Data Persistence
HDD capacity with NVMe caching stores large datasets while keeping frequently accessed data fast.
StorageHADOOP · SPARK · DATA LAKES AT SCALE
Big Data Dedicated Servers Built for
Hadoop, Spark, and Data Lakes at Real Scale
- 40TB to 144TB+ Raw Storage Capacity
- HDD Storage + NVMe Cache for Hot Data
- Private Networking for Hadoop & Spark Clusters
- Mix Compute & Storage Nodes for Custom Clusters
- Hadoop Cluster With Ceph/GlusterFS Setup
- Full root access with IPMI/KVM
7-day money-back guarantee · ISO 27001 certified · 99.5% uptime SLA
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40–144TB+
Raw Storage Capacity
Unmetered
Data Ingress
ISO 27001
Certified Infrastructure
99.5%
Uptime SLA
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- Cluster Management Support
- Unmetered Ingress
- NVMe Cache for Hot Data
| Plan | CPU / Processor | RAM (GB) | Bandwidth (Mbps) | Storage | Monthly Price (₹) | Order Now |
|---|---|---|---|---|---|---|
| ECG-73V3 (Xeon E5-2673v3) | 10C / 20T @ 2.4 GHz | 32 | 1000 | 2 x 480 GB SATA SSD | ₹7,499/mo | Get Now |
| EEG-70V3 (Dual Xeon E5-2670v3) | 24C / 48T @ 2.3 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹14,100/mo | Get Now |
| ECG-I5 (Core i5-12400) | 6C / 12T @ 2.5 GHz | 32 | 1000 | 2 x 1 TB NVMe SSD | ₹14,400/mo | Get Now |
| ICG-RZ5 (Ryzen 5 5600G) | 6C / 12T @ 3.9 GHz | 32 | 1000 | 2 x 1 TB NVMe SSD | ₹14,550/mo | Get Now |
| EEG-80V4 (Dual Xeon E5-2680v4) | 28C / 56T @ 2.4 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹14,700/mo | Get Now |
| EEG-99V4 (Dual Xeon E5-2699v4) | 44C / 88T @ 2.2 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹16,650/mo | Get Now |
| HCG-RZ9 (Ryzen 9 5950X) | 16C / 32T @ 3.4 GHz | 32 | 1000 | 2 x 1 TB NVMe SSD | ₹19,350/mo | Get Now |
| EEG-PT68 (Dual Platinum 8168) | 48C / 96T @ 2.7 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹23,100/mo | Get Now |
| EEG-GD50 (Dual Xeon Gold 6150) | 36C / 72T @ 2.7 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹26,700/mo | Get Now |
| EEG-PT76 (Dual Platinum 8176) | 56C / 112T @ 2.1 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹26,700/mo | Get Now |
| IEG-EP7542 (AMD EPYC 7542) | 32C / 64T @ 2.9 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹46,800/mo | Get Now |
| IEG-EP7742 (AMD EPYC 7742) | 64C / 128T @ 2.25 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹51,450/mo | Get Now |
| EEG-EP7313 (AMD EPYC 7313) | 16C / 32T @ 3.0 GHz | 64 | 1000 | 2 x 480 GB SATA SSD | ₹51,900/mo | Get Now |
| HEG-EP7542 (Dual AMD EPYC 7542) | 64C / 128T @ 2.9 GHz | 128 | 1000 | 2 x 480 GB SATA SSD | ₹60,450/mo | Get Now |
| HEG-EP7742 (Dual AMD EPYC 7742) | 128C / 256T @ 2.25 GHz | 128 | 1000 | 2 x 480 GB SATA SSD | ₹68,700/mo | Get Now |
| HEG-EP7763 (Dual AMD EPYC 7763) | 128C / 256T @ 2.45 GHz | 128 | 1000 | 2 x 480 GB SATA SSD | ₹79,800/mo | Get Now |
All big data dedicated server plans renew at the same fixed rate with full root + IPMI/KVM access, unmetered ingress, and a 99.5% uptime SLA. Need a custom cluster deployment? Talk to our team.
100% Risk-Free with a 7-Day Money-Back Guarantee
We are confident in the quality of our services. If you're not satisfied, we offer a full refund within 7 days.
