6 Your organization is building machine learning pipelines that operate at scale. Training datasets are measured in terabytes. Feature stores containing derived data and computed features contain additional terabytes. Model checkpoints, trained model artifacts, and inference caches add more. Experiment data, logs from training runs, and metadata about millions of experiments further expand the footprint. Traditional capacity planning methodologies—measuring current utilization and projecting forward assuming linear growth—catastrophically underestimate the storage needs of AI organizations. Storage for machine learning is not linear. Training datasets grow as you add data sources and longer historical windows. Feature stores grow as you compute new features and expand feature engineering. Model repositories accumulate as you train different variations. Experiment data accumulates if you’re not aggressively pruning it. The growth curve is steep. The inflection points are unpredictable. If your storage runs out mid-training run, you have minutes to add capacity before your job fails and you lose days of progress. For CDOs and AI engineering leaders, storage capacity planning is not a one-time exercise. It is a continuous discipline that requires understanding your data growth patterns, forecasting with buffers, and architecting storage infrastructure that can scale elastically as your needs grow. This post explores how AI organizations should approach storage capacity planning. Understanding Your Data Growth Patterns: Measurement Over Assumption The first step in building an accurate capacity planning model is understanding where your storage is actually being consumed. Many AI organizations run capacity planning exercises based on assumptions—”we think we need about 500 terabytes for our training data”—without actually understanding what’s in that 500 terabytes or how it’s growing. This requires instrumentation. You need to track storage consumption at a granular level: how much storage is consumed by training datasets, how much by feature stores, how much by model checkpoints, how much by experiment data, how much by inference caches. You need to understand growth rates for each category. Some categories, like inference caches, may grow and shrink based on feature deployments. Others, like historical training datasets, may grow more steadily as you archive new data. The second step is understanding inflection points. When do datasets grow suddenly? When a machine learning team expands feature engineering, feature store consumption may double in weeks. When you extend a training dataset backward to include more historical data, training dataset size may jump significantly. When you add a new data source—a new API, a new database, a new set of sensors—the growth curve changes. Tracking these inflection points requires ongoing communication between your data engineering and storage operations teams. When new initiatives are planned—new feature engineering projects, expansion of training datasets, new data sources—your storage team needs to know so they can project the impact. The third step is understanding transient vs. persistent storage needs. Not all data stored in your AI infrastructure is permanent. Experiment data from training runs might be retained for weeks or months and then deleted. Intermediate data generated during feature engineering pipelines might be temporary. Inference cache data might be cleared when new models are deployed. Understanding what is transient vs. persistent helps you forecast whether your growth curve will continue upward indefinitely or whether it will stabilize. Forecasting Methodologies: Moving Beyond Linear Projection Linear extrapolation—taking your current consumption and projecting forward—works if your organization is mature and storage needs are stable. For AI organizations that are still growing their capabilities and expanding their pipelines, linear projection massively underestimates capacity needs. Instead, forecast storage consumption through a multi-factor model that accounts for the different drivers of growth: Dataset growth forecasting requires understanding how your training datasets will expand. If you’re archiving sales data, project based on business growth rates. If you’re archiving sensor data, project based on sensor deployment plans. If you’re collecting user behavioral data, project based on user growth. Interview your data engineering and product teams about plans to expand datasets, and translate that into storage growth. Feature store growth is driven by the pace of feature engineering. If your team is consistently engineering new features, project growth based on the number of new features being created and the storage per feature. Consider whether features are being combined—if you’re computing composite features from simpler features, tiered storage for AI can help manage growth while maintaining performance and cost efficiency. Model and experiment storage grows based on the pace of model development. If your team runs fifty experiments per week, calculate the storage impact per experiment (weights, checkpoints, logs, metadata) and project that forward. As your organization scales, experiment velocity may increase, driving storage growth. Inference and cache storage grows based on deployment. Every model deployed to production generates cache and inference data. Project inference storage based on the planned number of deployed models and the size of your inference workload. Combine these forecasts into a total storage projection. Critically, project not just total storage but storage bandwidth demands. If your projected growth means that you’ll add a petabyte of storage in Q3, that’s not just a capacity planning question—it’s a question of network bandwidth. Can your infrastructure support absorbing a petabyte of new data in the timeframe you’re planning? If not, you need to either accelerate capacity planning or slow data ingestion. Buffering for Bursts and Uncertainty One of the most common causes of storage-related production incidents is insufficient buffering for unexpected data bursts or forecast miss. You reach the projected capacity threshold, and then a new high-priority dataset is