is not just “GPUs are cheaper to rent in India.”
Three forces are pushing Indian AI teams off AWS, Azure, and Google Cloud for their GPU workloads.
Law: DPDP Act 2023 made data residency a legal obligation. Which means you need GPU servers based in India, with legal guarantees that your data can’t be requested by foreign governments.
Economics: On a general-purpose cloud, other than paying for the GPU compute, you end up paying for management, egress, storage I/O, and surcharges you didn’t know existed at signup. Costs are generally inflated by 30-40% from what was advertised.
Fit: General purpose clouds were built for CPU workloads and elastic web traffic. And their pricing and service offering reflects it. AI needs sustained GPU access, fast fabric, an MLOps layer that ships integrated, and predictable pricing at scale. You still don’t really get that with them.
Seeing these challenges, a set of Indian AI cloud providers have moved into the gap.
Here are the five worth evaluating.
| Provider | What it is | Best fit |
| Neysa | Full-stack AI cloud with integrated compute, MLOps, and AI security | Enterprise production AI, regulated sectors, open-weight model teams |
| Yotta Shakti Cloud | Tier IV data centers with GPU pods, Slurm/K8s clusters, and Sarvam AI services on top | Sovereignty-first buyers, GIFT City IFSC, PSU and government procurement |
| E2E Networks | GPU-first Indian cloud with the TIR MLOps platform | Startups and research teams, burst workloads, price-sensitive self-service |
| Tata Communications Vayu | Unified IaaS + PaaS + AI + connectivity from Tata’s backbone | Large enterprises on Tata network, Government Community Cloud needs |
| Cyfuture Cloud | Developer-focused GPU rental on MeitY-empanelled India infra | Individual developers, early prototyping, bursty experimentation |
For production AI in India today, Neysa is the one that addresses all three forces at once.
Neysa
Why Neysa is the best hyperscaler alternative for AI & GPU workloads
Neysa is the only provider on this list that covers all three factors – it’s DPDP Act compliant, offers great AI economics, and has everything teams need to take their AI from an idea to production in one system.
Neysa’s AI acceleration platform is called Velocis. It is a full-stack AI cloud, which means the GPU compute, the MLOps layer, and the managed inference service all sit inside one system.
On the compute side, you get NVIDIA H100 SXM, H100 NVL, H200 SXM, L40S, L4, plus AMD MI300X [Blackwell GPUs in the near roadmap]. You can consume them as bare metal, as VMs or as managed Kubernetes.
The networking fabric built on RoCEv2 at 3.2 Tb/s per node with 1:1 bisection bandwidth, in the same league as Azure’s InfiniBand on ND H100/H200 VMs.
Above the compute, Velocis wires together the MLOps layer that Indian teams would have to spend a while assembling.
PyTorch, TensorFlow, HuggingFace, Jupyter, MLflow, W&B, Kubeflow Pipelines, Airflow, pre-configured. Data ingestion, DBaaS, CI/CD for ML included.
End to end open source. Nothing locked to Neysa, which means no lock-in either.
Then there’s Aegis LLM Shield, which the general-purpose clouds don’t have. It sits inline on every inference endpoint and handles prompt injection, jailbreaks, PII redaction, model poisoning, and exfiltration as one product.
What I liked
- Pricing is all-inclusive
- You speak to sales and book a reserved access. The rate is fixed – and it covers compute, storage, egress, Kubernetes, Jupyter, MLflow, W&B, inference endpoints.
- You can predict your GPU costs. No surprise bills.
- You speak to sales and book a reserved access. The rate is fixed – and it covers compute, storage, egress, Kubernetes, Jupyter, MLflow, W&B, inference endpoints.
- Support response is quick
- You get dedicated MLOps engineer who help you figure things out. Hyperscaler equivalents sit behind Premier support contracts that typically run into six figures a year.
Limitations I faced
Blackwell is not yet deployed. Its in the pipeline – but most providers in India don’t offer blackwell, anyway.
