# Q-Star AI Usage Projection

## Positioning

Q-Star AI is building a multi-agent AI creation infrastructure for video, apps, and automated creative workflows.

## Projection assumptions

These are application-planning numbers based on current internal workloads, active demos, and planned early-access rollout. Before final submission, replace baseline values with real evidence from server logs, model dashboards, queue metrics, storage usage, and demo recordings.

## 6-month and 12-month projection

| Metric | Current baseline | 6-month projection | 12-month projection | Primary Google Cloud impact |
| --- | ---: | ---: | ---: | --- |
| Daily generations | 300 | 20,000 | 80,000 | Vertex AI, Gemini API, Cloud Run |
| Monthly active creators | 30 | 3,000 | 15,000 | Cloud Run, IAM, analytics |
| API requests / day | 5,000 | 350,000 | 1,500,000 | Cloud Run, API gateway, Monitoring |
| Async jobs / day | 1,000 | 120,000 | 500,000 | Pub/Sub, Cloud Run workers |
| Token consumption / month | 60M | 2B | 8B | Vertex AI, Gemini API, model routing |
| Video rendering minutes / month | 1,200 | 80,000 | 300,000 | GPU compute, rendering workers |
| GPU inference hours / month | 120 | 3,000 | 10,000 | GPU Compute Engine or GKE |
| Generated storage | 0.5 TB | 12 TB | 50 TB | Cloud Storage |
| Usage events / month | 500,000 | 50M | 250M | BigQuery |
| Logs and monitoring events / month | 2M | 120M | 600M | Cloud Logging, Cloud Monitoring |

## Cloud spend narrative

The largest expected cost drivers are:

- AI model calls for planning, writing, code generation, and multimodal generation
- Video rendering and media processing jobs
- GPU inference for image, audio, and video workloads
- Object storage for generated media, APKs, prompts, and workflow artifacts
- Cloud Run execution for APIs, agent workers, and model-routing services
- BigQuery analytics for workload metrics, generation history, and cost tracking
- Monitoring and logging for production reliability

## Evidence to collect before submission

- Current daily generation count
- Current model API usage screenshots
- Current server or worker workload logs
- Current storage usage screenshot
- Queue or workflow execution screenshot
- Demo video showing prompt to output
- Architecture diagram showing Google Cloud services
- Public English landing page

## Conservative application wording

Q-Star AI is currently operating early internal and pilot workloads across AI video generation, app generation, agent orchestration, and automated creative workflows. Over the next 6 to 12 months, we expect usage growth to be driven by multi-agent workflow execution, AI model routing, video rendering, generated asset storage, async task queues, and analytics. Google Cloud credits would directly support this growth through Vertex AI, Cloud Run, Cloud Storage, Pub/Sub, BigQuery, Cloud Monitoring, and GPU-backed compute.