Quota policy
AI API Quota Policy Examples for SaaS Teams
Practical quota policy examples for SaaS teams managing customer API keys, model tiers, prepaid balances, abuse limits, and overage behavior in OpenAI-compatible gateways.
Why quota policies need more than one number
A single monthly token cap is easy to explain but weak in production. SaaS teams usually need several controls at the same time: a plan allowance, a per-minute abuse limit, model-tier restrictions, prepaid balance checks, and emergency stop rules for runaway jobs. If those policies live only in application code, every new product surface becomes another place to make billing mistakes.
An OpenAI-compatible gateway can enforce quotas before provider spend happens, while still recording the customer, API key, model, route, and reason for every allow, throttle, downgrade, or block decision.
Example quota policy matrix
| Policy | Example rule | Gateway behavior |
|---|---|---|
| Free trial allowance | 100k input tokens and 25k output tokens per workspace | Allow low-cost models only; block frontier models unless an admin upgrades the tenant. |
| Plan monthly cap | Starter plan gets $50 provider-cost equivalent per billing period | Track remaining allowance by tenant and return a billing-friendly limit response when exhausted. |
| Per-key burst limit | 60 requests per minute for a browser-issued customer API key | Throttle the key without blocking the whole tenant, and expose the key id in support logs. |
| Expensive model approval | Claude/OpenAI premium routes require enterprise flag or prepaid balance above $100 | Downgrade to an approved cheaper model or return a clear upgrade-required response. |
| Runaway job circuit breaker | Stop any feature after it burns 3x its normal hourly budget | Temporarily disable the route, alert engineering, and keep other features available. |
| Prepaid balance floor | Block new requests when balance falls below the estimated next-call cost | Estimate cost before routing; reserve balance, settle actual usage, then release unused reservation. |
Recommended evaluation order
- Authenticate the customer API key: identify tenant, workspace, key owner, allowed features, and revocation state.
- Classify the request: resolve model alias, feature, expected route, prompt size, and whether streaming or tools increase cost risk.
- Check hard blocks: disabled tenant, revoked key, unsupported region, missing prepaid balance, or compliance hold.
- Apply plan and model rules: allowed model tiers, monthly allowances, per-feature caps, and enterprise exceptions.
- Apply burst and abuse limits: per-key, per-tenant, per-IP, and per-feature request limits.
- Reserve estimated cost: hold enough balance for the expected provider call before routing traffic.
- Settle actual usage: update the usage ledger after the provider response, including retries, fallback, cached tokens, and refunds.
Response patterns that reduce support load
| Situation | Bad response | Better response |
|---|---|---|
| Monthly plan cap reached | 429 Too Many Requests | 402 plan_allowance_exhausted with tenant id, reset time, and upgrade/balance action. |
| Per-minute burst exceeded | Generic provider error | 429 key_rate_limited with retry-after and the customer API key id. |
| Model not available on plan | Silent downgrade | Either explicit downgrade metadata or 403 model_not_allowed, depending on the product contract. |
| Balance too low | Failed after provider call | Preflight block before provider spend, with required minimum balance estimate. |
Metrics to review weekly
- Top tenants by blocked cost, allowed cost, and downgraded model usage.
- API keys with high burst throttling but low successful completion rate.
- Features that trigger circuit breakers or fallback routes repeatedly.
- Prepaid balance reservations that remain unsettled too long.
- Plan caps that are frequently hit before customers get business value.
How FerryAPI fits
FerryAPI is designed around the gateway controls that SaaS teams need for AI monetization: customer API keys, quota rules, prepaid balances, model routing, and usage records that finance can reconcile. The goal is to make AI API access cheaper without losing the controls required to run it as a customer-facing product.
Related FerryAPI guides: tenant-level AI budget guardrails, LLM prepaid balance implementation, AI API usage attribution schema, and multi-provider invoice reconciliation.
Related: AI API cost anomaly detection runbook shows how to turn quota and prepaid balance policies into practical incident response alerts.
Quota checks should run before every retry and fallback attempt; pair this matrix with a retry budget policy so reliability work cannot silently bypass tenant budgets.
FerryAPI helps SaaS teams issue customer API keys, enforce quotas, route models, and track billable usage through an OpenAI-compatible gateway. Explore FerryAPI.
For developer-facing responses, pair these rules with AI API rate limit headers so SDKs can distinguish short-window throttles from budget and model-tier blocks.