Team flows
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Built for engineering, research & AI teams
Cloud Platform for Science and Industry
The platform for HPC, GPU, data, and AI workloads — cloud-hosted and on-demand when you need it, and private or hybrid when you need more control. Run simulations, ready-to-use scientific software, and high-performance clusters faster with less infrastructure friction.
Problem & Solution
Engineering, research, and AI teams lose valuable time stitching together fragmented infrastructure, software, and data workflows. High-performance workloads need the right platform to run smoothly and help you move from experiments to production faster.
Our platform brings cloud, private, and hybrid HPC with GPU-ready environments and software-ready operations in one place.
On-Demand GPU Compute
Access GPU-powered infrastructure for AI training, inference, simulation, and high-performance workloads whenever you need it
Elastic HPC Clusters
Spin up high-performance clusters for simulation, AI training, and research workloads without manual infrastructure work
Pay-as-You-Go Pricing
Use exactly the compute, storage, and cluster capacity you need and pay only for what you consume
Big Data Workflows
Prepare for large-scale data processing, shared storage, and pipeline-driven analytics in one environment
LLM & AI Workspaces
Give teams ready-to-run environments for model development, fine-tuning, inference, and future AI services
Flows
Workloads
42
Cloud HPC
HPC clusters
Monitor Slurm workspaces, queues, and research services from one place.
Ready
Aurora Slurm Lab
Slurm 23.11 · Scientific computing workspace
- Nodes
- 10
- vCPU
- 256
- Memory
- 1 TB
- Jobs
- 3
Open OnDemand
JupyterLab
Grafana
Cluster details
Aurora Slurm Lab
Ready · Slurm 23.11 · Created by Hana Naderi
Current status
Ready
Cluster services are online and accepting Slurm jobs.
- Template
- Slurm research cluster
- Login node
- 1 × gateway.medium
- Compute pool
- 8 × compute.xlarge
- Control node
- 1 × control.large
Open OnDemandPortal ready
JupyterLabNotebook ready
GrafanaMonitoring ready
cfd-wing-sweepRUNNING64 cores
genome-align-batchRUNNING96 cores
mesh-refine-042QUEUED32 cores
Kubernetes
Create Kubernetes cluster
Choose a template, size the topology, review preflight checks, and launch provisioning.
Template and topology
Start from an approved Kubernetes blueprint.
nebula-lab
Production Kubernetes · v1.30
- Control plane
- standard-2
- Workers
- 3 × medium
Addons and access
Enable the essential services.
AutohealingEnabled
MonitoringEnabled
Kubeconfig access is generated after the real cluster is provisioned.
Preflight and review
Validate and confirm the request.
Approved template and worker size
Quota estimate: 8 vCPU / 24 GB RAM
Wallet check: ready for provisioning
Namenebula-lab
Workers3 × medium
Kubernetesv1.30
AddonsAutohealing + monitoring
Ready to launch
Create your real Kubernetes cluster in WECORE Hub
This website shows a product preview only. Go to hub.wecore.ir to create and manage an actual Kubernetes cluster.
LLM deployment
Launch Qwen 3.7
Start a managed inference endpoint for research and production experiments.
Why WECORE
Built for Faster Results at Scale
Run HPC, GPU, and AI in one place
Launch ready-to-run software faster
Built for scaling
10+ companies use WECORE to streamline HPC workloads — all from one unified platform
Integrations
Unified Cloud Platform for Scientific and Industrial R&D
Connect compute, data, software, and AI workflows in one platform
HPC Workflows
Run simulation, batch, and research workloads with environments prepared for repeatable high-performance execution
Learn More
Data & Storage
Keep datasets, shared storage, and processing flows close to the compute resources they depend on
Learn More
Software Marketplace
Launch scientific and engineering applications in ready-to-run environments tuned for HPC performance
Learn More
AI Environments
Support training, fine-tuning, inference, and future model services without manual infrastructure assembly
Learn More
Pricing
Flexible Pricing for AI Workloads
Choose the model that fits your usage and control needs
On-Demand
Most Flexible
Usage-based
pay as you use
Best for burst workloads, experiments, short-term simulation runs, and GPU-heavy AI jobs
What fits best?
- Pay-as-you-go CPU and GPU capacity
- Fast start for HPC and AI workloads
- Fits burst and exploratory usage
- No fixed long-term commitment
Dedicated
Reserved
monthly / contract
Best for recurring engineering, research, and software-driven workloads that need stable capacity
What fits best?
- Reserved cluster resources
- More predictable cost and performance
- Great for recurring production jobs
- Supports software-ready environments
Private
Custom
architecture based
Best for private, sovereign, or hybrid deployments that need more control and long-term alignment
What fits best?
- Private or hybrid deployment
- More control over security and data
- Designed for strategic workloads
- Built around your environment
Updates
What’s New
Stay up to date with the latest product improvements and powerful new features
FAQ
Answers for Technical Teams
Quick answers on workloads, deployment, pricing, software, and support
WECORE is built for simulation, HPC, GPU-accelerated workloads, AI training, inference, and data-intensive processing. It is designed for teams that need more than general-purpose cloud compute.
Yes. WECORE supports on-demand cloud usage, private environments, and hybrid models for teams that need more control over performance, data, or infrastructure ownership.
Yes. Alongside HPC and GPU infrastructure, WECORE offers a marketplace of scientific and engineering software in ready-to-run environments tuned for high-performance workloads.
WECORE is not priced like a seat-based SaaS tool. The model depends on the workload and deployment choice: pay-as-you-use for burst demand, reserved capacity for recurring workloads, and custom pricing for private or hybrid environments.
Yes. Teams can start with on-demand resources, move into dedicated capacity, and expand into private or hybrid deployments as usage becomes more predictable or strategic.
WECORE is designed to reduce operational overhead, not just expose raw infrastructure. We support teams with platform guidance, deployment planning, and operational help for demanding technical workloads.