When it comes to AI, the compute layer matters. And it matters a lot.
The difference between a high-performing, cost-effective AI application and a sluggish, budget-burning one often comes down to how well your infrastructure matches your model’s needs.
So, what are your options? Broadly speaking, they fall into three camps: Hyperscale Cloud, IaaS Platforms, and Bare Metal.
1. Hyperscale Cloud (AWS, Azure, GCP)
Pros:
- Instant access to scalable GPU clusters
- Integrated ecosystem (storage, orchestration, monitoring)
- Global presence and compliance frameworks
Cons:
- Expensive for sustained or bursty training loads
- Overhead from shared tenancy and noisy neighbours
- Vendor lock-in through proprietary tools and pricing structures
Use Case: Ideal for experimentation, rapid prototyping, and deployments where agility outweighs cost.
2. AI-Optimised IaaS (e.g. Cudo Compute, Lambda Labs, CoreWeave)
Pros:
- Competitive pricing on GPU compute (per-hour or reserved)
- Access to modern hardware (H100s, A100s, RTX 6000s)
- Often less vendor lock-in
Cons:
- Less mature ecosystem and fewer managed services
- Requires more DevOps and MLOps overhead
- Limited geographic footprint compared to hyperscalers
Use Case: Great for sustained model training, custom workloads, or companies building AI as a core product.
3. Bare Metal / On-Prem Infrastructure
Pros:
- Full control over cost, security, and data locality
- No shared tenancy = predictable performance
- Long-term cost savings at scale
Cons:
- High upfront CapEx (hardware, datacentre, cooling, staffing)
- Long lead time to deploy and scale
- Difficult to adapt quickly as model needs evolve
Use Case: Reserved for large enterprises, research institutions, or AI-native businesses operating at scale.
Key Factors to Consider
1. Workload Type
- Are you training large models or fine-tuning existing ones?
- Is inference latency a concern?
2. Scale and Predictability
- Do you need GPU capacity all the time or in bursts?
- How predictable is your usage?
3. Data Governance and Compliance
- Do you have strict data residency or security requirements?
4. Budget and Resource Constraints
- Can you afford the upfront investment of bare metal?
- Do you have DevOps/MLOps staff to manage infrastructure?
What This Means for You
Choosing the right infrastructure isn’t just a tech decision, it’s a strategic one. For most, a blend of cloud and AI-optimised IaaS is the best balance of speed, cost, and flexibility.
If you’re running training workloads intermittently or have a lean team, cloud services will help you move fast. But as you scale or aim to bring inference in-house, platforms like Cudo Compute can offer significant performance-per-pound advantages.
In our next post, we’ll dive into the cost realities of DIY AI, because infrastructure is just one part of the bill.
