E2E Networks Share: What Does The Company Do And How Much Do They Make?

Synopsis: E2E Networks, branded as E2E Cloud, is an Indian sovereign cloud provider focused on AI and GPU computing. It offers NVIDIA-powered infrastructure, high-speed Infiniband networking, storage, Kubernetes and its TIR GenAI platform. Rising government and enterprise demand has boosted revenue yet heavy depreciation keeps it in a net loss.

Cloud computing is becoming a key part of India’s digital and AI growth as more companies need powerful computers and secure data storage. One Indian firm, E2E Networks, is positioning itself as a homegrown provider of high-performance cloud infrastructure for these needs. But what does E2E Networks actually do, and how much does it make?

About E2E Networks

E2E Networks, which operates under the brand name E2E Cloud, is among India’s prominent public cloud service providers, with a special emphasis on supporting artificial intelligence projects. The company offers a homegrown cloud platform that uses advanced NVIDIA graphics processors, including Hopper and L40S chips, which are in high demand for AI research, software development and technology innovation. 

These capabilities are made accessible to users through its TIR AI/ML platform, which caters to developers and researchers working on data-heavy computing tasks. E2E has also formed a strategic partnership with Larsen & Toubro, which owns a 19 percent stake in the company. This collaboration gives E2E preferential access to L&T’s data centre facilities, allowing it to expand its cloud capacity, strengthen infrastructure and deliver more reliable services to its clients. The shares of the company are trading at Rs. 3,020 with a market capitalization of Rs. 6,078.35 crore. 

What Do They Do?

At the heart of E2E Networks is an infrastructure services platform built to power modern cloud computing, especially for artificial intelligence and machine learning workloads. Through its TIR platform and its sovereign cloud architecture, the company supports both traditional CPU-based computing as well as high-powered GPU computing. This platform can be deployed inside a customer’s own premises or accessed remotely through the cloud, giving businesses flexibility in how they use computing resources.

The platform is designed to handle a wide range of tasks, including AI model training, real-time inference, managing vector databases, and deploying AI models in production environments. It is built for scalability, meaning companies can start with limited resources and gradually increase computing power as their needs grow. Today, most of E2E’s business comes from AI and machine learning customers who require very high-performance computing, often using hundreds of GPUs simultaneously through the company’s bare-metal and cloud infrastructure.

Cloud GPUs

E2E provides on-demand access to advanced NVIDIA GPUs that can scale from a single unit to thousands, depending on customer requirements. These GPUs are specifically built for training AI models, fine-tuning large language models, and running high-volume inference applications. The company guarantees 99.95 percent uptime and claims that its pricing is around 70 percent cheaper than global hyperscale cloud providers.

Customers can choose from multiple GPU options based on performance and cost. The NVIDIA H200 is available at Rs. 300.14 per hour, while the NVIDIA H100 costs Rs. 249.40 per hour. The NVIDIA A100 is priced at Rs. 170 per hour, the NVIDIA A40 at Rs. 96 per hour, the NVIDIA L40S at Rs. 83 per hour, the NVIDIA A30 at Rs. 90 per hour, and the NVIDIA L4 at Rs. 49 per hour.

Recently, E2E announced the installation of 1,024 NVIDIA B200 GPUs at its Chennai data centre. This new cluster provides approximately 184 terabytes of total GPU memory and delivers faster performance compared to earlier models. Access to this infrastructure is priced at Rs. 430 per hour.

Linux Cloud

E2E’s Linux-based cloud services are designed to be both high-performing and cost-effective for businesses that need CPU-intensive or memory-heavy computing. These services provide dedicated virtual CPUs, high-speed processors, large amounts of RAM, and NVMe SSD storage, making them suitable for demanding workloads such as big data processing, analytics, and enterprise applications.

High-Speed Networking: Infiniband

To enable large-scale AI computing, E2E uses ultra-fast Infiniband networking technology with speeds of up to 3.2 terabits per second. This low-latency system allows multiple servers and GPUs to communicate efficiently, making it possible to scale cloud GPU clusters far beyond the limits of a single physical machine.

Storage and Cloud Solutions

E2E offers a fully integrated cloud ecosystem that includes services such as load balancers, managed databases, firewalls, auto-scaling tools, and multiple types of storage. Block storage is priced at Rs. 5 per GB per month, while the company’s scalable file system costs Rs. 12 per GB per month. Object storage is available at Rs. 2.50 per GB per month, and the parallel file system is priced at Rs. 20 per GB per month. These services allow businesses to store, access, and manage data securely while maintaining performance and flexibility.

Containers and Serverless

E2E supports high-performance Kubernetes containers and OpenFaaS-based serverless computing, enabling developers to build and deploy cloud-native applications quickly. Users can launch a Kubernetes cluster with master and worker nodes in just a few minutes using the E2E Cloud platform. One of its compute offerings, the C3.8GB Master Node, provides four virtual CPUs running at a minimum frequency of 2.8 GHz, 8 GB of dedicated RAM, and 100 GB of NVMe SSD storage at a monthly cost of Rs. 2,263.

