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: Akka
providerId: akka
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: false
tags:
  - Actor Model
  - Distributed Systems
  - Frameworks
  - Java
  - Microservices
  - Reactive
  - Scala
  - Rate Limiting
description: Akka does not publish a centralized public rate-limit policy for its libraries, SDK, or Serverless
  platform. Throughput characteristics are tied to provisioned cores (self-managed) or Akka hours / per-region
  capacity (managed). Application-level back-pressure is handled by Akka Streams' reactive-streams backpressure
  protocol rather than HTTP rate limits. Confirm specific service throttling with the Akka support team
  for managed deployments.
sources:
  - https://akka.io/pricing
  - https://doc.akka.io/
notes: No public rate-limit documentation - limits are provisioned via core/region/Akka-hour purchases
  and operationally enforced by the platform.
responseCodes:
  throttled: 429
  serviceUnavailable: 503
limits:
  - name: Self-Managed Throughput
    scope: deployment
    metric: varies
    limit: provisioned by per-core licensing - throughput scales with core count
    notes: Akka SDK and Akka Libraries scale linearly with provisioned cores. Backpressure is managed
      via Akka Streams and reactive-streams semantics in-application.
  - name: Akka Serverless Throughput
    scope: account
    metric: varies
    limit: scales with Akka hours consumed and platform capacity
    notes: Elastic scaling under Akka Serverless; consult Akka support for tenant-level throttle thresholds.
  - name: Akka in Your VPC Throughput
    scope: account/region
    metric: varies
    limit: provisioned per-region capacity
    notes: Throughput is dimensioned during contract negotiation alongside data isolation and compliance
      requirements.
policies:
  - name: Reactive Backpressure
    description: Akka Streams uses Reactive Streams backpressure - downstream consumers signal demand to
      upstream publishers, replacing traditional rate-limit signaling at the HTTP layer.
  - name: Capacity Planning
    description: For self-managed deployments, throughput is provisioned by core count; for managed services,
      by Akka hours or region capacity. Customers right-size with Akka technical account managers.
  - name: Backoff Strategy
    description: Clients integrating with Akka HTTP services should implement exponential backoff with
      jitter and honor Retry-After headers if returned by user-implemented services.
  - name: Fair Use
    description: Sustained usage materially exceeding contracted capacity may be subject to fair-use throttling
      under managed-service agreements.
maintainers:
  - FN: Kin Lane
    email: kin@apievangelist.com