> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2/pricing.md).
# 가격
해당 [roboflow.com/credits](https://roboflow.com/credits) 페이지에서는 1 credit이 500초의 추론 시간에 해당한다고 언급합니다. 더 정확한 공식은 다음과 같습니다:
```matlab
if x-remote-processing-time header is set:
credits = (100ms + x-remote-processing-time) / 500,000ms
else:
credits = max(x-processing-time, 100ms) / 500,000ms
```
여기서 `x-processing-time` 및 `x-remote-processing-time` 는 float 형식(초)의 HTTP 응답 헤더입니다. 참고: [roboflow.com/pricing](https://roboflow.com/pricing) 에서 credit 가격을 확인하세요.
### Model Inference
아래 예시에서는 다음에 대해 추론을 실행합니다: [coco/39 model](https://universe.roboflow.com/microsoft/coco/model/39) (RF-DETR Small, 560x560)입니다. 응답 헤더에서 다음을 찾을 수 있습니다: `x-processing-time` , 이는 81ms입니다. 이 경우 다음이 됩니다: `credits = max(81, 100) / 500,000 = 0.0002 credits` , 또는 이미지 1000장당 0.2 credits입니다.
```shellscript
curl -X POST "https://serverless.roboflow.com/coco/39?api_key=API_KEY&image=https://media.roboflow.com/notebooks/examples/dog.jpeg" -I
HTTP/2 200
content-type: application/json
content-length: 995
x-model-cold-start: false
x-model-id: coco/39
x-processing-time: 0.08100700378417969
x-workspace-id: my-workspace-id
```
#### 콜드 스타트
10분 후에 동일한 요청을 실행하면, 모델이 언로드되어 GPU에 다시 로드되어야 할 수도 있습니다. 이를 콜드 스타트라고 합니다. 모델 로딩에는 최대 몇 초가 걸릴 수 있으며, 인퍼런스 간 지연과 매우 높은 상관관계가 있습니다.
```bash
curl -X POST "https://serverless.roboflow.com/coco/39?api_key=API_KEY&image=https://media.roboflow.com/notebooks/examples/dog.jpeg" -I
HTTP/2 200
content-type: application/json
content-length: 995
x-model-cold-start: true
x-model-id: coco/39
x-model-load-details: [{"m": "coco/39", "t": 0.7791134570725262}]
x-model-load-time: 0.5791134570725262
x-processing-time: 1.1060344696044922
x-workspace-id: my-workspace-id
```
**공식**: `credits = max(1106, 100)/500,000 = 0.0022` , 또는 **1000개당 2.2 credits** (cold start) 이미지.
### Workflow 실행
Workflows의 경우, 모델 추론을 일반 Workflow 처리와 분리합니다. 즉, Workflow 자체는 (더 저렴한) CPU 전용 머신에서 실행되고, 모델 추론에만 GPU 머신을 사용하므로 더 비용 효율적인 처리가 됩니다.
2개의 object detection model, dynamic cropping, 여러 visualization, 그리고 OCR용 Gemini가 포함된 번호판 인식 Workflow
```bash
curl --location 'https://serverless.roboflow.com/my-workspace-id/workflows/lpr-workflow' -i \\
--header 'Content-Type: application/json' \
--data '{
"api_key": "API_KEY",
"inputs": {
"image": {"type": "url", "value": "https://storage.googleapis.com/com-roboflow-marketing/docs/cars-highway.png"}
}
}'
HTTP/2 200
content-type: application/json
content-length: 2277416
x-model-cold-start: false
x-processing-time: 6.334797143936157
x-remote-processing-time: 1.0542614459991455
x-remote-processing-times: [{"m": "vehicle-detection-bz0yu/4", "t": 1.0091230869293213}, {"m": "license-plate-w8chc/1", "t": 0.017786026000976562}, {"m": "license-plate-w8chc/1", "t": 0.01506495475769043}, {"m": "license-plate-w8chc/1", "t": 0.012287378311157227}]
x-workspace-id: my-workspace-id
```
**공식**: `credits = (100ms + 1054ms)/500,000` 이므로 **0.0023 credits** 처리에 대한 비용이며, Gemini API 호출에는 아주 작은 금액이 추가됩니다(토큰 수에 따라 다름, 참고: [roboflow.com/credits](https://roboflow.com/credits)).
---
# 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:
```
GET https://docs.roboflow.com/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2/pricing.md?ask=
```
The question should be specific, self-contained, and written in natural language.
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.