Two Models for AI Infrastructure
Every organization deploying AI workloads faces the same infrastructure decision: should you pay a provider to manage GPU servers on your behalf, or colocate your own hardware and run it yourself? The answer depends on your scale, team capabilities, budget structure, and how much control you need over the hardware stack.
Both models deliver physical GPU compute in a professional data center environment. The difference is where the operational boundary falls. This guide breaks down each model across the dimensions that matter most: cost structure, control, staffing, scaling, and risk. For a broader look at what AI hosting encompasses, see our complete guide to AI hosting.
What AI Managed Hosting Includes
In a managed AI hosting arrangement, the provider owns the GPU servers and delivers compute capacity as a service. You pay a recurring fee per GPU or per server, and the provider handles everything below the application layer.
A typical managed hosting package includes:
- Hardware procurement and deployment: The provider selects, purchases, racks, and cables the GPU servers. You choose the GPU model and cluster size; they handle logistics.
- Operating system and driver management: CUDA toolkit, GPU drivers, and OS patches are maintained by the provider's engineering team.
- Monitoring and incident response: 24/7 hardware monitoring with automated alerting. Failed GPUs, memory modules, or storage drives are replaced under SLA, typically within 4 hours.
- Networking: InfiniBand or high-speed Ethernet fabric connecting your GPU nodes, configured and managed by the provider.
- Power and cooling: All electrical and thermal infrastructure, including per-rack power budgeting for high-density GPU deployments.
The result is a turnkey environment where you SSH into provisioned servers and run your training or inference workloads. You do not need data center operations staff. For details on GPU server hosting pricing models, see our dedicated pricing guide.
What Self-Managed Colocation Includes
In self-managed colocation, you own the hardware. The data center provides power, cooling, physical security, and network connectivity. Everything inside the rack is your responsibility.
You handle:
- Hardware selection and procurement: Direct relationships with NVIDIA, AMD, or OEM system integrators. Full control over server specifications, GPU models, NVLink topology, and storage architecture.
- Rack-and-stack: Your team or a contracted smart-hands crew physically installs the hardware. Cable management, power distribution, and labeling are your responsibility.
- OS, drivers, and cluster management: You maintain the software stack, from BIOS firmware to container orchestration. Cluster management tools like Slurm, Kubernetes, or custom schedulers run on your infrastructure.
- Hardware maintenance: When a GPU fails, your team coordinates the RMA, procures a replacement, and installs it. Some facilities offer remote-hands support for physical tasks.
- Security and compliance: Physical cage locks, logical access controls, and compliance documentation are your responsibility within the colocation space.
The trade-off is clear: more work, but complete control over every layer of the stack.
Cost Comparison: Monthly Spend vs Total Ownership
Managed hosting typically costs more per GPU per month because operational services are bundled into the price. Self-managed colocation has lower recurring costs but requires significant capital expenditure and ongoing staffing investment.
Here is how the cost components typically break down for a mid-scale deployment (a cluster of 32 to 64 high-end GPUs, as of mid-2026):
| Cost Component | Managed Hosting | Self-Managed Colocation |
|---|---|---|
| Hardware (CapEx) | None (provider-owned) | Full purchase price (servers, GPUs, networking) |
| Monthly recurring | Per-GPU or per-server fee (includes power, cooling, management) | Per-kW or per-rack colocation fee (power + space only) |
| Staffing | Minimal (application-level only) | 1-3 infrastructure engineers + on-call |
| Hardware replacement | Included in SLA | Self-funded spares inventory + RMA process |
| Network fabric | Included | Self-purchased (InfiniBand switches and cables can represent 15-25% of cluster cost) |
| Depreciation risk | Provider absorbs | You absorb (GPU residual value declines as newer models ship) |
Over a 36-month period, self-managed colocation typically achieves a lower total cost of ownership for organizations running at high utilization rates (above 70-80%). Managed hosting is often more cost-effective for smaller deployments, proof-of-concept phases, or teams that would need to hire dedicated infrastructure staff. For a deeper economic analysis, see our GPU-as-a-Service economics guide.
Control, Customization, and Flexibility
Control is the primary reason organizations choose self-managed colocation over managed hosting. The differences are significant:
- Hardware selection: Colocation lets you choose exact server SKUs, GPU interconnect topology (NVLink, NVSwitch), storage (NVMe vs. distributed), and networking (InfiniBand NDR vs. RoCE). Managed providers offer a fixed menu of configurations.
- Firmware and drivers: In colocation, you control BIOS settings, GPU driver versions, and CUDA toolkit versions. This matters for AI research teams that need specific driver versions for reproducibility or bleeding-edge features.
- Network topology: You design the network fabric to match your workload. Fat-tree, rail-optimized, or custom topologies are all possible. Managed providers typically offer a standard fabric design.
- Security policies: Physical access, network segmentation, encryption, and audit logging are fully under your control in colocation. Managed hosting inherits the provider's security posture.
