Overview - Runpod Documentation

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
REST API

Navigate to the Serverless section and click New Endpoint.
Choose your deployment path:
- Hello World: Runpod forks a starter worker template into a new GitHub repo in your account. Choose Queue-based or Load balancing, then click Deploy.
- Hugging Face LLM: Search for any text-generation model on Hugging Face (for example, type “Gemma” to find Gemma 4), select it, and click Create Endpoint. Runpod deploys a vLLM endpoint for you.
- Docker: Deploy from a container image. Select a saved Serverless template to fill in the container configuration automatically, or skip the template and enter an image name manually. See Deploy from Docker.
- GitHub: Select a repository, filtering by code owner if needed. Runpod checks for a Dockerfile and runs a background check on your handler: queue-based endpoints check for handler files, and load balancing endpoints check for a /ping path. See Deploy from GitHub.
- Hub: Opens the Hub browser, where you can browse and deploy prebuilt workers. This replaces the previous “Ready-to-Deploy Repos” option. See Hub overview.
- Flash: A guided setup flow for Flash that walks you through installing the SDK, initializing your project, and sending your first command. Steps complete automatically as you progress.
For the GitHub, Docker, and Hello World paths, configure your endpoint before deploying:
- Endpoint name and type (Queue-based or Load balancing)
- GPU configuration and worker scaling
- Model (optional): Enter a Hugging Face URL for cached models
- Environment variables and container configuration. See environment variables.
Click Deploy Endpoint.

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

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

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

Delete an endpoint

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

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

Send requests

Endpoint settings

Job states

Model caching

​Endpoint types

​Create an endpoint

​Edit an endpoint

​Delete an endpoint

Endpoint types

Create an endpoint

Edit an endpoint

Delete an endpoint