ingested, or experimental data retention policies are relaxed, and suddenly you’re at 95% utilization with no headroom. Your capacity planning should include explicit buffers for uncertainty and burst capacity. One common approach is to maintain at least 20-30% free capacity in your storage infrastructure at all times. This provides headroom for bursts, allows for operational flexibility, and prevents you from running right up to the edge of capacity where any forecast error causes problems. For critical path storage—training datasets and feature stores that directly support your production models—the buffer should be larger. If a training job fails because storage runs out, the cost is significant: idle compute resources, delayed model updates, potential impact on downstream applications. Understanding your storage total cost of ownership helps justify maintaining 30-40% free capacity on critical datasets as a reasonable investment in operational reliability. Additionally, plan for rapid capacity expansion. If your forecast indicates rapid growth, ensure that your storage architecture can scale elastically. If you’re planning to grow from 10 petabytes to 30 petabytes over the next eighteen months, can your infrastructure add 100 terabytes of capacity in a single month if necessary? If the answer is no, you have an architecture problem that capacity planning alone cannot solve. Architecture Considerations: Can Your Storage Keep Up? Capacity planning is not just about how much storage you need; it is about whether your storage architecture can deliver that capacity with the performance characteristics your AI workloads require. Training workloads are bandwidth-hungry and latency-sensitive. When a training job pulls data from storage, it needs high throughput. Insufficient storage bandwidth becomes a bottleneck that prevents your expensive GPUs from being fully utilized. If you’re planning to expand training capacity from 10 training jobs running simultaneously to 50, your storage bandwidth needs to increase accordingly. Can your storage infrastructure provide the throughput you’ll need? Similarly, feature stores and inference caches require both capacity and sufficient I/O performance. Feature serving latency directly impacts inference latency. If your feature store is undersized or under-provisioned, inference latency increases, potentially breaching SLAs. This means capacity planning cannot be divorced from performance planning. Forecast storage capacity, but also forecast storage throughput. Calculate the aggregate bandwidth consumption of your planned workloads, and ensure your storage infrastructure can deliver that bandwidth. For many organizations, this drives architectural decisions. Disaggregated storage—where compute and storage are independent resources—makes it easier to scale each independently. If you’re planning for massive storage growth while compute growth is more moderate, disaggregated storage and scaling while reducing costs strategies allow you to grow storage elastically without over-provisioning compute. Locality also matters. If your AI workloads are running across multiple regions or availability zones, where will your data live? Pulling training data across regions for training jobs incurs network costs and latency penalties. Capacity planning that doesn’t account for data locality is incomplete. Implementing Capacity Planning Discipline Effective storage capacity planning in AI organizations requires ongoing discipline and cross-functional collaboration. Your storage and infrastructure teams must have regular forums with data engineering, ML engineering, and product teams. These forums are where you understand new data initiatives, where you update forecasts, where you identify risks early. Implement monitoring and alerting that tracks capacity utilization in real time. When utilization exceeds thresholds—70%, 80%, 90%—alert your infrastructure team so they can proactively add capacity rather than reacting to outages. For critical infrastructure, consider predictive analytics that forecast when capacity thresholds will be exceeded and trigger capacity additions automatically. Maintain a capacity plan that is reviewed and updated quarterly at minimum. As your organization’s priorities shift, as new initiatives are launched, as data growth patterns change, your capacity plan should reflect that. A capacity plan that is static will inevitably become inaccurate. Building Confidence in Your Forecasts The final and most important aspect of storage capacity planning is building confidence that your forecasts are accurate. This requires that you look back regularly at your forecasts and compare them to actual consumption. Did your training dataset grow as fast as you projected? Was feature store growth faster or slower than expected? Did experiment data retention policies hold, or did they creep beyond what you planned? These comparisons drive better forecasts over time. Your organization’s historical data is the most accurate predictor of future growth. By tracking what actually happened versus what you projected, you can refine your forecasting methodology and increase accuracy. The organizations that manage storage capacity most effectively for AI workloads are those that treat capacity planning as a continuous discipline. They maintain ongoing visibility into storage consumption. They forecast with appropriate buffers. They architect storage for both capacity and performance. They measure actual vs. forecast and refine their models continuously. As your AI organization grows and storage demands increase, the cost of forecast error increases. Under-provisioning causes incidents and slows development. Over-provisioning wastes capital and reduces operational efficiency. Treating capacity planning as a strategic discipline is how you navigate this balance and build storage infrastructure that actually serves your organization’s needs. Further Reading Tiered Storage for AI: Scalable Performance and Cost Control Scalable Backup Target Architecture Scale Up Storage While Downsizing Costs AI Data Pipelines: Architecture, Stages, and Best Practices Total Cost of Ownership for Data Storage Storage Cost Per Terabyte: Enterprise Calculation Guide
