E2E Networks Review: GPU Pricing, Cloud Services and Alternatives in India

E2E Networks Limited is India's first NSE-listed public cloud provider, founded in 2009 and headquartered in New Delhi. What began as a contractless computing platform for India's earliest startups - powering Zomato, Cars24, 1mg, Jabong, and Healthkart during their formative years - has evolved into India's leading AI-first hyperscaler, now serving 10,000+ businesses.

E2E is best known today for its GPU cloud infrastructure, including NVIDIA B200, H200, H100, A100, L40S, A30 and L4 configurations listed on its public pricing page. Its GPU cloud is positioned for AI training, inference, fine-tuning and development workloads, with on-demand pricing, commitment options and dashboard-based provisioning.

Beyond GPU, E2E offers a full-stack cloud platform: Linux and Windows cloud VMs, managed Kubernetes, object storage, VPC, load balancers, CDN and its TIR AI/ML Platform, a Jupyter-based environment for AI development. The company presents itself as MeitY empanelled and enterprise-ready. Buyers should verify current certifications, egress policy, region availability and compliance requirements directly with E2E before deployment.

E2E Networks is best suited for Indian AI startups, ML teams, developers and businesses that need GPU cloud infrastructure with India-focused availability and transparent public pricing. It is especially relevant for teams comparing H200, H100, A100, L40S and L4 GPUs for model training, inference, fine-tuning, RAG workloads and AI development environments.

Buyers that need hands-on managed cloud operations, deep migration support, multi-cloud consulting or broader enterprise transformation services should compare E2E Networks with alternatives such as AceCloud, Cyfuture Cloud, AWS, Azure and DigitalOcean before finalizing a vendor.

E2E Networks Limited is an NSE-listed Indian cloud infrastructure company. This provider profile focuses on its cloud services and infrastructure pricing, not live market data. Share price, share price history, split dates, financial results and corporate announcements can change frequently.

For the latest E2E Networks share price, financial results, stock split updates or investor disclosures, users should check NSE, official investor relations pages or a regulated financial platform. getInfra.cloud does not provide investment advice or share price targets.

• Broad NVIDIA GPU range listed publicly, including B200, H200, H100, A100, L40S, A30 and L4 configurations
• Public GPU pricing starts from low-cost L4 configurations, with INR estimates depending on the USD exchange rate and applicable taxes
• Spot, promotional or commitment-based rates may be available for suitable workloads. Verify availability and interruption rules before provisioning
• India's first NSE-listed cloud provider - financial transparency, publicly audited quarterly results
• Dashboard-based GPU provisioning and AI/ML environments are available for common training and inference workflows
• All pricing in ₹ INR - no currency risk, no dollar conversion surprises on your bill
• Data transfer and egress policies should be checked before running large data-heavy AI workloads
• India-focused infrastructure can support data residency needs, but buyers should validate exact regulatory and compliance requirements
• Enterprise and compliance credentials are highlighted by E2E. Buyers should verify current certifications for their own procurement requirements
• MeitY empanelled - eligible for Indian government and public sector procurement
• TIR AI/ML Platform included - Jupyter-based environment with pre-built AI stacks and model registry
• Pay hourly, monthly, or annually - save up to 40% with longer commitments

All prices in ₹ INR per month (approximate). Pay-as-you-go hourly billing also available.

GPU Cloud Pricing (Official USD/hr with approximate INR conversion at ₹94.98/USD)

Chennai is listed as one of E2E Networks' India cloud regions. Buyers evaluating South India latency should compare Chennai with Delhi NCR and Mumbai based on GPU availability, storage needs and end-user location.