A Developer’s Guide to Integrating Neysa Aegis LLM Shield
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AI has already slipped into the bloodstream of everyday business.
It’s tagging tumours in radiology labs, crunching fraud patterns for banks, powering crop forecasts, and fine-tuning marketing campaigns in Tier 2 cities. But none of this moves without the cloud underneath. The question is — which one’s actually built for the job?
And this is where things get exciting.
India has seen an explosion of AI cloud providers in the past year. Not generic cloud platforms trying to moonlight as AI-ready, but platforms built with AI in their DNA. We’ve compared them all. From hyperlocal heroes to global heavyweights. Pricing, GPUs, flexibility, and what truly sets them apart.
Beyond a chat on options available, it’s also about knowing where your AI ambitions will actually run.
Let’s start with the one that’s been built for AI from the ground up. Neysa has become India’s first fully AI-focused cloud platform — launched in 2023 and already powering training, fine-tuning, and deployment for teams that can’t afford to lose time to DevOps chaos.
Ideal for early-stage AI startups building LLMs, computer vision apps, or recommender systems. Neysa’s fractional GPU pricing and ready-to-go MLOps environments save devs time and infra cost from day one.
| Plan | vCPUs | RAM | GPU | Price (₹/hr) |
| Entry AI | 6 | 42 GB | H100 (10 GB) | ₹40/hr |
| Mid-Tier AI | 16 | 96 GB | L4 (24 GB) | ₹74.8/hr |
| Enterprise AI | 32 | 180 GB | L40S (48 GB) | ₹100/hr |
| Ultimate AI | 48 | 288 GB | H100 SXM (80 GB) | ₹275/hr |
| Next Gen AI | 48 | 288 GB | H200 (141 GB) | ₹200/hr |
Akash Networks hasn’t followed the crowd. It has disrupted cloud with decentralisation, giving users a peer-to-peer platform that slashes middlemen and costs.
Works best for decentralised applications like blockchain-based AI, federated learning, or when transparency and no single point of control are must-haves.
| Plan | vCPUs | RAM | GPU | Price (₹/hr) |
| Starter | 8 | 64 GB | 1 x A100 | ₹120 |
| Pro | 16 | 128 GB | 2 x H100 | ₹480 |
| Ultra | 32 | 256 GB | 4 x H100 | ₹960 |
Indian-built and AI-focused, Jarvis Labs has become a favourite for those who want granular control — and refuse to waste a second or a rupee more than needed.
Suits solo researchers or startups with tight budgets running small-to-mid-scale training or inference cycles — especially with frequent spin-up/spin-down compute needs.
| Plan | vCPUs | RAM | GPU | Price (₹/hr) |
| Nano | 4 | 32 GB | 1 x A100 | ₹100 |
| Micro | 8 | 64 GB | 1 x H100 | ₹270 |
| Macro | 16 | 128 GB | 2 x H100 | ₹550 |
MilesWeb started in web hosting but has now pushed into AI cloud, with surprisingly strong offerings.
Designed for teams transitioning from traditional hosting to AI — useful for mid-sized enterprises or agencies experimenting with AI model deployment without overhauling their stack.
| Plan | vCPUs | RAM | GPU | Price (₹/hr) |
| Nano | 4 | 32 GB | 1 x A100 | ₹100 |
| Micro | 8 | 64 GB | 1 x H100 | ₹270 |
| Macro | 16 | 128 GB | 2 x H100 | ₹550 |
NeevCloud has quietly positioned itself as the go-to choice for mid to large-scale businesses that want performance without the AWS tax.
Great for research labs and engineering teams running scientific simulations, medical imaging models, or high-throughput AI pipelines — especially with multi-GPU training.
| Plan | vCPUs | RAM | GPU | Price (₹/hr) |
| Nano | 4 | 32 GB | 1 x A100 | ₹100 |
| Micro | 8 | 64 GB | 1 x H100 | ₹270 |
| Macro | 16 | 128 GB | 2 x H100 | ₹550 |
Ace Cloud has steadily grown into a reliable AI cloud choice.
Works for Indian companies needing predictable, locally supported infrastructure for long-term AI deployment — ideal for SaaS tools using embedded AI features.
| Plan | vCPUs | RAM | GPU | Price (₹/hr) |
| Start | 8 | 64 GB | 1 x A100 | ₹130 |
| Grow | 16 | 128 GB | 1 x H100 | ₹300 |
| Scale | 32 | 256 GB | 2 x H100 | ₹620 |
Amazon Web Services has offered the entire AI ecosystem with: Sagemaker, AMIs, EC2, and global reach.
Best for global teams already running workloads on AWS. Works for massive-scale training or deployment in regulated industries that demand enterprise-grade reliability.
| Instance | vCPUs | RAM | GPUs | Price (₹/hr) |
| p4d.24xlarge | 96 | 1.1 TB | 8 x A100 | ₹2,500 |
| p5.48xlarge | 192 | 2.3 TB | 8 x H100 | ₹3,800 |
Google has combined AI-native tools like Vertex AI and TPUs with scalable cloud infrastructure.
Ideal for TensorFlow-first teams, data science platforms, or ML Ops teams using Vertex AI. TPUs suit training transformer-based models fast.
| Instance | vCPUs | RAM | GPU | Price (₹/hr) |
| A2 Mega | 96 | 1.4 TB | 8 x A100 | ₹2,600 |
| A3 Ultra | 192 | 2.5 TB | 8 x H100 | ₹3,900 |
Azure has focused squarely on the enterprise market.
Fits large enterprises using Microsoft stack — e.g. building AI into Excel, Power BI, or internal workflows via Azure AI Studio.
| Instance | vCPUs | RAM | GPUs | Price (₹/hr) |
| ND | 96 | 1.5 TB | 8 x A100 | ₹2,700 |
| NH | 192 | 3.0 TB | 8 x H100 | ₹4,000 |
Oracle has quietly built a cloud that’s become GPU-competitive.
Great for teams already using Oracle DB or apps — useful for AI+data warehousing combos and Kubernetes-based AI deployment.
| Plan | vCPUs | RAM | GPUs | Price (₹/hr) |
| GPU Base | 96 | 1.2 TB | 8 x A100 | ₹2,400 |
| GPU Ultra | 192 | 2.4 TB | 8 x H100 | ₹3,700 |
Build and scale your next real-world impact AI application with Neysa today.
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