Pricing

Serverless Hosted API Pricing page

The roboflow.com/credits page mentions that 1 credit corresponds to 500 seconds of inference time. A more accurate formula is the following:

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

Where x-processing-time and x-remote-processing-time are HTTP Response headers, in float format (seconds). See roboflow.com/pricing for credit pricing.

_{Model Inference}

In the example below, we run inference on coco/39 model (RF-DETR Small, 560x560). In response headers we can find x-processing-time , which is 81ms. In this case, we'd have credits = max(81, 100) / 500,000 = 0.0002 credits , or 0.2 credits per 1000 images.

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

If you run the same request a 10 minutes later, it could happen that the model has been unloaded and needs to be loaded to the GPU again - a cold start. Model loading can take up to a few seconds, and is highly correlated with the delay between inferences.

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 , or 2.2 credits per 1000 (cold start) images.

Workflow run

For Workflows, we split model inference from general Workflow processing. This means that Workflow itself will be executed on (cheaper) CPU-only machines, and only use GPU machines for model inference, resulting in a more cost-effective processing.

License plate recognition Workflow with 2x object detection model, dynamic cropping, multiple visualizations, and Gemini for OCR

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 , so 0.0023 credits for processing, and some tiny amount for the Gemini API call (depending on token count, see roboflow.com/credits).