> 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-hi/deploy/serverless-hosted-api-v2/pricing.md).

# Pricing

यह [roboflow.com/credits](https://roboflow.com/credits) पेज में उल्लेख है कि 1 credit 500 सेकंड के inference time के बराबर है। एक अधिक सटीक formula निम्नलिखित है:

```matlab
यदि x-remote-processing-time header सेट है:
   credits = (100ms + x-remote-processing-time) / 500,000ms
else:
   credits = max(x-processing-time, 100ms) / 500,000ms
```

जहाँ `x-processing-time` और `x-remote-processing-time` HTTP Response headers हैं, float format (seconds) में। देखें [roboflow.com/pricing](https://roboflow.com/pricing) क्रेडिट pricing के लिए।

### <sub>Model Inference</sub>

नीचे दिए गए उदाहरण में, हम inference चलाते हैं [coco/39 model](https://universe.roboflow.com/microsoft/coco/model/39) (RF-DETR Small, 560x560). response headers में हम पा सकते हैं `x-processing-time` , जो 81ms है। इस मामले में, हमारे पास होगा `credits = max(81, 100) / 500,000 = 0.0002 credits` , या 1000 images पर 0.2 credits।

```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
```

#### Cold start

यदि आप 10 मिनट बाद वही request चलाते हैं, तो ऐसा हो सकता है कि model unload हो गया हो और उसे फिर से GPU पर load करने की आवश्यकता हो - एक cold start। Model loading में कुछ सेकंड तक लग सकते हैं, और यह inferences के बीच की delay से अत्यधिक संबंधित होता है।

```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
```

**Formula**: `credits = max(1106, 100)/500,000 = 0.0022` , या **1000 पर 2.2 credits** (cold start) images.

### Workflow run

Workflows के लिए, हम model inference को सामान्य Workflow processing से अलग करते हैं। इसका मतलब है कि Workflow स्वयं (सस्ते) केवल-CPU machines पर execute होगा, और model inference के लिए केवल GPU machines का उपयोग करेगा, जिसके परिणामस्वरूप अधिक cost-effective processing होगी।

<figure><img src="/files/d908fe13377d6925807b32540b7cda2c0dfa2154" alt=""><figcaption><p>2x object detection model, dynamic cropping, multiple visualizations, और OCR के लिए Gemini वाला License plate recognition Workflow</p></figcaption></figure>

```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
```

**Formula**: `credits = (100ms + 1054ms)/500,000` , इसलिए **0.0023 credits** processing के लिए, और Gemini API call के लिए कुछ बहुत छोटी राशि (token count पर निर्भर, देखें [roboflow.com/credits](https://roboflow.com/credits)).


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