How much can
your business save?

Methodology and sources

All performance figures come from independent test results published in the COSIMO whitepaper, Through the Eyes of AI: From Pixels to Perception. Savings split across three stages of the AI video pipeline: Encode (frame creation, typically on-device or at the camera), Train (cloud GPU time during model training), and Perform (on-device inference). The headline figures used in this calculator: 3.12× more efficient storage (Encode), 27× less hardware memory needed during operation (Perform), and 78.5% smaller AI models (Train). Test results were consistent across five independent runs with cryptographically verified seeds; accuracy clustered approximately three times more tightly than the legacy baseline. Each figure has a cryptographic verification record. Verify the underlying numbers →

All pricing is based on publicly listed rates from major cloud providers and hardware vendors as of April 2026. Cloud storage is priced at AWS S3 Standard ($0.023 per gigabyte per month). Computing is priced at on-demand cloud server rates from AWS and Google Cloud. Hardware is priced at NVIDIA published rates: $2,500 for an L4 server GPU and $249 for the Jetson Orin Nano edge module. Electricity is priced at the U.S. industrial average of $0.083 per kilowatt-hour. Mobile data for connected vehicles is priced at $0.005 per megabyte, mid-range for fleet plans. Larger customers typically negotiate lower rates than these published figures.

Cloud video savings grow with the daily volume of video processed, anchored to a base of 720,000 hours per day (the figure YouTube reports for new uploads). For autonomous vehicles, the model splits the AV figures into two distinct categories. Operating savings (Encode + Train) — cellular uplink, edge storage, cloud GPU training time, and engineering debug cycles — scale with platform size and are anchored to a 100,000-vehicle platform. These savings apply to AV operators today; they are independent of any chip-substitution decision. Per-vehicle architectural BoM impact (Perform) — the $249 edge module substituting for a $2,500 server-grade GPU — is a design-time figure for an architect designing into a new platform. It does not retroactively apply to existing AV programs running on custom in-house silicon (e.g., Tesla HW3/HW4) or platforms already validated on a fixed substrate.

Surveillance and security savings grow with the number of cameras, calculated for around-the-clock operation. The surveillance vertical includes a real edge consolidation figure that does not require the architectural caveats of the AV vertical. On legacy H.264/H.265 video, a single $249 Jetson Orin Nano supports approximately 8 to 12 cameras at 1080p / 15 fps with standard object detection — the bottleneck is hardware video decode, not pure inference. Under Geometric Video preprocessing, the same Orin Nano supports approximately 50 to 80 cameras: the H.264 decode pipeline is replaced with sparse SGM frames, the inference working set drops 27×, and the model is 78.5% smaller. For a 100,000-camera deployment, that is the difference between 10,000 edge devices ($2.49M) and ~1,667 edge devices ($415K) — a one-time savings of roughly $2.07M on each 5-year hardware refresh cycle, before second-order savings on power, cooling, rackspace, and installation. Unlike the AV per-vehicle figure, this is not a design-time architectural implication: a consumer-grade Jetson Orin Nano is production-viable for surveillance, with no ASIL-D constraint and no redundancy multiplier.

The full underlying model lives in COSIMO_Savings_Calculator_v1.xlsx. Email info@cosimo.ai for a custom run using your specific business numbers.

A few notes for finance and analyst teams. A small number of line items use estimates that will be updated as more direct measurements become available. Engineering time savings assume a $200,000 fully-loaded senior engineer rate and are conservative; the figure does not include opportunity cost from delayed product launches, which often dwarfs raw labor cost. The earlier-revenue figure assumes validation milestones reached 6 to 12 months sooner as a result of accuracy clustering reducing reproducibility investigations — teams spend roughly one-third the time asking whether a result is real or noise. It is a one-time gain and should not be repeated annually in projections. The AV per-vehicle hardware figures are design-time architectural implications for an architect designing a new platform, not present-day fleet operating savings. Production vehicle deployment typically requires ASIL-D certified silicon (the Jetson AGX Orin Industrial at roughly $1,500, not the consumer Jetson Orin Nano at $249) and 2× redundant compute for fail-over. The headline $2,700-per-vehicle figure should be read as architectural BoM headroom available to a platform architect; redundancy and certification choices determine how much of that headroom translates into net savings on a specific platform. Operating savings (Encode + Train) are unaffected by these AV-specific safety constraints and apply to any AV operator running active cloud training and on-vehicle video logging. Some camera-related costs vary widely by deployment, so we have left them out of the calculator and modeled them only in the full workbook. For a custom analysis using your own pricing, contact our sales team.