Quick answerCapacity fairness during contention means deciding, in the moment demand exceeds available throughput, how the shared pool gets allocated across customers who are all otherwise in good standing, rather than deciding in advance what response time you owe any single customer or how you stop free-tier abuse. The common approaches are proportional throttling based on a customer's normal baseline usage, a reserved floor per account so no one customer gets starved to zero, and a visible queue instead of a silent slowdown, chosen based on how contention actually shows up in your traffic.
A different problem from SLA commitments and abuse prevention
An internal platform team's SLA sets ongoing KPI targets the team is judged against. A regression fix-time commitment is about how fast a confirmed bug gets patched. Neither answers what should happen in the specific moment your shared inference capacity is fully booked and five customers are all trying to use the agent at once. This is also not the same problem as designing trial and freemium access without getting abused, which exists to stop bad-faith usage; fairness during contention assumes every customer drawing on the pool is legitimate and paying, and asks purely how to divide a finite resource among them in real time.
Why "first come, first served" quietly becomes unfair
The simplest allocation rule, whoever's request arrives first gets served first, sounds neutral but in practice rewards whichever customer happens to have the chattiest integration or the most aggressive retry logic, not the customer with the most legitimate need in that moment. A customer running a tight, well-behaved integration can end up starved behind one that fires requests in bursts, which is not a fairness outcome anyone actually intended.
Three allocation models worth choosing between deliberately
A proportional model throttles each customer relative to their own normal baseline usage, so a spike from customer A doesn't crowd out customer B's completely normal traffic. A reserved-floor model guarantees every account a minimum slice of capacity regardless of what else is happening, so no one customer can be pushed to zero even during a severe crunch. A visible-queue model, instead of silently slowing every response, tells the customer their request is queued and gives a rough wait estimate, trading a longer wait for honesty about what is happening. Most mature setups combine a reserved floor with proportional throttling above it, reserving the visible queue for extreme contention events.
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Make the allocation policy legible to account teams before a crunch happens
The worst time to design this policy is during an actual capacity crisis with a customer's account manager asking why their client is getting slow responses. Document the allocation rule in advance, in plain language an account team can repeat to a customer, the same way what KPIs belong in an internal AI platform team SLA turns an operational commitment into something concrete enough to actually reference under pressure.
Watch for the cost-control trap
It is tempting to treat contention purely as a cost problem and solve it by aggressively capping usage across the board, but that is a blunt instrument that punishes light users along with heavy ones. The better instinct, familiar from preventing runaway AI agent costs from token usage or infinite loops, is to isolate the actual source of the spike (one customer's traffic surge, a retry storm, a genuine platform-wide demand increase) before choosing which allocation lever to pull, rather than reaching for a global throttle as the default response.
Monitor the allocation outcome, not just the trigger
Track which accounts actually get throttled during contention events over time. If the same handful of customers are disproportionately absorbing the slowdown, your allocation rule has a hidden bias worth investigating, whether that's usage pattern, integration design, or account tier, before it becomes a retention problem you only discover from a churn conversation.
FAQ
Should enterprise accounts get a bigger reserved floor than self-serve accounts? That is a legitimate tiering decision, but make it an explicit, documented policy rather than an emergent side effect of whichever team escalates loudest during a crunch.
Does this require real-time infrastructure changes, or can it be handled at the application layer? Most of the allocation logic (proportional throttling, reserved floors, queue visibility) can live at the application layer in front of the model provider, without needing changes to the provider's own infrastructure.
How is this different from just buying more capacity? Buying more capacity delays the day contention happens again; it doesn't answer what happens fairly on the day it does, which is what this policy is actually for.