> For the complete documentation index, see [llms.txt](https://docs.eyepop.ai/developer-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.eyepop.ai/developer-documentation/compute-unit.md). # Compute Unit Compute Units (CUs) are the standard measurement of processing consumption within EyePop.ai. Whenever EyePop analyzes an image, video, livestream, or performs AI inference, Compute Units are consumed based on the amount of work performed. ### Why Compute Units Exist Different AI workloads require different amounts of compute. For example: * Detecting a single object in an image requires less compute than analyzing a high-resolution video. * Running multiple models in sequence requires more compute than running a single model. * Processing a one-hour recording requires more compute than processing a single frame. Compute Units provide a consistent way to measure usage across all EyePop products and capabilities. ### What Affects Compute Unit Consumption? Several factors influence Compute Unit usage: #### Media Type * Images * Video files * Live streams #### Processing Duration Longer videos and streams generally consume more Compute Units than shorter inputs. #### Model Complexity Advanced workflows involving multiple detections, tracking, OCR, or Vision Language Models (VLMs) may consume more Compute Units than simpler pipelines. #### Resolution and Frame Rate Higher resolutions and higher frame rates require more processing and may increase consumption. #### Workflow Configuration A pipeline that performs multiple steps such as: Vehicle Detection → License Plate Detection → OCR will consume more Compute Units than a single-stage detection workflow. ### Monitoring Usage Compute Unit consumption can be monitored through the EyePop dashboard. Usage reporting helps teams: * Track consumption trends * Estimate future costs * Optimize workflows * Understand which applications consume the most resources ### Billing Most EyePop plans include a monthly allocation of Compute Units. If usage exceeds the included allocation, additional Compute Units may be billed according to your plan. For current pricing and included allocations, see the Billing & Pricing documentation. ### Best Practices To maximize Compute Unit efficiency: * Process only the frames required for your use case. * Use object detection to filter content before running more expensive analysis. * Select the smallest model capable of solving the problem. * Leverage tracking to avoid reprocessing the same objects repeatedly. * Test workflows using representative data before deploying at scale. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.eyepop.ai/developer-documentation/compute-unit.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.