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Reference

Quotas

Operational limits, enforcement, and design strategies

Quotas define the operational boundaries of your Organization, Projects, and components. They provide predictability, fairness, and safety.

Quota Categories

Execution-Based (Organization)

DimensionDescriptionWhen Exceeded
Total ExecutionsRuns per rolling windowNew runs rejected until reset
Concurrent ExecutionsMax simultaneous runsRuns queue or fail fast
Max DurationUpper bound per runRun terminated at limit
Flow DepthSequential node stepsValidation error before execution
Recursion DepthNested calls (Flows/Agents)Triggering call blocked

Resource-Based

DimensionDescriptionWhen Exceeded
Payload SizeMax input/output sizeValidation error or truncation
Script SizeAction.py footprintBuild fails
Stored PayloadsSaved examples per NodeCannot save until pruned
Module SizePackage size & dependenciesPublish fails

API & Integrations

DimensionDescriptionWhen Exceeded
Request RateRequests per second/minuteHTTP 429, retry with backoff
Endpoint ConcurrencyIn-flight requests per endpointRequests queue or fail
Schedule FrequencyMinimum interval between triggersSkipped or delayed ticks
Build/Publish RateOperations per windowPublishes deferred/rejected

Account-Specific

DimensionDescriptionWhen Exceeded
EnvironmentsNamed environments per OrgCannot create more
Variables/SecretsEntries per environmentNew entries rejected
Alerts/EvaluationsRules per ProjectCannot add until archived

How Limits Are Enforced

  • Hard limits: Requests rejected (HTTP 429 or validation error)
  • Soft limits: Warnings issued, usage reduction encouraged
  • Windows: Rolling counters reset after defined time periods

Monitoring Your Usage

  • Review execution history and metrics for run counts and durations
  • Check project surfaces (APIs/Schedules) for request rates
  • Create a dashboard Flow to periodically snapshot usage

When You Hit a Limit

  1. Backoff and retry — Use exponential backoff for rate-limit errors
  2. Reduce payload sizes — Split work into smaller batches
  3. Parallelize smaller items — Fan-out with smaller payloads
  4. Upgrade your plan — If sustained traffic requires more capacity

Design Strategies

Batch & Bound — Break large inputs into lists; toggle Parallel on consumer Nodes

Short-circuit — Validate inputs early and exit fast on no-op paths

Compose shallow — Use sub-Flows for reuse but keep run paths short

Time-box — Use conservative duration expectations; handle partial progress

Design for headroom — Target 70-80% of limits under peak load

Good Hygiene

  • Review Variables quarterly; remove dead flags and stale endpoints
  • Keep secrets in secure storage; reference them, don't duplicate
  • Tag Projects and Nodes with owners for quota notifications
  • Use consistent naming (SERVICE__ENV__KEY) for Variables

warning: Binary dependencies — Only pure-Python packages are supported. Packages requiring binary installs are not available.

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