Akka · Rate Limits
Akka Rate Limits
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.
3 Limits
Throttle: 429
Actor ModelDistributed SystemsFrameworksJavaMicroservicesReactiveScalaRate Limiting
Limits
Self-Managed Throughput deployment
provisioned by per-core licensing - throughput scales with core count
Akka SDK and Akka Libraries scale linearly with provisioned cores. Backpressure is managed via Akka Streams and reactive-streams semantics in-application.
Akka Serverless Throughput account
scales with Akka hours consumed and platform capacity
Elastic scaling under Akka Serverless; consult Akka support for tenant-level throttle thresholds.
Akka in Your VPC Throughput account/region
provisioned per-region capacity
Throughput is dimensioned during contract negotiation alongside data isolation and compliance requirements.
Policies
Reactive Backpressure
Akka Streams uses Reactive Streams backpressure - downstream consumers signal demand to upstream publishers, replacing traditional rate-limit signaling at the HTTP layer.
Capacity Planning
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.
Backoff Strategy
Clients integrating with Akka HTTP services should implement exponential backoff with jitter and honor Retry-After headers if returned by user-implemented services.
Fair Use
Sustained usage materially exceeding contracted capacity may be subject to fair-use throttling under managed-service agreements.