E2E Networks is India’s first NSE-listed company focused squarely on AI cloud infrastructure. Founded in 2009 and headquartered in New Delhi, they’ve carved out a unique position: a publicly traded, India-based cloud provider offering serious GPU compute — from NVIDIA L4s all the way up to B200s. If you’re building AI in South Asia or want low-latency access to Indian data centers, E2E deserves a close look.
Why E2E Networks stands out
The GPU lineup here is genuinely impressive for a regional provider. Most India-based clouds top out at A100s, but E2E stocks the full modern stack — B200, H200, H100 (both SXM and PCIe), plus mid-tier workhorses like the L40S and A40. That’s a range you’d expect from a hyperscaler, not a mid-size cloud. Being NSE-listed also adds a layer of accountability and financial transparency you won’t find with most independent GPU clouds.
The feature set is solid across the board: Kubernetes orchestration, Jupyter notebooks, Docker support, API access, persistent storage, and multi-GPU configurations. They offer both reserved and spot instances, giving you flexibility depending on whether your workloads are steady-state training runs or bursty inference jobs.
Pros
- Deep GPU catalog — ten GPU options spanning budget (L4) to cutting-edge (B200)
- India-local infrastructure — low latency for South Asian workloads, data sovereignty compliance
- Publicly listed (NSE) — financial transparency and corporate governance you can verify
- Full feature stack — Kubernetes, Jupyter, Docker, API, persistent storage all included
- Flexible billing — per-hour billing with both reserved and spot instance options
- Multi-GPU support — scale up for large training jobs
Cons
- No SOC 2 compliance — enterprise buyers with strict audit requirements may hit a wall
- Limited global presence — if you need GPUs in US or EU regions, look elsewhere
- No spot pricing listed — spot instances are advertised but current spot rates aren’t published
- Smaller ecosystem — fewer integrations and community resources compared to RunPod or Vast.ai
Getting started
- Sign up at E2E Networks — you’ll need to complete KYC verification
- Add credits via credit card or bank transfer
- Navigate to the GPU cloud section and select your desired GPU configuration
- Choose between on-demand, reserved, or spot instances based on your workload
- Launch your instance with a pre-built AI/ML image or bring your own Docker container
- Access your instance via SSH, Jupyter, or the API
Best for: AI teams and startups in India who want enterprise-grade GPU compute with local data residency, competitive pricing, and the peace of mind of a publicly listed provider.