Should I colocate GPUs or keep them in public cloud?

It depends on how steady your GPU demand is, how much control you need, and how latency-sensitive your workload is.

Rule of thumb

• Colocate GPUs if you have:

  • High, steady utilization most of the time
  • Workloads that run 24/7 or predictably
  • A need for lower long-term cost
  • Requirements for data control, compliance, or custom networking
  • Engineering staff who can handle ops, capacity planning, and failures

• Stay in public cloud if you have:

  • Spiky or uncertain demand
  • Fast-changing experiments / model development
  • A need to scale up and down quickly
  • Limited ops bandwidth
  • A preference for managed services over infrastructure ownership

Cost logic

Public cloud is usually best when:

• You need flexibility
• GPUs sit idle a lot
• You’re still learning your traffic patterns

Colocation often wins when:

• You can keep GPUs busy a lot of the time
• You know your required capacity
• You can amortize the hardware and network costs over enough usage

A common break-even pattern:

• Below moderate, inconsistent utilization → cloud
• High, predictable utilization over many months → colo

Operational tradeoff

Public cloud advantages

• Fast procurement
• Easy scaling
• Managed storage/networking options
• Easier geographic expansion
• Less hardware maintenance burden

Colocation advantages

• Lower unit cost at scale
• More predictable spend
• Better control over hardware, firmware, and networking
• Potentially better performance consistency
• Easier to build a dedicated high-bandwidth environment

Colocation drawbacks

• Upfront capex
• Longer provisioning lead times
• You own failures, spares, and refresh cycles
• Requires more infrastructure expertise

Good candidates for colocated GPUs

• Inference services with stable traffic
• Internal training pipelines with predictable schedules
• Fine-tuning jobs that keep GPUs busy
• Large-scale batch processing
• Workloads where cloud egress/network costs are painful

Good candidates for cloud GPUs

• Early-stage ML teams
• R&D and experimentation
• Burst training runs
• Seasonal or event-driven demand
• Teams that need global reach or rapid prototyping

A practical decision framework

Ask these questions:

1. Is GPU usage predictable?

   • Yes → colo becomes more attractive
   • No → cloud

2. Can you keep GPUs utilized most of the time?

   • Yes → colo
   • No → cloud

3. Do you have staff to manage hardware and networking?

   • Yes → colo is feasible
   • No → cloud is safer

4. Do you need to scale quickly or experiment often?

   • Yes → cloud
   • No → colo

5. Is the workload sensitive to latency, bandwidth, or data locality?

   • Yes → colo may help
   • No → cloud is simpler

Often the best answer: hybrid

Many teams do both:

• Cloud for dev, experimentation, overflow, and burst capacity
• Colo for steady-state production or training

That gives you flexibility without committing everything to one model.

My short recommendation

• If you’re still figuring out usage patterns: stay in cloud.
• If you have stable, high GPU utilization and in-house infra maturity: colocate.
• If you’re not sure: start in cloud, measure utilization and cost, then move steady workloads to colo.

If you want, I can help you decide with a simple cost model if you share:

• GPU type(s)
• expected utilization
• monthly hours
• bandwidth/egress needs
• team size / ops maturity