Rate Limiter

Class: RateLimiterBlockV1

Source: inference.core.workflows.core_steps.flow_control.rate_limiter.v1.RateLimiterBlockV1

The Rate Limiter block controls the execution frequency of a branch within a Workflow by enforcing a cooldown period. It ensures that the connected steps do not run more frequently than a specified interval, helping to manage resource usage and prevent over-execution.

Block usage

Rate Limiter is useful when you have two blocks that are directly connected, as shown below:

--- input_a --> ┌───────────┐ ┌───────────┐ --- input_b --> │ step_1 │ --> output_a --> │ step_2 │ --- input_c --> └───────────┘ └───────────┘

If you want to throttle the Step 2 execution rate - you should apply rate limiter in between:

• keep the existing blocks configuration as is (do not change connections)

• set depends_on reference of Rate Limiter into output_a

• set next_steps reference to be a list referring to [$steps.step_2]

• adjust cooldown_seconds to specify what is the number of seconds that must be awaited before next time when step_2 is fired

Cooldown limitations

Current implementation of cooldown is limited to video processing - using this block in context of a Workflow that is run behind HTTP service (Roboflow Hosted API, Dedicated Deployment or self-hosted inference server) will have no effect for processing HTTP requests.

Type identifier

Use the following identifier in step "type" field: roboflow_core/rate_limiter@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
cooldown_seconds	float	The minimum number of seconds between allowed executions..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Rate Limiter in version v1.

• inputs: OpenAI, Perception Encoder Embedding Model, Email Notification, Property Definition, Detections Filter, Pixelate Visualization, Byte Tracker, Circle Visualization, SIFT, YOLO-World Model, Color Visualization, Object Detection Model, Detections Merge, Image Blur, Gaze Detection, Buffer, Template Matching, Halo Visualization, Byte Tracker, Moondream2, Polygon Visualization, Environment Secrets Store, Stability AI Image Generation, JSON Parser, VLM as Classifier, Image Convert Grayscale, SIFT Comparison, Detection Offset, Twilio SMS Notification, Crop Visualization, Classification Label Visualization, Depth Estimation, OpenAI, LMM For Classification, Cosine Similarity, Anthropic Claude, Mask Visualization, Camera Calibration, Barcode Detection, Perspective Correction, Multi-Label Classification Model, Background Color Visualization, QR Code Detection, Object Detection Model, Dominant Color, Dot Visualization, Data Aggregator, Bounding Rectangle, Google Vision OCR, Single-Label Classification Model, Llama 3.2 Vision, Roboflow Dataset Upload, CogVLM, Model Comparison Visualization, Dynamic Crop, Image Threshold, First Non Empty Or Default, Size Measurement, Time in Zone, Keypoint Detection Model, Polygon Zone Visualization, Continue If, OCR Model, Cache Set, Local File Sink, Detections Consensus, Stability AI Outpainting, Webhook Sink, Velocity, Distance Measurement, Label Visualization, SIFT Comparison, Line Counter, Cache Get, Keypoint Detection Model, Clip Comparison, Absolute Static Crop, Instance Segmentation Model, Dimension Collapse, Florence-2 Model, Relative Static Crop, Line Counter Visualization, Image Contours, Expression, Single-Label Classification Model, Clip Comparison, Time in Zone, Trace Visualization, Detections Classes Replacement, Keypoint Visualization, Detections Stitch, Byte Tracker, Florence-2 Model, Overlap Filter, Grid Visualization, Roboflow Dataset Upload, OpenAI, Dynamic Zone, Rate Limiter, Path Deviation, Qwen2.5-VL, Multi-Label Classification Model, Detections Transformation, SmolVLM2, Segment Anything 2 Model, Stability AI Inpainting, Image Slicer, VLM as Detector, Camera Focus, Model Monitoring Inference Aggregator, CSV Formatter, PTZ Tracking (ONVIF).md), Bounding Box Visualization, Identify Outliers, Line Counter, Pixel Color Count, Roboflow Custom Metadata, Reference Path Visualization, Ellipse Visualization, Blur Visualization, CLIP Embedding Model, Triangle Visualization, VLM as Detector, Stitch Images, Stitch OCR Detections, Image Slicer, Slack Notification, Detections Stabilizer, Corner Visualization, VLM as Classifier, LMM, Delta Filter, Google Gemini, Identify Changes, Image Preprocessing, Path Deviation, Instance Segmentation Model
• outputs: None

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Rate Limiter in version v1 has.

Bindings

• input

  • depends_on (*): Step immediately preceding this block..
  • next_steps (step): Steps to execute if allowed by the rate limit..
  • video_reference_image (image): Reference to a video frame to use for timestamp generation (if running faster than realtime on recorded video)..

• output

Example JSON definition of step Rate Limiter in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/rate_limiter@v1",
    "cooldown_seconds": 1.0,
    "depends_on": "$steps.model",
    "next_steps": [
        "$steps.upload"
    ],
    "video_reference_image": "$inputs.image"
}