Sovereign Cloud Platform

E2E’s sovereign cloud platform is an enterprise-grade infrastructure designed specifically for AI workloads. It includes more than 50 infrastructure and platform services that give organizations complete control over their data, software stack, and computing scale. The company’s latest M3 series is built for memory-intensive workloads and offers features such as quick service recovery, snapshots, system images, faster input-output performance, and quicker system launch times.

AI Labs as a Service

E2E provides a fully cloud-based AI training environment aimed at universities and educational institutions. A single high-performance GPU can support more than 15 students at the same time, making it cost-effective for large classrooms. The platform includes an integrated learning management system where professors can upload lectures, coding assignments, and assessments in one place. Educators also get a real-time dashboard to monitor student activity and GPU usage. Each university receives its own private and secure AI lab environment that is customized to its requirements. Students can train AI models, work with shared datasets, and deploy models for inference from the same platform, all while using tools that integrate smoothly with their existing curriculum.

TIR GenAI Platform

E2E’s TIR GenAI platform is a GPU-powered environment built for advanced AI training, inference, and model deployment. Through TIR Foundation Studio, users can fine-tune large language models and vision models using techniques such as LoRA and DreamBooth across both CPU and GPU setups. The TIR Pipeline allows developers to create serverless AI workflows using YAML, with features like automated retries, version control, scheduled execution, and detailed logging. The Nodes environment provides collaborative JupyterLab workspaces with pre-configured AI frameworks, flexible storage, and GPU options including A100 and H100.

The platform also includes a managed vector database based on Qdrant for storing AI embeddings, which integrates with tools such as LangChain and LlamaIndex. Its Retrieval Augmented Generation system allows AI models to use external documents to generate more accurate responses and reduce hallucinations. E2E enables multi-node training clusters with shared storage, cluster monitoring, and support for frameworks such as Slurm and PyTorch. The parallel file system ensures fast data access across multiple computing nodes, while reserved IP addresses allow businesses to assign fixed public endpoints to their services.

For real-world applications, the platform supports production-grade inference with auto-scaling endpoints, versioned model repositories, and backends such as Triton and TensorRT-LLM. Through its AILaaS offering, academic institutions can create structured AI courses with dedicated compute plans, enabling collaborative learning on a cloud-based infrastructure.

How Much Do They Make?

Revenue Performance

E2E Networks reported strong growth in its latest quarter, reflecting rising demand for its cloud and AI infrastructure services. The company’s operational revenue reached Rs. 70 crore, marking an increase of 68.3 percent compared to the same quarter last year and 59.8 percent compared to the previous quarter. This expansion was driven by better utilisation of its computing capacity, higher adoption of its platform by enterprise customers, and early progress in strategic and government-linked contracts.

By December 2025, the company’s monthly revenue run rate had risen to Rs. 28 crore. Management indicated that contracts under the India AI Mission have already begun and that engagement with Larsen & Toubro’s enterprise ecosystem is gaining momentum, which should support continued revenue growth in the coming quarters.

The company described Q3 as a period of strong top-line growth and solid operating strength. Its strategic priorities remain focused on maximising GPU utilisation, scaling enterprise and sovereign cloud workloads, and achieving sustainable profitability as new capacity matures.Management expects continued momentum, supported by government-linked AI projects, deeper enterprise partnerships, and rising demand for high-performance computing in India.

Operating Profitability

E2E reported an EBITDA of Rs. 39.6 crore for the quarter, up 60.9 percent year-on-year and 120.2 percent quarter-on-quarter. The company’s EBITDA margin stood at 56.6 percent, highlighting strong operating leverage and efficiency in its core business.

Management attributed this performance to better scale, higher capacity utilisation, and a business model that allows incremental revenue to flow through at higher margins. They also emphasised that cost control remains tight even as the company continues to expand its infrastructure footprint.

Net Profit and Key Reasons for Loss

Despite strong operating performance, E2E posted a loss of Rs. 5.7 crore for the quarter. However, this represented a 58 percent improvement compared to the previous quarter, suggesting that profitability is gradually moving in the right direction as newly added capacity begins to generate revenue.

The primary reason for the net loss was a sharp rise in depreciation expenses, which increased by Rs. 47.6 crore due to the commissioning of large-scale GPU infrastructure in FY 2025 and FY 2026. Finance costs also rose as the company started drawing down its term loans to fund further infrastructure expansion.

Total expenses for the quarter stood at Rs. 30.4 crore, which management described as aligned with its business expansion plans. They reiterated that the inherent scalability of the model should support higher profitability as revenues grow.

Historical Growth Trend

Looking at past performance, E2E recorded year-on-year revenue growth of 43 percent in FY 2024 and 74 percent in FY 2025. This growth was driven by continuous capacity additions and healthy repeat business from existing customers. Over time, the company’s lean cost structure and improving scale have contributed to a steady expansion in operating profitability.

In FY 2025, E2E raised approximately Rs. 1,500 crore to support capacity expansion. Along with higher additions to its reserves, this reduced reliance on external debt and strengthened its overall capital structure.

Conclusion

E2E Networks is building India’s own high-power AI cloud at the right time, when demand for GPUs and data centres is rising fast. The company is growing revenues quickly and making strong operating profits, which shows its business model works. If utilisation keeps improving and government and enterprise demand continues, E2E could move toward sustained profitability in the coming years.

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