GPU cloud vs bare metal for inference

For inference, the tradeoff between GPU cloud and bare metal usually comes down to:

• Cloud = faster to start, easier to scale, more flexibility
• Bare metal = lower cost at steady high usage, more control, often better performance consistency

When GPU cloud is better

Choose cloud if you need:

• Quick setup / short-term projects
• Variable or spiky traffic
• Easy scaling up/down
• Managed infrastructure with less ops work
• Testing multiple GPU types before committing
• Global deployment across regions

Typical cloud benefits:

• No hardware purchase
• Easier autoscaling
• Can use spot/preemptible instances for lower cost
• Better for experimentation and rapid iteration

Typical cloud drawbacks:

• Can get expensive at high utilization
• Possible noisy-neighbor effects
• Less control over hardware/network stack
• Egress and managed-service fees can add up
• Availability of top GPUs can be limited

When bare metal is better

Choose bare metal if you need:

• High, predictable utilization over months
• Lowest possible cost per inference
• Maximum performance consistency
• Full control over drivers, topology, networking, storage
• Specialized setups like multi-GPU inference, low-latency serving, or custom networking

Typical bare metal benefits:

• Better economics if GPUs are busy most of the time
• Less shared-tenant variability
• More predictable latency
• Can optimize tightly for throughput/latency

Typical bare metal drawbacks:

• Upfront commitment
• Harder to scale quickly
• You manage failures, provisioning, replacement, capacity planning
• Longer procurement/deployment time

Rule of thumb

• < 40–50% average GPU utilization or uncertain demand → cloud
• > 60–70% average utilization and stable workload → bare metal often wins on cost

For inference specifically

Inference workloads often benefit from:

• Cloud if you have bursty traffic, multiple models, or a need to launch quickly
• Bare metal if you’re serving one or a few models continuously and care about latency and cost efficiency

Simple decision matrix

Factor	Cloud	Bare Metal
Time to deploy	Best	Slower
Scaling	Best	Harder
Upfront cost	Low	High
Ongoing cost at scale	Higher	Lower
Operational burden	Lower	Higher
Performance consistency	Medium	High
Best for bursty demand	Yes	No
Best for steady demand	Sometimes	Yes

Practical recommendation

A common strategy is:

1. Start on cloud to validate traffic, latency, and model size
2. Measure:
   • GPU utilization
   • tokens/sec or req/sec
   • p95/p99 latency
   • cost per 1k requests or per million tokens
3. Move steady-state production to bare metal once demand is predictable

If you want, I can also give you:

• a cost comparison formula
• a decision tree
• or recommendations for LLM inference specifically (vLLM/TGI/TensorRT-LLM).