Launch-Day Burst Traffic Is Where AI Features Die

9:18am, and the feature is already dead

The launch went out at 9am: the email blast, the homepage banner, the founder's post, the coordinated push a quarter of work had built toward. By 9:18 the AI feature was returning errors to most of the people trying it for the first time. By 9:40 the thread on social was no longer about the feature, it was about the feature being broken.

Nothing had failed in the ordinary sense. The servers were up, the provider was healthy, the code that passed every staging test ran exactly as written. What failed was the assumption underneath the launch: that traffic would arrive at the average rate the capacity plan was built on. It did not. It arrived in a wall, in the first half hour, at roughly ten times the steady-state estimate, and the feature died on the one day the entire go-to-market motion was aimed at.

Launch traffic is a spike, not a ramp

Organic growth is a ramp: traffic builds over weeks, the autoscaler keeps up, the plan tracks reality with comfortable margin. Everything you learned about scaling came from ramps, and ramps are forgiving.

A coordinated launch is the opposite shape. You spend real money to make as many people as possible try the thing at the same moment. The email lands, the announcement drops, the embargo lifts, and demand arrives as a near-vertical spike, a large fraction of your entire audience in the first thirty to sixty minutes, then a long decay. The peak-to-average ratio can exceed 10x, and the peak lands at the very beginning, before anything warms up or scales out. The thing you optimized your GTM spend to maximize, simultaneity, is precisely the thing that breaks your infrastructure.

This is not a fringe risk. The most-watched AI products keep demonstrating it at the largest scale: launch and viral spikes that overwhelm capacity and produce rate-limited, degraded, or unavailable service in the opening hours are a recurring feature of the category, visible on provider status histories whenever a major release or surge hits, OpenAI's own CEO having publicly described GPUs "melting" under demand and rate limits imposed as a result (OpenAI status, Anthropic status). If the companies with the most inference capacity on earth get squeezed by launch spikes, the assumption that yours will glide through on an average-rate plan is optimism, not engineering.

Why AI features fail harder at the spike

A spike hurts any service. It hurts AI features more, for reasons specific to the stack, and they compound.

Provider rate limits are a hard ceiling you do not control. Your account has a tokens-per-minute and requests-per-minute cap with the provider. A normal day stays comfortably under it; a 10x launch spike blows straight through, and the provider returns 429s, not because you did anything wrong, but because you asked for more than your tier allows in that minute. You cannot autoscale out of this; adding your own servers does nothing about the provider's ceiling, which is upstream of everything you control.

Capacity is denominated in tokens, and the spike carries the tail. As we argued in Capacity Planning for AI, LLM capacity is bound by token throughput and KV-cache memory, not request count. A launch spike brings the full distribution at once, including the heavy tail of long-output requests, arriving together. Decode slots and KV-cache memory fill, throughput craters, and the system browns out at a request rate the plan thought was safe.

Cold caches, cold autoscalers. The spike arrives when every cache is cold (nothing requested yet) and every autoscaler is at its floor (no load yet to scale on). The worst traffic of the launch meets the least-warm system of the launch. Prompt cache hit rates are at their floor exactly when volume is at its ceiling, the collapse we detail in Your Prompt Cache Hit Rate Collapses Exactly When You Need It, so effective cost and latency are both at their worst in the opening minutes.

Autoscaling is too slow for the leading edge. Even a fast autoscaler takes minutes to detect load, schedule capacity, and warm it. A launch spike does its damage in the first ten. By the time new capacity is ready, the impression is made and the social thread has turned. Reactive scaling cannot catch a spike whose entire destructive payload lands before the scaler finishes its first reaction.

Spike testing is not soak testing, and you need both

Most teams that load test at all do soak testing: sustained load over time, to find leaks and degradation. Valuable, and it will not save your launch, because a launch is not sustained load. It is a spike: a near-instant jump from baseline to many times peak, then decay. The load-testing literature treats these as distinct test types for exactly this reason (Grafana k6 on load-test types).

