Video is the fastest-growing input for AI training pipelines — autonomous vehicles, computer vision, generative models. The datasets are measured in petabytes. So are the storage invoices.
Video-based AI is expanding across industries. Each use case has its own data volume profile — but all of them share the same underlying dynamic: more training data means better models, and more data means more storage cost.
Multi-camera arrays running continuously across test fleets. Raw footage rarely deleted — edge cases and rare events are the most valuable training signals.
Object detection, segmentation, activity recognition. Datasets must be stored, versioned, and re-accessed across multiple training iterations.
Video generation models require massive, diverse training corpora. Dataset size directly correlates with model capability — the pressure to grow datasets is structural.
Defect detection and process monitoring generate continuous high-frame-rate video. Footage retained for model retraining as product lines evolve.
Cloud storage pricing is deceptively simple per-GB — until the dataset is measured in petabytes and training runs read the same data repeatedly. Egress costs, replication, and redundancy multiply the base storage number significantly.
| Dataset Size | Standard Storage / mo | Archival / mo | Egress (10 runs/mo) | Saving with MForja (50%) |
|---|---|---|---|---|
| 100TB | $2,000–$5,000 | $400–$1,000 | $500–$8,500 | $1,450–$7,250 / mo |
| 1PB | $20,000–$50,000 | $4,000–$10,000 | $5,000–$85,000 | $14,500–$72,500 / mo |
| 10PB | $200K–$500K | $40K–$100K | $50K–$850K | $145K–$725K / mo |
| 100PB | $2M–$5M | $400K–$1M | $500K–$8.5M | $1.45M–$7.25M / mo |
Egress costs — charged when data moves from storage to compute — are often the largest surprise in AI infrastructure bills. Training runs that read full datasets multiple times per month generate egress costs that can exceed the storage cost itself. Smaller files mean proportionally lower egress on every run.
MForja operates upstream of encoding — at the point where raw video is being ingested and prepared for storage. It does not interact with the training framework, the model, or the inference pipeline.
Entropy conditioning inserts before encoding. The data loader decodes files exactly as with standard encoding — decoded frames are pixel-identical. Training signal is unchanged.
Smaller files reduce the volume of data that must be read from storage and transferred to GPU memory on every training step. In I/O-bound pipelines — common when training on large video datasets — this directly improves GPU utilization and reduces wall-clock training time. The same infrastructure does more.
GPU compute still dominates most AI infrastructure budgets. But as model training scales and datasets grow, the ratio is shifting — especially for video-heavy applications where data volume grows faster than parameter counts.
Archival tiering reduces storage costs but adds retrieval latency — a real problem when training runs need fast, repeated access to the same data. Entropy conditioning reduces the data volume without affecting retrieval speed.
Tell us your dataset size, codec, and training frequency. We'll model the storage and egress savings for your specific pipeline.