What is Training Infrastructure?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

For most companies, buy managed services like AWS SageMaker Training, Google Vertex AI Training, or Azure ML. These handle GPU provisioning, job scheduling, and distributed training without operational burden. Build custom infrastructure only when managed services become cost-prohibitive above $10,000/month, you need hardware not available in managed services, or data residency requirements prevent cloud usage. Budget $500-5,000/month for managed training infrastructure depending on training frequency and model size.

Use spot or preemptible instances for training, saving 60-80% compared to on-demand pricing. Implement checkpointing to handle interruptions. Schedule training during off-peak hours for lower pricing in some regions. Right-size GPU selection since not every model needs an A100. Use profiling to identify CPU-bound bottlenecks before upgrading GPUs. Pool GPU resources across teams through a shared training cluster with fair-share scheduling. Most teams can reduce training costs 50%+ through these optimizations without any model changes.

Start with a single GPU instance on a managed service. You need a training script, a dataset in cloud storage, a way to track experiments using MLflow or similar, and a method to export the trained model. Total cost for initial setup: $50-200 for compute plus 2-3 days of engineering time. Don't invest in distributed training, custom schedulers, or GPU clusters until you have a proven model that needs to train faster. Most first production models can train on a single GPU in under 4 hours.

For most companies, buy managed services like AWS SageMaker Training, Google Vertex AI Training, or Azure ML. These handle GPU provisioning, job scheduling, and distributed training without operational burden. Build custom infrastructure only when managed services become cost-prohibitive above $10,000/month, you need hardware not available in managed services, or data residency requirements prevent cloud usage. Budget $500-5,000/month for managed training infrastructure depending on training frequency and model size.

Use spot or preemptible instances for training, saving 60-80% compared to on-demand pricing. Implement checkpointing to handle interruptions. Schedule training during off-peak hours for lower pricing in some regions. Right-size GPU selection since not every model needs an A100. Use profiling to identify CPU-bound bottlenecks before upgrading GPUs. Pool GPU resources across teams through a shared training cluster with fair-share scheduling. Most teams can reduce training costs 50%+ through these optimizations without any model changes.

Start with a single GPU instance on a managed service. You need a training script, a dataset in cloud storage, a way to track experiments using MLflow or similar, and a method to export the trained model. Total cost for initial setup: $50-200 for compute plus 2-3 days of engineering time. Don't invest in distributed training, custom schedulers, or GPU clusters until you have a proven model that needs to train faster. Most first production models can train on a single GPU in under 4 hours.

For most companies, buy managed services like AWS SageMaker Training, Google Vertex AI Training, or Azure ML. These handle GPU provisioning, job scheduling, and distributed training without operational burden. Build custom infrastructure only when managed services become cost-prohibitive above $10,000/month, you need hardware not available in managed services, or data residency requirements prevent cloud usage. Budget $500-5,000/month for managed training infrastructure depending on training frequency and model size.

Use spot or preemptible instances for training, saving 60-80% compared to on-demand pricing. Implement checkpointing to handle interruptions. Schedule training during off-peak hours for lower pricing in some regions. Right-size GPU selection since not every model needs an A100. Use profiling to identify CPU-bound bottlenecks before upgrading GPUs. Pool GPU resources across teams through a shared training cluster with fair-share scheduling. Most teams can reduce training costs 50%+ through these optimizations without any model changes.

Start with a single GPU instance on a managed service. You need a training script, a dataset in cloud storage, a way to track experiments using MLflow or similar, and a method to export the trained model. Total cost for initial setup: $50-200 for compute plus 2-3 days of engineering time. Don't invest in distributed training, custom schedulers, or GPU clusters until you have a proven model that needs to train faster. Most first production models can train on a single GPU in under 4 hours.