Provider Network
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GPU cloud providers available for workload placement
Case Study
Enterprise Cost Optimization
GPU compute is the fastest-growing line item in enterprise AI budgets. stack8s gives engineering and finance teams the tools to cut GPU cloud spend through multi-cloud arbitrage, seamless provider switching, intelligent workload placement, and unified cost visibility across every provider and region.
Spot Savings
Up to 0%
Discount on training with managed spot orchestration
Workload Portability
Zero
Code changes required to switch GPU providers
One Control Plane. Zero Cloud Accounts Required.
We Bring Compute to Your Data
stack8s handles billing, provisioning, and GPU pooling end to end from a single control plane. Your data stays exactly where it is — on-prem, in your sovereign environment, wherever your governance requires. Instead of moving data to the cloud, stack8s brings compute to your data. No need to bring your own cloud accounts, negotiate separate provider contracts, or manage multi-vendor billing. One platform, one invoice, full control.
End-to-End Billing
One consolidated invoice across every GPU provider. No separate cloud accounts, no surprise bills, no vendor-by-vendor reconciliation.
Compute Comes to You
Your data never moves. stack8s orchestrates GPU capacity from 15+ providers and routes it to where your data already lives — preserving sovereignty and compliance.
Single Control Plane
Provision, pool, schedule, and monitor GPU resources across on-prem and cloud from one unified interface. No cloud accounts to configure or maintain.
The GPU Cost Problem
Enterprise AI teams are spending millions on GPU compute, but much of that spend is avoidable. Vendor lock-in prevents price shopping, idle resources burn budget around the clock, and fragmented billing makes it impossible to attribute costs to the teams and projects that incur them. The result: GPU budgets that balloon faster than the AI value they deliver.
GPU Cloud Spend is Unpredictable
GPU pricing varies wildly across providers, regions, and instance types. Without real-time visibility, enterprises overpay for compute they could source cheaper elsewhere.
Vendor Lock-In Inflates Costs
Proprietary tooling and single-cloud commitments prevent teams from moving workloads when better pricing appears, turning short-term convenience into long-term overspend.
Idle GPUs Burn Budget
Training jobs finish but instances keep running. Inference endpoints sit underutilized overnight. Without automated lifecycle management, idle GPU hours accumulate fast.
No Unified Cost View
Finance teams reconcile bills from multiple cloud providers, GPU neo-clouds, and on-prem infrastructure with no single pane of glass for total AI compute cost.
Reserved vs Spot Complexity
Balancing reserved capacity for production inference with spot instances for training requires constant manual attention and deep provider-specific knowledge.
How stack8s Cuts GPU Spend
stack8s treats GPU providers as interchangeable capacity pools. Because every workload runs on Kubernetes-native infrastructure, switching between clouds is as simple as changing a placement policy. No re-architecture. No pipeline rewrites. Just lower bills.
Multi-Cloud Cost Optimization Flow
Why GPU Switching Matters
In a traditional setup, moving a training job from AWS to CoreWeave or Lambda Labs means re-provisioning infrastructure, reconfiguring networking, and adapting storage layers. On stack8s, every provider looks the same to the workload. Switch providers by updating a single placement policy and the platform handles the rest.
No More Cloud Lock-In Premium
Hyperscalers know that once your data and pipelines live on their platform, switching costs keep you paying premium prices. stack8s eliminates that leverage. Your workloads are portable from day one, so every provider competes for your GPU spend on price and performance alone.
Cost Optimization Use Cases
From real-time provider switching to automated idle reclamation, stack8s provides multiple levers to reduce GPU spend without sacrificing performance or engineering velocity.
GPU Switching Between Clouds
Move training jobs from an expensive hyperscaler to a neo-cloud provider in minutes. stack8s abstracts the infrastructure layer so workloads are portable across any Kubernetes-compatible GPU provider without re-engineering pipelines.
Estimate Your Savings
Drag the slider to your current monthly GPU spend and see projected savings across four optimization levers.
