Why Public Cloud Isn’t Always the Right Fit for AI
For many AI teams, public cloud seemed like the obvious choice—until surprise costs, limited GPU availability, and compliance headaches slowed progress.
Are you facing these issues with with public AI infrastructure choices:
- GPU access is inconsistent and regionally limited
- Usage-based billing creates unpredictable costs
- Multi-tenant infrastructure complicates compliance
- Noisy neighbors create latency and resource contention
If your AI workloads are latency-sensitive, subject to strict regulations, or too important to risk performance instability, it’s time to reconsider the default.
Why use Private Infrastructure for your AI workloads
Running AI on private infrastructure or a private cloud offers several key advantages that are critical for modern AI deployments.
First and foremost, it ensures data privacy by keeping sensitive datasets fully under your control. Private AI environments also deliver consistent performance and predictable costs, eliminating the variability and surprise expenses common in public cloud AI services.
By reducing security exposure and providing secure development environments, private infrastructure helps teams build and train models with confidence. Additionally, private clouds allow for custom model training and optimization, ensuring your infrastructure fits your unique AI requirements.
With consistent latency and uptime and no vendor lock-in, Private AI empowers organizations to scale and innovate on their terms.
OpenMetal Private AI: Built for Innovators
Many AI startups begin in the cloud but pivot to dedicated infrastructure after scaling pains and compliance challenges emerge. OpenMetal bridges the gap—delivering on performance, price stability, and control. OpenMetal Private AI is the trusted choice for companies that can’t afford to wait, risk security, or gamble on GPU availability and is ideal for many use cases including:
AI startups running proprietary model training or fine-tuning
Regulated industries requiring data locality and isolation
Teams needing consistent low-latency inference
Enterprises escaping the unpredictability of on-demand billing
Vendors reselling their own on-demand GPU slices.
Private Infrastructure Purpose-built for Secure, Scalable AI
When your AI workloads are mission-critical, guesswork isn’t an option. OpenMetal’s Private AI infrastructure delivers the consistency, control, and expert support you need—without the limitations of public cloud or the overhead of building your own. Here’s what sets us apart.
| Feature | Benefit | Why it matters |
|---|---|---|
| Dedicated GPU Nodes | No multi-tenancy | Predictable, isolated performance |
| Fixed Monthly Pricing | No billing shocks | Budget with confidence |
| MIG + Time-Slicing | Built-in GPU sharing options | Optimize usage across teams |
| Slack-Based Support | Real engineers, not ticket queues | Fast problem-solving, personal support |
| Full Infrastructure Access | Storage, networking, BIOS transparency | Enables compliance and performance tuning |
Compare AI Infrastructure Choices
Private AI, Public AI, Buy your Own GPU
Choosing the right infrastructure for AI workloads can dramatically impact performance, cost predictability, and compliance. Below is a side-by-side comparison of three common approaches—OpenMetal Private Cloud GPU, Public Cloud On-Demand, and Buying Your Own GPUs—highlighting key differences in deployment speed, control, scalability, and operational overhead.