Compare VyomCloud vs Big Data Infrastructure Alternatives
For storage-heavy and big-data workloads, the meaningful comparison is dedicated bare metal versus cloud infrastructure. VyomCloud offers purpose-built storage and compute nodes, while OVHcloud provides a broad bare-metal range and DigitalOcean uses virtualized Droplets with separately scalable storage.
| Factor | VyomCloud | OVHcloud Bare Metal |
|---|---|---|
| Entry Price |
₹7,499/mo entry dedicated tier
|
Storage-oriented Advance-STOR from ₹39,050/mo ex. GST
|
| Dedicated Hardware |
Yes, purpose-built bare metal
|
Yes, bare metal
|
| Single-Node Storage |
40TB raw Storage Node; up to 144TB Data Lake Pro
|
Advance-STOR supports 2×24TB to 8×24TB HDD configurations
|
| Clustering / Private Networking |
Compute Node includes private networking and clustering support;
Hadoop Cluster offers 40/100Gbps private switching
|
25Gbps private networking on Advance;
up to 50Gbps on Scale
|
| Big-Data Stack |
Optimized for Hadoop, Spark, Kafka and Elasticsearch;
managed Hadoop clusters available
|
Big Data & Analytics dedicated-server options available
|
| Network Throughput |
Up to 10Gbps
|
—
|
| Data Sovereignty |
Region-based deployment options advertised
|
—
|
Still not sure which plan fits? Contact our team or use our Server Finder to receive a personalised recommendation in seconds.
Built for Storage-Heavy, Data-Intensive Workloads
Features of Big Data Dedicated Servers
Private Networking
Dedicated private connections improve predictable performance for cluster replication and node traffic.
NetworkCustom Configurations
Mix compute and storage nodes to build clusters around your actual data workload requirements.
FlexibilityPredictable Performance
Dedicated hardware avoids noisy neighbors, helping keep batch processing and analytics performance consistent.
PerformanceUnmetered Ingress
Unmetered incoming data transfer helps move large datasets without unexpected bandwidth charges.
BandwidthPublic Cloud Interconnectivity
Private connectivity with AWS, Azure, and Google Cloud supports flexible hybrid data architectures.
Hybrid CloudCluster Management Support
Managed services help configure, secure, and monitor distributed clusters with specialized big-data stacks.
Support99.5% Uptime SLA
Backed by real credits — dedicated infrastructure built for consistent, dependable operation.
SLAReal-World Use Cases of Big Data Dedicated Servers
Hadoop and Spark Clusters
Data engineering teams running distributed processing frameworks need multiple nodes with fast private networking between them. Compute Node and Storage Node tiers can be mixed to match a cluster's compute-to-storage ratio, with the Hadoop Cluster tier handling multi-node deployment and stack installation directly.
Enterprise Data Lakes
Organizations centralizing large volumes of structured and unstructured data need storage that scales into the hundreds of terabytes without performance degrading as capacity grows. Data Lake Pro's 144TB raw capacity and hardware RAID are built specifically for this.
Machine Learning Data Pipelines
ML teams need to store, preprocess, and repeatedly access large training datasets. NVMe caching keeps active datasets fast to read, while HDD capacity handles the full historical dataset without requiring all of it to sit on expensive flash storage.
Log Aggregation and Analytics (Kafka, Elasticsearch)
Businesses running real-time log or event pipelines need consistent I/O and network throughput to keep ingestion and querying responsive as event volume grows, workloads VyomCloud's infrastructure is explicitly optimized to support.
Backup and Archive at Scale
Organizations needing long-term retention of large datasets, compliance archives, historical data for later analysis, use the Storage Node tier's high-capacity HDD configuration as a cost-effective home for data that doesn't need to be actively processed.
Data Migration Projects
Teams moving a large existing dataset from another provider or from on-premises infrastructure benefit from unmetered ingress, which keeps the initial data transfer from becoming an unplanned cost during the migration itself.
Regulated Industries Needing Data Residency
Healthcare, finance, and other regulated businesses that need to keep sensitive data within specific jurisdictions use region-based deployment to align with GDPR, HIPAA, or local data-protection requirements.
Hybrid Cloud Data Architectures
Businesses running part of their stack in AWS, Azure, or Google Cloud but wanting dedicated hardware for the storage- or compute-intensive parts use VyomCloud's private cloud interconnects to link the two environments together.
Scale Storage. Keep Control. Stay Predictable.
Why Choose VyomCloud for Big Data Dedicated Servers?
Deploy scalable high-density storage ranging from 40TB raw capacity up to 144TB+ enterprise data lakes. Benefit from high-capacity Enterprise SATA/SAS drives paired with blazing-fast NVMe caching tiers that accelerate hot analytical queries while keeping long-term data archival highly economical.
100% bare-metal architecture guarantees zero hypervisor overhead and eliminates noisy-neighbor throttling. With dedicated multi-core Intel Xeon and AMD EPYC processors, your distributed Hadoop batch runs, Spark analytics, and Kafka streaming pipelines execute with consistent, predictable low latency.
Designed specifically for distributed big-data architectures including Apache Hadoop, Apache Spark, Kafka, Ceph, and GlusterFS. Interconnect multi-node clusters with isolated 10Gbps/40Gbps private network backplanes for high-throughput inter-node data replication without consuming public bandwidth.
Clear, upfront dedicated server pricing starting from ₹7,499/mo with zero hidden bandwidth egress surcharges. Scale up compute nodes, add storage capacity, or spin up multi-node distributed clusters on predictable monthly terms with an included 99.5% uptime SLA.
