In modern machine learning, the cost of a project is often dominated by a handful of factors. Compute remains the largest share – some hyperparameter tuning runs can cost hundreds of thousands or even millions of dollars. But storage and networking costs are rising too as datasets grow and models become more complex.
The following chart provides a typical breakdown of ML costs across compute, storage, networking and ancillary services:
Compute
Training large models and performing hyperparameter sweeps can consume vast amounts of GPU time. Rightsizing compute means:
- Choosing instance types with the right memory and GPU count for the task.
- Using CPUs for preprocessing and post‑processing instead of GPUs.
- Leveraging spot or preemptible instances where interruption is tolerable.
Storage
Datasets and model checkpoints grow quickly. To avoid runaway costs:
- Use tiered storage (e.g. infrequent access or archive tiers) for older checkpoints.
- Compress and deduplicate data where possible.
- Clean up unused experiment outputs on a schedule.
Networking
Data transfer between regions and services can silently inflate bills. Consider:
- Keeping compute close to data to minimise egress charges.
- Bundling reads/writes to reduce per‑request overhead.
- Leveraging private links or peering where available.
By tackling each of these drivers, you can build ML workloads that deliver results without overspending. Platforms like MLMind provide the granular visibility required to see where compute cycles and bytes are going, enabling you to fine‑tune resources with confidence.
Interested in a tailored breakdown of your cost drivers? Reach out for a free analysis and discover where your biggest savings lie.