Policy design patterns
This page provides practical policy composition patterns using the current gate model.
Pattern 1: Free tier baseline
Goal:
- broad access with strict quota and rate limits
Shape:
- wildcard feature baseline for conservative rate/quota
- explicit feature policies for product-critical exceptions
Use when:
- onboarding or trial plans with low risk tolerance
Pattern 2: Paid tier with burst tolerance
Goal:
- stable monthly quota with short-window burst control
Shape:
- quota policy with longer window
- rate policy with short window on same feature
Use when:
- you want predictable spend plus abuse protection
Pattern 3: Seat-gated collaboration feature
Goal:
- cap concurrent active identities for a feature
Shape:
- seats policy (
unit=seat) - optional feature-level quota/rate policies for usage inside each seat
Use when:
- entitlement alone is not enough and active seat cardinality matters
Pattern 4: Add-on controlled uplift
Goal:
- enable higher limits only when add-on is purchased
Shape:
- base bundle with conservative policies
- add-on plan/bundle path with higher-priority selection
Use when:
- packaging needs commercial upsell without meter price changes
Pattern 5: Enterprise negotiated behavior
Goal:
- preserve shared meter pricing baseline, customize runtime guardrails
Shape:
- enterprise-focused bundle variants
- optional contract terms for pricing/e2r parameters
Use when:
- sales-led accounts need controlled exceptions
Anti-patterns to avoid
- relying only on wildcard policies for all features
- in-place mutation of active high-traffic bundles without staged rollout
- using seats as a substitute for entitlement design
- coupling product UX to checkout redirect success instead of runtime decision results
Design checklist
- Is each feature's primary business risk covered by at least one explicit policy?
- Are rate and quota windows aligned to product UX expectations?
- Is seat policy used only where identity cardinality is a true requirement?
- Can support explain deny/quarantine outcomes using bundle + policy + hints?
Next
- Data model constraints: Policy model
- Runtime decision flow: Policy evaluation