The right choice depends mostly on three factors:
- How predictable your GPU usage is.
- How much operational complexity you're willing to manage.
- Whether data locality, compliance, or latency matter.
Here's how I'd think about each option.
| Option | Best for | Pros | Cons |
|---|
| RunPod | Small teams, startups, experimentation | Easy deployment, good GPU availability, simple UI | More expensive than raw marketplaces, less infrastructure control |
| Vast.ai | Cost-sensitive training, research | Often the cheapest GPU rentals, many GPU types | Variable reliability, heterogeneous hardware, more operational overhead |
| On-prem Kubernetes | Large, steady workloads | Lowest long-term cost at high utilization, full control | High upfront cost, hardware lifecycle, staffing requirements |
RunPod
A good choice if you want infrastructure to "just work."
Ideal when:
- 1–20 GPUs
- Fine-tuning LLMs
- Stable Diffusion training
- Inference services
- Small engineering teams
Advantages:
- Fast provisioning
- Persistent volumes
- Managed networking
- Less DevOps effort
- Good support for Docker and Kubernetes-style workflows
Tradeoffs:
- Hourly prices are higher than marketplaces.
- Limited ability to optimize placement.
- Some GPU types may experience shortages.
Vast.ai
This is essentially a GPU marketplace.
Best when:
- Budget is the primary concern.
- Training jobs are fault tolerant.
- You can checkpoint frequently.
Advantages:
- Extremely low prices for older GPUs.
- Can often save 30–70% versus managed providers.
- Large variety of GPUs.
Challenges:
- Hosts vary in reliability.
- Different CPUs, storage, and networking.
- Spot-like interruptions.
- Need automation to handle failures.
For large-scale distributed training, you'll likely need:
- Automatic checkpointing
- Job retries
- Host health monitoring
- Dataset caching
Without those, engineering time can outweigh infrastructure savings.
On-prem Kubernetes
Makes sense when you consistently utilize expensive GPUs.
Example:
- 8× H100s running 80–90% utilization year-round
- Dedicated ML platform team
- Sensitive datasets
- Heavy distributed training
Advantages:
- Lowest cost per GPU-hour once fully utilized.
- Full control over networking and storage.
- High-performance fabrics like InfiniBand are possible.
- No cloud egress charges.
Disadvantages:
- Large capital investment.
- Hardware depreciation.
- Cooling and power costs.
- Spare parts.
- Cluster administration.
- Capacity planning.
If GPU utilization drops below roughly 50–60%, cloud options often become more economical.
Kubernetes considerations
Kubernetes becomes valuable when you have:
- Multiple users
- Multiple projects
- Shared GPU pools
- Job scheduling
- Auto-scaling
- Reproducible environments
Common components include:
- NVIDIA GPU Operator
- Kueue or Volcano for batch scheduling
- Ray or Kubeflow for ML workflows
- Prometheus + Grafana for monitoring
- Object storage for checkpoints and datasets
For only a handful of GPUs, Kubernetes can be unnecessary overhead.
Cost optimization tips
The biggest savings usually come from improving GPU utilization rather than negotiating lower hourly rates.
1. Maximize utilization
Target:
- 80–95% GPU utilization during training.
Watch for:
- CPU bottlenecks
- Slow storage
- Data loading stalls
- Small batch sizes
2. Profile data loading
Many expensive GPUs spend time waiting for data.
Use:
- More DataLoader workers
- Dataset caching
- Local NVMe
- Prefetching
- Memory mapping
3. Use spot/preemptible capacity
If your jobs checkpoint every few minutes:
- Spot GPUs can reduce costs substantially.
- Keep checkpoints in durable object storage.
4. Mixed precision
Use:
Most modern GPUs see significant speedups with little impact on model quality.
5. Right-size GPUs
Many workloads don't require the newest accelerators.
Examples:
- Fine-tuning often runs well on L40S or A100 GPUs.
- Data preprocessing may only need CPUs.
- Hyperparameter searches can use smaller GPUs.
6. Schedule idle shutdowns
Automatically:
- Delete idle notebooks.
- Stop inactive instances.
- Scale inference deployments to zero when appropriate.
Idle GPUs are one of the easiest ways to waste money.
7. Cache datasets
Avoid repeatedly downloading or preprocessing large datasets.
Use:
- Local NVMe cache
- Shared distributed cache
- Object storage with lifecycle policies
8. Monitor cost per experiment
Track metrics such as:
- GPU hours
- Cost per run
- Tokens processed
- Samples processed
- Training throughput
This makes it easier to identify inefficient jobs.
A practical progression
Many ML teams evolve their infrastructure in stages:
- Early stage (1–8 GPUs): RunPod for ease of use, or Vast.ai if minimizing cost is the priority.
- Growth (8–64 GPUs): Managed Kubernetes or self-managed Kubernetes on cloud infrastructure, with shared scheduling and monitoring.
- Large scale (64+ GPUs, high utilization): On-prem Kubernetes or a hybrid approach, keeping baseline workloads on owned hardware while bursting to cloud providers during peak demand.
For most startups and growing ML teams, a hybrid strategy works well: maintain a reliable baseline on RunPod or owned infrastructure for predictable workloads, and use Vast.ai for fault-tolerant, checkpointed jobs where lower cost outweighs occasional interruptions. This balances operational simplicity with meaningful savings.
