Jupyter Notebook · Rate Limits

Jupyter Notebook Rate Limits

Project Jupyter does not operate a public hosted API and therefore does not impose first-party rate limits. The Jupyter Server REST and WebSocket APIs run on user-owned infrastructure; any rate limiting is configured by the operator (reverse proxy, JupyterHub, or hosting platform such as Binder / mybinder.org).

Jupyter Notebook Rate Limits is the machine-readable rate-limit profile for Jupyter Notebook on the APIs.io network, conforming to the API Commons Rate Limits specification.

It captures 2 rate-limit definitions, measuring varies.

The profile also includes 3 backoff/retry policies defined and response codes documented for throttled and serviceUnavailable.

Tagged areas include Data Science, Interactive Computing, Jupyter, Machine Learning, and Notebooks.

2 Limits Throttle: 429
Data ScienceInteractive ComputingJupyterMachine LearningNotebooksPythonRate Limiting

Limits

Self-hosted - operator-defined deployment
varies
not imposed by the Jupyter project; configured at the deployment / proxy layer
mybinder.org public service (community-operated) deployment
varies
see https://mybinder.readthedocs.io/ for fair-use limits on the public Binder federation

Policies

Self-Hosted Throttling
Operators typically apply rate limiting at the ingress (NGINX, Traefik, cloud load balancer) rather than inside the Jupyter Server itself.
JupyterHub Quotas
JupyterHub provides per-user concurrency and resource quotas via spawner configuration; these are deployment-defined.
Backoff Strategy
Clients should implement exponential backoff with jitter on 429 / 503 responses.

Sources