For enterprise AI infrastructure with strict compliance requirements (financial services, healthcare, government), the control available in colocation is often a non-negotiable requirement.
Staffing and Operational Requirements
The staffing gap between the two models is one of the most underestimated cost factors:
Managed hosting requires your team to handle application deployment, model training, and inference pipeline management. You need ML engineers and data scientists, but you do not need data center operations expertise. A team of 5-10 ML engineers can operate effectively on managed infrastructure without any infrastructure staff.
Self-managed colocation requires additional roles:
- Infrastructure engineers to manage hardware, networking, and the OS/driver stack
- On-call rotation for hardware failures (GPU, memory, NIC, storage, power supply)
- Vendor relationships for hardware procurement, RMAs, and spare parts inventory
- Compliance staff if your deployment has regulatory requirements
For a 64-GPU cluster, this typically means 2-3 dedicated infrastructure engineers in addition to your ML team. At 256+ GPUs, you may need a small infrastructure team of 4-6 people with specialized expertise in GPU cluster operations.
Scaling AI Workloads Under Each Model
Scaling speed and flexibility differ significantly between the two models:
Managed hosting can often provision additional GPU servers within days to weeks, depending on hardware availability. The provider maintains inventory and has established supply chain relationships. Scaling down is also straightforward: reduce your server count at the next contract renewal or, with some providers, on shorter notice.
Self-managed colocation scaling requires hardware procurement (lead times of weeks to months for high-demand GPUs), shipping, rack-and-stack, and commissioning. Scaling down means dealing with hardware that you own: resale, redeployment, or storage. The upside is that you are not locked into a provider's hardware refresh cycle.
For workloads with unpredictable scaling needs, the elasticity of managed hosting reduces risk. For steady-state workloads with predictable growth, colocation's lower per-unit cost outweighs the slower scaling.
Decision Framework: Which Model Fits
| Factor | Choose Managed Hosting | Choose Self-Managed Colocation |
|---|---|---|
| GPU count | Under 64 GPUs | 64+ GPUs at sustained utilization |
| Team | ML-focused, no infra staff | Has or can hire GPU infra engineers |
| Budget | OpEx-preferred, avoid large CapEx | Can fund hardware CapEx |
| Timeline | Need infrastructure in days/weeks | Can plan 2-4 months ahead |
| Control | Standard configs acceptable | Need custom hardware/network/security |
| Compliance | Provider's certifications suffice | Need direct control of security perimeter |
| Workload stability | Variable, experimental | Stable, high utilization |
Use our AI hosting provider checklist to evaluate specific providers across these dimensions.
The Hybrid Approach
Many mature AI organizations use both models simultaneously. A common pattern:
- Self-managed colocation for the core training cluster: large, stable, high-utilization workloads where TCO optimization matters most
- Managed hosting for burst capacity, new projects, and inference serving where rapid scaling and operational simplicity outweigh per-unit cost
This hybrid model lets you optimize cost for predictable workloads while maintaining flexibility for variable demand. It also provides a natural migration path: start with managed hosting, prove the workload, then migrate high-utilization jobs to colocation.
For organizations evaluating the cloud vs. colocation trade-off more broadly, the hybrid approach extends to include public cloud GPUs for truly ephemeral workloads alongside dedicated infrastructure for sustained compute.
Ready to explore both options? Rax offers managed AI hosting and self-managed GPU colocation with competitive power pricing and purpose-built high-density infrastructure. Contact our team to discuss which model fits your workload.
Frequently Asked Questions
What is AI managed hosting?
AI managed hosting is a service model where the hosting provider owns and operates the GPU servers, networking, and storage on your behalf. You purchase compute capacity rather than hardware. The provider handles hardware procurement, rack-and-stack, OS and driver management, monitoring, and hardware replacement. You focus on your AI workloads while the provider handles infrastructure operations.
Is managed AI hosting more expensive than self-managed colocation?
Managed hosting carries a higher monthly cost per GPU because you are paying for operational services bundled into the price. However, self-managed colocation requires you to fund hardware purchases upfront, hire infrastructure staff, and absorb hardware replacement risk. Over a 3-year period, total cost of ownership can be comparable depending on scale, utilization rates, and internal staffing costs.
When should I choose self-managed colocation for AI workloads?
Self-managed colocation makes sense when you need full control over hardware selection, firmware, networking topology, and security policies. It is typically more cost-effective at scale (100+ GPUs) for organizations that already have or can hire data center operations staff. Enterprises with strict data sovereignty requirements or custom hardware configurations also benefit from self-managed colocation.
Can I start with managed hosting and move to colocation later?
Yes. Many organizations start with managed hosting to launch AI projects quickly and migrate to self-managed colocation once workloads stabilize and the team has the operational maturity to manage GPU infrastructure directly. This staged approach reduces initial risk while preserving the option to optimize costs at scale.