They probe different failures. Soak testing finds slow problems: memory leaks, connection-pool exhaustion, gradual latency creep, the cache slowly filling. Spike testing finds fast problems: rate-limit tripping, cold-start storms, queue saturation, autoscaler lag, thundering-herd retries. A system can pass soak testing flawlessly and die in the first minute of a spike, because the spike triggers a class of failure sustained load never reaches. Launch day is a spike test run in production with your reputation as the assertion. Better to run it in staging first.

The fix: pre-provision, shed load, and queue

Pre-provision for the launch; do not rely on autoscaling. For a known event, scale up before the spike, manually, and hold. Treat launch capacity as a scheduled commitment. Provision for your spike estimate, then double it, launch demand is the hardest traffic to predict and the most expensive to underestimate, and idle capacity for a few hours is trivial against a dead launch.

Raise provider limits in advance. Your account's TPM and RPM ceiling is the limit you cannot autoscale past, so it must be raised before launch. Both major providers let you request higher rate limits or commit to provisioned throughput for known events; the lead time is real, often days, so this belongs on the launch checklist a week out, not as a fix on launch morning. This single item prevents the most common and most embarrassing launch failure: the upstream 429 you did nothing to deserve.

Queue, and tame the retries. A bounded queue with backpressure converts a brownout into a wait, which is survivable. But guard against the thundering herd: when first-time users get an error they hammer retry, multiplying the spike at its worst moment. Use exponential backoff with jitter on the client, idempotency keys so retries do not double-spend tokens, and a circuit breaker so a struggling backend is not buried under retries it cannot answer. The retry storm is a self-inflicted second spike on top of the first; suppress it deliberately.

Rehearse the spike before the market runs it for you

The launch spike is the most predictable unpredictable event you will face. You know it is coming, you scheduled it. You roughly know the audience size, you bought the list. You know the shape, near-vertical rise, long decay. There is no excuse for meeting it untested, yet the overwhelming majority of AI features meet launch day having only ever been tested at average rate on the happy path.

The rehearsal is a spike test that reproduces launch shape: baseline to 10x in under a minute, sustained through the peak window, then decay, using the real token distribution with its long-output tail, not a fixed convenient prompt. The assertions are specific and ruthless. Does the feature stay available, even degraded? Does it shed load gracefully or error hard? Do provider rate limits trip, and if so does the fallback engage? Does the queue hold and drain, or unbound? Does the retry logic damp the herd or amplify it? Does cost per conversation stay sane when the cache is cold and volume is peak, the way we frame it in Cost Per Conversation Under Load, or does the opening hour quietly run up a five-figure bill?

# Spike profile: baseline → 10x in < 60s, hold, decay
async def launch_spike(endpoint, baseline_rps=20, peak_mult=10):
    await ramp(endpoint, frm=baseline_rps, to=baseline_rps*peak_mult, over_s=45)
    metrics = await hold(endpoint, rps=baseline_rps*peak_mult, for_s=600,
                         output_dist="production")   # include the long-output tail
    assert metrics.availability   > 0.99, "hard-failed under spike"
    assert metrics.provider_429s == 0 or metrics.fallback_engaged, "no 429 fallback"
    assert metrics.queue_bounded,            "queue grew without backpressure"
    await decay(endpoint, frm=baseline_rps*peak_mult, to=baseline_rps, over_s=300)

Run it against staging at production scale, then run a smaller version against production itself in the quiet hours before launch, staging never perfectly mirrors prod, and the gap is exactly where launch failures hide. Each failure the rehearsal surfaces is one that does not happen live, in front of the audience your GTM spend assembled, on the one day the impression is permanent.

The one day it has to work

Most days, a degraded AI feature is a shrug, the user retries tomorrow and the average works itself out. Launch day is not most days. It is the day you spent the quarter and the budget to make the maximum number of people try the thing for the first time, simultaneously, which is to say, the day you engineered the exact traffic shape most likely to break it, aimed at the audience least likely to forgive it. Pre-provision past your estimate. Raise the provider limit a week out. Degrade gracefully, queue with backpressure, tame the retries. And rehearse the spike in staging until it is boring, because the alternative is rehearsing it live, in front of everyone, on the one day it absolutely had to work.