| Feature / Consideration | OpenMetal Private GPU Cloud | Public Cloud On-Demand | Buying Your Own GPUs |
|---|---|---|---|
| Deployment Speed | 4 – 8 Week lead time | Instant availability | Delayed by procurement, setup, and configuration |
| Resource Availability | Guaranteed hardware access, not shared | Shared pools, availability varies by region and demand | Full control after setup |
| Security & Data Isolation | Hardware-level isolation by default, no multi-tenancy | Multi-tenancy varies by vendor | Complete control, but dependent on local security practices |
| Performance Consistency | Predictable – no resource contention | Performance varies with shared infrastructure | Consistent, assuming dedicated maintenance |
| Scalability | Scales within dedicated infrastructure without contention | Depends on availability | Requires manual expansion and upfront planning |
| Cost Structure | Fixed monthly pricing avoids surprise charges | Usage-based billing with potential for cost overruns | High initial expense, lower monthly overhead |
| Budget Control | Simple – no managerial approvals per workload | Budgeting per use adds overhead and delays | Fixed expense, but less flexible |
| Support and Guidance | Includes direct access to technical staff and Slack-based support | Limited to general-purpose ticketing | Self-managed unless third-party support is retained |
| GPU Sharing (MIG, Time-Slicing) | Full MIG and time-slicing support with setup assistance | Vendor-dependent support | Requires expertise to implement |
| Compliance & Data Sovereignty | Easily supports compliance-driven deployment needs | Shared infrastructure may violate compliance standards due to lack of visibility into physical location and underlying architecture | Depends on physical location and internal controls |
| Hardware Upgrades | Transition to new hardware is handled by the provider with minimal disruption | Quickly deployable instances allow for self-serve migrations | Upgrades require full replacement of existing hardware |
| Operational Overhead | Low – infrastructure and GPUs are managed | Low – infrastructure and GPUs are managed | High – includes procurement, monitoring, and maintenance |
| Best Fit Use Case | Teams running sensitive, ongoing, or latency-sensitive AI workloads | Short-term, unpredictable, or bursty workloads | Long-term, low-change environments |
| Tipping Point for Adoption | When security, predictability, and availability are priorities | When needs are low-commitment and variable | When internal IT capacity and expertise justifies ownership |
Advantages of OpenMetal Private AI GPU Cloud
Dedicated and Predictable
Guaranteed Availability
Your GPUs are always there when you need them. No waiting, no quotas, and no risk of someone else taking your instance.
No Need to Tear Down and Rebuild
Unlike on-demand public cloud models that encourage spinning down to save costs (and risk not getting the hardware back later), OpenMetal’s model lets you keep your environment running, ensuring stability for periodic or ongoing workloads.
Consistent Performance
The environment is physically isolated. You’re not sharing storage, CPU, or GPU with other tenants. There are no unpredictable slowdowns caused by noisy neighbors.
Low Latency by Design
Because the hardware is dedicated to your workloads, you avoid contention that could introduce latency or reduce throughput
Less Downtime Risk
With no competing users on the same hardware, you’re less likely to encounter the kind of instability that can occur in shared, virtualized environments.
Built-In Compliance and Security
Isolated by Default
Workloads run on dedicated hardware. No multi-tenancy, no shared resources. Eliminating common compliance concerns tied to shared environments.
Infrastructure Visibility
Full access to the architecture (including storage, networking, and BIOS) provides transparency and control required for regulated workloads.
Supports Advanced Security Controls
Because you control the hardware and system stack, you can implement complex security configurations that aren’t feasible in shared or abstracted environments.
Adoption of New Technologies
You’re not restricted by the public cloud provider’s roadmap. Use emerging hardware features and firmware-level protections settings without waiting for upstream support.
Reduced Attack Surface
Fewer moving parts, no co-tenants, and no noisy neighbors mean a smaller and more controlled attack surface for malicious actors to target.
Engineering Access
Direct Access to Engineers
Customers are onboarded into private Slack channels where OpenMetal engineers are available to assist with deployment, troubleshooting, and architecture questions in real time.
Hands-On Collaboration
Whether configuring GPU sharing with MIG, optimizing for AI workloads, or integrating with orchestration tools, OpenMetal engineers work side-by-side with your team to get it right the first time.
Expertise You Don’t Have to Hire
OpenMetal brings real-world experience with AI/ML GPU deployments across a range of use cases, removing the need to hire outside consultants or build deep infrastructure skills in-house.
Hardware Refresh Without Reinvestment
Customers aren’t locked into aging infrastructure. When newer GPUs become available, OpenMetal supports transitioning without the sunk cost that comes with owned hardware.
Interested in Private GPU Servers and Clusters?
GPU Server Pricing
High-performance GPU hardware with detailed specs and transparent pricing.
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Let’s discuss your GPU or AI needs and tailor a solution that fits your goals.
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