Why Businesses Choose VyomCloud for Big Data Hosting Needs
01 · Analytics
Analytics & Data Teams
Dedicated performance keeps analytics jobs and pipeline runs consistent, helping dependent business processes receive data on schedule.
- Consistent analytics performance
- Predictable pipeline completion
01
02 · SaaS
Growing SaaS Data Volumes
Faster storage and network throughput reduce bottlenecks as customer data grows, without forcing immediate infrastructure re-architecture.
- Faster data processing
- Room for growing data
02
03 · Compliance
Compliance & Data Residency
Region-based deployment and dedicated hardware give teams more control over where sensitive data is processed and how infrastructure is managed.
- Better data location control
- Dedicated hardware environment
03
04 · Agencies
Agency Data Infrastructure
Flexible compute and storage options let agencies shape infrastructure around each client's workload instead of using one fixed template.
- Workload-specific infrastructure
- Flexible compute and storage
04
05 · Hybrid Cloud
Hybrid Cloud Architectures
Private interconnects with AWS, Azure, and Google Cloud let enterprises combine dedicated infrastructure with public cloud services.
- Connect private infrastructure
- Use cloud services where needed
05
06 · Reliability
Predictable Data Processing
Dedicated resources provide consistent performance when analytics and pipeline completion times affect downstream business operations.
- Consistent resource availability
- Reliable data processing
06
07 · Flexibility
Compute & Storage Flexibility
Mix compute-heavy and storage-heavy nodes to match infrastructure with different workload requirements across multiple projects.
- Match resources to workloads
- Separate compute and storage
07
08 · Control
Dedicated Infrastructure Control
Dedicated hardware gives enterprises more direct control over infrastructure used for sensitive or compliance-relevant data.
- Greater infrastructure control
- Dedicated processing environment
08
What Our Customers Say
See how businesses use VyomCloud Big Data Dedicated Servers for high-performance computing, analytics, storage, and data-intensive workloads.
“We run a 6-node Hadoop cluster processing daily sales data from 200+ stores. Since moving off shared cloud, our nightly batch jobs finish in a predictable window instead of sometimes doubling in runtime when a noisy neighbor was active.”
“We migrated roughly 60TB of transaction history off S3 to cut storage costs, and the unmetered ingress meant the migration itself didn't add a surprise bandwidth bill on top of the move.”
“Our training sets don't fit comfortably in memory, so the NVMe caching in front of bulk HDD storage matters a lot — active batches stay fast while months of historical data sits on cheaper storage behind it.”
“We started on a single Storage Node for early customers and moved to the managed Hadoop Cluster tier once our data volume outgrew it — being able to expand nodes instead of re-architecting saved us a rebuild we didn't have budget for.”
FAQ
Big Data Dedicated Server FAQs
A big data dedicated server is a physical machine built specifically for data-intensive workloads, high-capacity storage, fast networking, and enough compute to process large datasets without sharing hardware with other tenants. It's the infrastructure layer under distributed systems like Hadoop, Spark, or Elasticsearch.
It depends entirely on your data volume and retention requirements, there's no universal answer. Storage Node's 40TB raw capacity suits smaller data lakes or archival use cases, while Data Lake Pro's 144TB raw capacity is built for enterprise-scale data lakes. If you're unsure, VyomCloud's team can help size a configuration based on your current dataset and expected growth.
Growing businesses use this infrastructure too, particularly once in-house analytics, log processing, or ML workloads start to outgrow what a standard VPS or shared cloud database can handle. You don't need to be an enterprise to need dedicated, predictable I/O performance.
Yes, deployments run on ISO 27001-certified infrastructure with region-based deployment options that support GDPR- and HIPAA-relevant data residency requirements. Data Lake Pro also includes advanced security features for higher-sensitivity workloads.
Yes. You can start on a single Storage Node or Compute Node and later expand into additional nodes or move to the Hadoop Cluster tier as your data infrastructure needs grow.
It depends on scale and predictability. Cloud compute works well for smaller or highly variable workloads that benefit from elastic scaling. Once you're running consistent, storage-heavy batch processing or need guaranteed I/O performance without "noisy neighbor" variability, dedicated hardware typically becomes more cost-effective and predictable at scale.
Both are supported. Storage Node, Compute Node, and Data Lake Pro can each be deployed individually or combined to build a custom cluster topology. For a fully managed, multi-node distributed deployment with clustering software already configured, the Hadoop Cluster tier is built for exactly that.
Yes. Full root access on Storage Node and Compute Node lets you install and configure your own stack. If you'd rather not manage that setup yourself, the Hadoop Cluster tier includes big data stack installation as part of the service.
Yes. Incoming data transfer (ingress) is unmetered, so migrating an existing dataset onto VyomCloud's infrastructure doesn't add unexpected bandwidth charges on top of the migration itself.
Yes. Cluster management support is available to help configure, secure, and monitor distributed data clusters, and it's built directly into the Hadoop Cluster tier for teams that want a fully managed deployment rather than self-managing the stack.