What does dense video
actually cost you?
Pick your vertical. Pick your scale. We'll show resource-by-resource savings — compute, storage, bandwidth, power, and VRAM — from replacing legacy dense-video AI with the COSIMO Sparse Geometric Matrix. Anchored to the 5-seed canonical benchmark and public list pricing from AWS, GCP, and NVIDIA.
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Full breakdown by line item
Every line from the underlying model, scaled to your selected workload.
| Line item | Legacy ($/yr) | COSIMO ($/yr) | Savings ($/yr) |
|---|
Methodology & sources
Performance multipliers (3.12× storage compression, 28× inference VRAM reduction, 78.5% fewer parameters, σ=0.017 cross-seed variance) are anchored to the 5-seed canonical benchmark documented in the COSIMO whitepaper, Through the Eyes of AI: From Pixels to Perception.
Pricing inputs are public list rates as of April 2026: AWS S3 Standard $0.023/GB-month, AWS data transfer $0.09/GB, AWS p5.48xlarge $98.32/hour (8× H100), AWS g6.xlarge $0.8048/hour (1× L4), NVIDIA L4 $2,500, Jetson Orin Nano Super Dev Kit $249, U.S. industrial avg electricity $0.083/kWh, Google datacenter PUE 1.1. Cellular IoT data anchored at $0.005/MB; large fleets typically negotiate $0.001–$0.010/MB.
Hyperscaler savings scale linearly with hours of new video ingested per day (anchored at 720,000 hr/day = YouTube-stated 500 hr/min). AV/Robotics savings normalize per vehicle: $1,686/yr recurring + $2,251 one-time edge hardware + $22,500 one-time revenue pull-forward, with time-to-market acceleration of 6 months. Surveillance scales with deployed cameras (anchored at 100,000 cameras × 24×7). Both anchors and the underlying model live in COSIMO_Savings_Calculator_v1.xlsx; reach out to sales@cosimo.ai for the workbook and a custom run on your numbers.
Caveats: dense 3D-CNN inference latency on L4 is FLOP-scaled estimate, not measured. ML-engineering debugging-time savings (rolled into Compute) reflect a $200k senior engineer fully-loaded rate at 5 weeks of seed-luck investigation per year that COSIMO eliminates; replace with your actual figure for a tightened number. The TTM revenue acceleration is one-time and should not be annualized without a multi-year NPV. Surveillance bandwidth and edge-power savings vary widely by deployment topology and are not modeled on the homepage view; the workbook supports custom inputs.
Get the savings methodology by email
We'll send a 3-page PDF detailing how the model works for each vertical, the public list-pricing inputs with citations, every performance multiplier traced back to the canonical 5-seed benchmark, and the explicit caveats about what isn't modeled at the homepage scale.