Overview

Deploy and manage Serverless endpoints using the Runpod console or REST API.

Endpoints are the foundation of Runpod Serverless, serving as the gateway for deploying and managing your Serverless workers. Each endpoint provides a unique URL that accepts HTTP requests, processes them using your handler function, and returns results.

Send requests

Learn how to send requests to your endpoints.

Endpoint settings

Configure scaling, timeouts, and GPU selection.

Job states

Monitor job status and metrics.

Model caching

Reduce cold starts with cached models.

Endpoint types

	Queue-based	Load balancing
Processing	Requests queued and processed sequentially	Direct HTTP access to workers
Execution modes	Async (/run) or sync (/runsync)	Custom HTTP endpoints
Retries	Automatic retries on failure	No automatic retries
Handler required?	Yes	No (use any HTTP framework)
Best for	Batch jobs, guaranteed execution	Real-time apps, streaming

Learn more about load balancing endpoints.

Create an endpoint

Before creating an endpoint, ensure you have a handler function and Dockerfile.

Web

1. Navigate to the Serverless section and click New Endpoint.
2. Choose your deployment source:
   • Import Git Repository: See Deploy from GitHub
   • Import from Docker Registry: See Deploy from Docker Hub
   • Ready-to-Deploy Repos: Select a preconfigured endpoint
3. Configure your endpoint:
   • Endpoint Name and Type (Queue-based or Load balancer)
   • GPU Configuration and worker settings
   • Model (optional): Enter a Hugging Face URL for cached models
   • Environment Variables: See environment variables
4. Click Deploy Endpoint.

REST API

curl --request POST \
  --url https://rest.runpod.io/v1/endpoints \
  --header 'Authorization: Bearer RUNPOD_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "name": "my-endpoint",
    "templateId": "30zmvf89kd",
    "gpuTypeIds": ["NVIDIA GeForce RTX 4090"],
    "workersMin": 0,
    "workersMax": 3,
    "idleTimeout": 5
  }'

See the Endpoint API reference for all parameters.

Optimize cost and availability by specifying multiple GPU types in priority order. Runpod allocates your first choice if available, otherwise uses the next in your list.

After deployment, your endpoint displays a unique API URL: https://api.runpod.ai/v2/{endpoint_id}/

Edit an endpoint

1. Navigate to the Serverless section.
2. Click the three dots on your endpoint → Edit Endpoint.
3. Update endpoint settings and click Save Endpoint.

Changes to GPU types or worker counts may require restarting active workers.

Delete an endpoint

1. Navigate to the Serverless section.
2. Click the three dots on your endpoint → Delete Endpoint.
3. Type the endpoint name to confirm.

Deleting an endpoint permanently removes all configuration, logs, and job history.