Run AI on Your Terms. With Pricing That Makes Sense.
Neysa Velocis delivers performance with cost predictability. We offer better unit economics and 70% lesser TCO due to lean infrastructure structure, purpose-built for AI.
Smart Pricing, Built for You
Save up to 40% when you commit. Both on-demand and reserved pricing options available.
Pro-rated Billing
No guesswork. Just transparent billing, pay only for what you actually consume.
Fit for Every Use-case
From PoCs and testing to production and variable workloads — achieve high model throughput with optimized GPUs.
- AI Cloud
(Managed VM Instances) - Bare Metal GPUs
(Single-Tenant Servers) - Managed Kubernetes Clusters
(Master Nodes / Worker Nodes)
L4 (24GB)
Starts at $1.17 / hour
From $428.37/month
(36-month reserved)
NVIDIA
L40S (48GB)
Starts at $1.95 / hour
From $713.96/month
(36-month reserved)
NVIDIA
H100 SXM (80GB)
Starts at $4.39 / hour
From $1,779.96/month
(36-month reserved)
NVIDIA
H100 NVL (94GB)
Starts at $4.39 / hour
From $1,779.96/month
(36-month reserved)
NVIDIA
H200 SXM (141GB)
Starts at $4.73 / hour
From $1,866.78/month
(36-month reserved)
NVIDIA
8(GPU count) x L40S
$4,306.62 / month
36-month reserved
NVIDIA
8(GPU count) x H100 SXM
$12,433.64 / month
36-month reserved
NVIDIA
8(GPU count) x H200 SXM
$13,822.86 / month
36-month reserved
NVIDIA
VKE Master Node (Non-HA)
$113.34 / month
NVIDIA
3-Master Node (HA)
$212.39/ month
NVIDIA
Pay for Only What You Use
Transparent pricing model with no hidden fees— solely for GPU compute consumption.
- No additional charges for data ingress, egress, or inference transactions.
- No “surprise costs” tied to storage I/O operations or API calls.
- Flexible configuration options available. Find the right fit for your AI workload.
| List of Included Services | Pricing |
|---|---|
| vCPU | Free |
| System RAM | Free |
| Video RAM | Free |
| Flexible storage options(Data & OS Disk) | Free |
| Networking including IP Allocation | Free |
| Managed Kubernetes Services (CPU & GPU clusters) | Free |
| Managed VM (CPU & GPU instances) | Free |
| Jupyter Notebook | Free |
| Native Integration with ML Lifecycle Management tools | Free |
| Inference end-points | Free |
Whether You’re Experimenting or Scaling
We’ve Got You.
Basis the AI workload you are running, choose from three deployment models – private, hybrid and public cloud, each optimized for flexibility, performance, and cost-efficiency.
AI Cloud
(Managed VM Instances)
- Ideal for rapid experimentation, prototyping, and scalable inference.
- Pre-configured AI environments with hourly or monthly pricing.
- Elastic capacity—start small, scale anytime.
Bare Metal GPUs
(Single-Tenant Servers)
- Best for heavy training workloads and high-throughput performance.
- Maximum control, no hypervisor overhead, exclusive GPU access.
- Multi-GPU nodes available on request.
Managed Kubernetes Clusters
(Master Nodes and Worker Nodes)
- Deploy & orchestrate AI workloads at scale with fully managed Kubernetes.
- Includes VCE Master / Worker Node options.
- Ideal for teams standardizing on containers and pipelines.
BUILT in India.
Estimate Your AI Cloud Spend Instantly
Want to model your costs based on GPU type, instance count, usage hours, and commitment term? Talk to our experts.
Features:
- Compare on-demand vs reserved pricing
- Select by workload type: training or inference
- View cost breakdown: monthly & total
- Optional: currency toggle (INR/USD)
- Includes support for Managed Kubernetes Clusters (Master Node pricing)
Enterprise-Grade AI Cloud, Fraction of the Cost
- Up to 70% lower TCO vs general-purpose hyperscalers
- No lock-ins. No opaque billing.
- Open-source and multi-cloud friendly
- Pre-built with orchestration, monitoring, and MLOps
- GPU options for every use case — from fine-tuning to inference
- Fully managed Kubernetes Clusters for scalable deployment orchestration
- Better model throughput and lower latency with optimized GPUs vs general purpose hyperscalers
When Cloud Credits Expire, Costs Explode!
Neysa AI Velocity Program gives startups a predictable path to scale, without the post-credit shock.