specification: API Commons Rate Limits
specificationVersion: '0.1'
schema: https://raw.githubusercontent.com/api-evangelist/interface-research/main/schema/api-commons.yml#/$defs/RateLimits
provider: Google TensorFlow
providerId: google-tensorflow
created: '2026-05-04'
# Provenance stamped 2026-08-11: this artifact was written by the API Evangelist
# bulk sweep dated 2026-05-04, not harvested from the provider. See roadmap#35.
method: generated
modified: '2026-05-05'
reconciled: true
tags:
  - Rate Limiting
  - AI
  - Machine Learning
  - Open Source
description: TensorFlow is open-source software with no centrally enforced rate limits. TensorFlow Serving
  runs inside the consumer's own infrastructure, so request throughput is governed by the deployer's
  hardware (CPU/GPU/TPU), TensorFlow Serving server flags (max batch size, num_concurrent_requests),
  and any front-door API gateway. TensorFlow Hub / Kaggle Models is a free public mirror with fair-use
  download limits set by the host platform.
sources:
  - https://www.tensorflow.org/tfx/serving/api_rest
  - https://github.com/tensorflow/serving
responseCodes:
  throttled: 429
  serviceUnavailable: 503
limits:
  - name: TensorFlow Serving — operator-defined
    scope: deployment
    metric: varies
    limit: 'self-hosted; governed by serving binary flags and underlying hardware'
    notes: Throughput depends on `--max_num_load_retries`, `--num_load_threads`, batching parameters,
      and GPU/TPU availability in the deployer's environment.
  - name: TensorFlow Hub / Kaggle Models downloads
    scope: IP
    metric: varies
    limit: 'fair-use download policy enforced by the model host'
    notes: No documented numeric quota; very high concurrent download volume may be throttled.
policies:
  - name: Self-managed throttling
    description: Because TensorFlow Serving is self-hosted, deployers should put their own rate-limit
      / quota / circuit-breaker layer (e.g. an API gateway or service mesh) in front of the inference
      endpoint.
  - name: Batching for throughput
    description: Use TensorFlow Serving batching configuration (max_batch_size, batch_timeout_micros)
      to amortize request overhead and raise effective requests-per-second.
  - name: Open-source community support
    description: Performance / scaling issues are addressed through GitHub issues and the TensorFlow
      Forum; there is no vendor SLA or paid support tier from the TensorFlow project.
maintainers:
  - FN: Kin Lane
    email: kin@apievangelist.com