Monthly GPU Spend
$100K/mo
$10K$100K$500K$2M
Multi-Cloud Arbitrage
$18K/mo
10–25% on 100% of spend · Savings from routing to cheapest provider
Spot Orchestration
$21K/mo
50–70% on 35% of spend · Discount on training-eligible workloads
On-Prem Repatriation
$13K/mo
40–60% on 25% of spend · Savings on steady-state workloads moved to owned hardware
Idle Reclamation
$7K/mo
25–40% on 20% of spend · Recovery from wasted idle GPU hours
Projected Annual Savings
$690K57% reduction
Monthly Savings
$58K/mo
Estimates use midpoint of each savings range. Actual results vary based on workload mix, provider selection, and operational maturity.
Platform Capabilities
Unified Multi-Cloud Billing
Consolidate GPU spend from every provider into a single dashboard with project-level cost attribution, team budgets, and anomaly alerts.
Portable Workloads
Kubernetes-native orchestration means workloads run identically across any provider. Switch clouds without touching application code or retraining models.
Real-Time Pricing Intelligence
Continuously monitor GPU pricing across providers and regions. Surface opportunities to move workloads where cost is lowest without sacrificing performance.
Quota and Budget Controls
Set per-team GPU budgets, enforce project-level quotas, and trigger alerts before spend exceeds thresholds. Finance teams get predictability, engineers get autonomy.
Intelligent Workload Placement
Policy-driven scheduler considers cost, latency, data residency, and GPU type to place each workload on the optimal provider automatically.
Hybrid Cloud Gateway
Seamlessly span on-premise GPU clusters and cloud providers from a single control plane. Burst when needed, repatriate when cost-effective.
Scenario: Moving a Training Job Across Clouds
Consider a typical enterprise ML team training a large language model. Here is what the experience looks like with and without stack8s.
- Team locked into single cloud provider
- 2–4 weeks to migrate to a new provider
- Storage, networking, and auth must be re-configured
- No real-time price comparison across providers
- Idle GPUs run unnoticed over weekends
Platform / AIK Architect
Right-Size GPU for Every Workload
The most common source of GPU overspend is over-provisioning. Teams default to the most powerful GPU available when a smaller, cheaper option would deliver the same result. AIK Architect analyses workload requirements and recommends the most cost-effective GPU configuration with clear performance and cost trade-offs.
Workload Profiling
Captures model architecture, dataset size, batch size, and latency targets to determine the minimum viable GPU specification for each job.
Cost-Performance Matrix
Compares GPU options (A100, H100, L40S, B200) across providers with real-time pricing, showing the cost-per-token or cost-per-epoch for each configuration.
Budget-Aware Recommendations
Set a monthly GPU budget and AIK Architect recommends the configuration that maximizes throughput within that ceiling, including hybrid on-prem and cloud mixes.
Provider Comparison
See the same GPU type priced across every available provider, with availability, SLA, and data residency information to make informed placement decisions.
Integrated AI and Data Ecosystem
stack8s runs the same open-source tooling your teams already use. No proprietary lock-in means your investment in existing pipelines, models, and workflows transfers to any provider.
Outcomes for Enterprise Teams
Lower Total GPU Spend
Multi-cloud arbitrage, spot orchestration, and idle reclamation combine to significantly reduce the cost of running GPU workloads at enterprise scale.
Full Cost Transparency
Unified billing across every provider with project-level attribution gives finance teams the visibility they need to plan budgets and hold teams accountable.
Engineering Velocity Preserved
Cost optimization happens at the platform layer. Engineers keep their existing workflows, tools, and deployment patterns with zero disruption.
Strategic Cloud Flexibility
Never be held hostage by a single cloud provider again. Maintain leverage in negotiations and shift workloads whenever better options appear.
Stop Overpaying for GPU Compute
Every dollar saved on GPU infrastructure is a dollar that can fund more experiments, train more models, and ship more AI products. stack8s gives enterprises the multi-cloud portability, real-time cost intelligence, and automated optimization they need to make GPU budgets go further.
Whether you are spending six figures or eight on GPU compute, the savings compound at every scale.