Raw GPU number is smaller than a general purpose clouds. For eight-to-sixty-four-GPU clusters (which covers most enterprise Indian AI), not a constraint. For a thousand-GPU single training run, Neysa is still building toward it.
Model catalog is open-weight only. If you need GPT, Claude, or Grok as a hosted API, Neysa isn’t the platform.
Yotta
Yotta’s main advantage is its physical infrastructure, with Tier IV-certified data centers in India and a GIFT City footprint that is useful for regulated financial workloads.
It offers GPU compute, Slurm and Kubernetes, and managed inference as well.
Sarvam AI, which runs on Yotta, adds APIs for Indian-language workloads across speech, text, and translation.
What I liked
GPU compute is available on bare metal with full InfiniBand interconnect.
INR billing for GPU compute and Sarvam AI access simplifies payments, though other providers on this list also offer INR billing.
Limitations I faced with Yotta
MLOps capabilities are limited. You get GPU pods and clusters, but pipeline orchestration, experiment tracking, and cost attribution are left to the user.
There is no AI-specific security layer. You need to get an external tool.
The headline bare metal rate does not include the control plane, support, or interconnect, which are billed separately.
Independent reviews are thin. For a provider at this scale, almost no G2 / Trustpilot / Reddit footprint is itself a signal.
E2E Networks
E2E’s main advantage is self-service speed. Users can provision H100 nodes directly through their portal.
What I liked
TIR offers a managed ML workflow comparable to Vertex AI or SageMaker.
Per-hour billing with no minimum commitment makes it easy to shut down when you’re not using compute.
Object storage (EOS) is S3-compatible and priced aggressively.
Limitations I faced
There’s no integrated AI security layer, or native observability available.
Committed-instance pricing has an edge to watch for. This Reddit thread reports that terminating a commit early forfeits the remaining period entirely, with no documented partial refund.
Beyond the compute rate, E2E charges separately for storage, firewall, load balancer, VPC, SSL, security compliance, egress.
No AI security layer. No integrated experiment tracking at the GPU rate.
Tata Communications Vayu
Vayu is a GPU cloud from Tata Communications, launched as a rebrand of IZO Cloud.
It offers H100 and L40S compute through Vayu AI Cloud, an MLOps layer called AI Studio, and a Government Community Cloud variant for PSU and central government workloads. The bundle integrates with Tata’s broader IaaS, PaaS, security, and connectivity services.
What I liked
No egress charges on the GPU rate.
Their Government Community Cloud offering is purpose-built for regulated-sector.
Enterprise delivery is mature, with TAM, formal SLAs, and account management standard on larger deals.
Limitations I faced
Vayu is new. AI Studio is earlier-stage than Velocis or TIR. Model catalogs is thinner than Bedrock or Vertex AI Model Garden.
GPU pricing isn’t transparent. The pricing page lists service catalogs, but the specific H100 and L40S hourly rates are behind sales.
No bare-metal-first offering. GPUaaS is managed VMs.
No AI-specific security.
Cyfuture Cloud
Cyfuture is a per-hour GPU rental platform aimed at developers and small teams.
It offers H100, H200, A100, L40S, and V100 GPUs, with per-minute billing and DPDP compliance built in.
What I liked
Entry-level pricing is among the lowest on this list.
Provisioning is fast, with pre-configured PyTorch, TensorFlow, and vLLM environments.
Data residency and MeitY empanelment are documented rather than implied, which is useful for teams that need audit trail clarity.
Limitations I faced
Enterprise maturity is the weak spot. SOC 2 Type II, ISO 27001 for the AI platform, published enterprise SLAs, TAM-level support, all less visible than the rental experience itself.
No integrated MLOps. Raw compute with pre-configured frameworks. Model registry, experiment tracking, pipeline orchestration, drift detection, governance: you build all of it yourself. The fit force is thin.
No AI-specific security.
Brand recognition in enterprise procurement is lower. For a CTO defending the choice to a board in a regulated sector, that’s a drag even when the tech is fine.
