Ayneye fit
Operational evidence, timelines, cost-aware review, developer APIs, and controlled beta workflows.
The financial visualizer
Start from the cost question CTOs actually care about: how much video do we need to process, and which workloads truly require GPU semantic depth?
| Comparison lens | Public baseline example | Ayneye private beta |
|---|---|---|
| Cost reference | Google label detection has been publicly listed at $0.10/min (~$6/hr). Twelve Labs Marengo indexing has been publicly listed at $0.042/min (~$2.52/hr). | $0.53–$0.66/hr planning target for eligible CPU-first workloads. |
| Architecture | Managed video APIs or GPU/foundation-indexing platforms depending on vendor and feature. | CPU-first World-State, evidence bundles, bounded capture, and REST/dashboard workflows. |
| Privacy posture | Varies by provider and configuration. | Designed not to send raw frames to external LLMs by default. |
| Query model | Cost depends on vendor, feature, indexing, and query usage. | Process into state first, then query the extracted artifacts within beta limits. |
| Accuracy boundary | Large GPU/foundation models may have stronger semantic depth. | Strong for structured operational evidence; escalate complex/high-risk scenes. |
This table is a conversion aid, not a purchasing guarantee. Public vendor prices change and capabilities are not feature-equivalent.
How to read this comparison
Do not compare price alone. Compare artifact shape, semantic depth, query model, privacy posture, and fallback behavior.
GPU model fit
Subtle human expressions, cinematic context, high-motion scenes, and broad semantic reasoning.
Enterprise fit
Requires benchmark review, safety gates, and explicit acceptance criteria.
Test the cost model with a real beta workspace
Create a limited account, upload a small non-critical video, and inspect the resulting cost.json artifact.