Your vLLM Autoscaler Is Flapping Because You Picked the Wrong Signal — Not the Wrong Number

Part of a series on running vLLM on AKS. Companion piece: GPU sizing. Infrastructure setup — coming soon.

So... you've picked the right GPU for your use case, provisioned the cluster and node pool, deployed your model behind vLLM, installed KEDA and picked the request-queue as the autoscaling parameter as per the common recommendation: "Tune the request queue to obtain the preferred latency, and use batch size if you can't hit your preferred latency."

Then you watch it in production:

• A burst hits, a new pod spins up — and by the time it's ready, the burst is gone. The queue drained, so the autoscaler scales back down.
• Minutes later the next spike repeats the cycle. The replica count oscillates instead of settling.
• What gives??

That's flapping and it's costing you.

What flapping is, and why you should care

Flapping refers to a resource rapidly switching between two states and in this case: a pod Up/Running or Down/Failed.

I can hear you say: "So what ... The requests still get served, right?"

True, but flapping costs you:

1. Money — you pay for GPU nodes that spin up and tear down without ever serving traffic.
2. Latency — the mechanism meant to absorb load spends its time flapping instead. Requests sit in the queue while pods churn.
3. Trust — pods and nodes appearing and vanishing "at random" is exactly the chaos you don't want when debugging under pressure.

This article shows how to avoid flapping by using the right type of signal and why request-queue is the wrong choice.

We demonstrate this by walking through three progressive designs for the autoscaler and its defining parameter(s), reasoning about the output, and finally coming to a conclusion about a signal that actually holds steady, and why.

The three progressive steps we will walk through are:

1. Attempt 1 — arbitrary threshold (flaps, and over-scales)
2. Attempt 2 — calculated threshold (still flaps)
3. Attempt 3 — stabilized design (stable)

Setup

• Cloud: Azure AKS 1.35.6
• GPU node: Standard_NV36ads_A10_v5 (1× A10, 24 GB)
• Image / model: vllm/vllm-openai:latest / Qwen2.5-7B-Instruct-AWQ
• Autoscaler: KEDA ScaledObject, Prometheus trigger on vllm:num_requests_waiting
• Visibility: Prometheus + Grafana

- Constraint: nvidia.com/gpu: "1" → one pod per GPU node, so every replica needs a new node (this matters later)

Attempt 1 — an arbitrary threshold (flaps, and over-scales)

Pick a low, round queue number that sounds reasonable (5) and move on — sound familiar?

This over-scales badly because the underlying Kubernetes HPA controller — which KEDA delegates the actual scaling decision to — computes desiredReplicas = ceil(total_waiting / threshold) (Kubernetes HPA algorithm details). So a transient queue of 40 demands 8 replicas. Fine for a proof-of-concept, but in production each of those replicas cost you.

It's better to calculate the threshold from the hardware specs, not a guess.

So ... let's calculate a real one.

Here is the scaledobject for attempt1 if you are curious. scaledobject-attempt1-guess.yaml

Attempt 2 — a calculated threshold (still flaps)

Assumptions:

• Average tokens/request ≈ 1,000
• Average completion time = 10s (how long it takes vLLM to generate a response once processing starts — prefill + decode)
• Queue-wait budget W_max ≤ 5 seconds (SLO)

To calculate the threshold, we first need to determine how much cache is available to our model

Read from the model's startup log (the engine profiles this at boot): this is VRAM left after weights + activation + overhead.

Result: Available KV cache memory: 13.76 GiB

Next we determine the bytes per token based on the model's specifications (all from the model's config.json):

bytes_per_token = 2 (K and V) × layers × kv_heads × head_dim × dtype_bytes
                = 2 × 28 × 4 × 128 × 2 (fp16) = 57,344

Calculate how many tokens are available given the cache size and the bytes required per token
257,584 ≈ 13.76 GiB / 57,344 bytes_per_token

Determine how many requests our GPU can handle at once given our assumption(s) (~1,000 tok/req)
Concurrent_requests = 257,584 tokens / 1,000 tokens/request ≈ 258

(Full VRAM → token-budget derivation is in the GPU sizing companion piece.)

Finally, calculate the Threshold from an SLO (Little's Law — how many requests may queue before wait breaches W_max):

# Calculate how many requests will be completed every second
Drain_rate = Concurrent_requests / avg_completion_time = 258 / 10s ≈ 25.8 req/s

# Calculate the threshold to increase the pod number by 1.
Threshold = W_max × Drain_rate = 5s × 25.8 ≈ 129

129 is a defensible number, derived from measured capacity and a stated SLO. Not a guess.

However, for demonstration purposes, the rest of this article runs on a deliberately shrunk rig:

Demo vs. production numbers: --max-num-seqs 32 caps concurrency at 32 so the queue is reachable at demo scale. Measuring with unique per-request prompts gave a real drain rate of μ ≈ 0.46 req/s; with a W_max = 15s budget, the same formula calibrates to T = 15 × 0.46 ≈ 7, versus C ≈ 258 / T ≈ 129 for the full-size pod. Don't get caught up on the smaller numbers — same method, same core point; it's just easier to demonstrate the flapping behavior and saves me money. deployment.yaml

Apply the ScaledObject (scaledobject-attempt2-calculated.yaml):

triggers:
  - type: prometheus
    metadata:
      serverAddress: http://kps-kube-prometheus-stack-prometheus.monitoring.svc:9090
      query: sum(vllm:num_requests_waiting)
      threshold: "7"

Apply the load (load_test.py):

Let's walk through what happened:

1. Requests start arriving, running count climbs to 32 (one pod's cap) — no queue yet, all requests are actually running.
2. Once running is saturated at 32, new arrivals start queuing instead. The queue grows.
3. Queue crosses 7 (the threshold) a little before 20:00 → KEDA scales to 2 replicas.
4. While that second pod is still loading (model weights + readiness, ~90s), the queue keeps growing — the new pod isn't serving yet, so it does nothing to relieve the backlog. By the time it's ready, the queue is way past 7 (peak ~50).
5. KEDA polls again, sees the queue still far over threshold, and scales to 3 pods.
6. Now with more capacity coming online, the queue starts draining. It falls below threshold, and KEDA scales back down (2, then 1) — the queue graph and replica graph fall together around 20:04–20:07.
7. The load never stopped: it's the same continuous arrival rate (0.6 req/s) throughout. The moment replicas dropped to 1 again, that single pod was immediately outmatched by the same steady arrivals, so the queue re-formed and crossed 7 again almost immediately (~20:08–20:10) — a second flap cycle.
8. Eventually the load generator's 12-minute run ends, arrivals stop, the queue drains for real, and replicas settle back to the floor of 1.

Under steady load, the oscillation isn't coming from the traffic — it's coming from the shape of the signal.

The queue reads ~0 until the pod is full, then climbs; the autoscaler is just asking "is this threshold met or not?"

The binary nature of the signal is what causes the flap.

Why you might not have noticed this in the wild: Kubernetes' HPA defaults to a 300s scale-down stabilization window (0s scale-up), so out of the box the flap is slowed to a ~5-minute cycle — infrequent enough to miss at a glance, but you pay for it on every swing. (Kubernetes HPA defaults)
For this demo, stabilizationWindowSeconds was set to 0 in Attempt 2, to expose the flap within a short window instead of stretching it to 5+ minutes:

advanced:
  horizontalPodAutoscalerConfig:
    behavior:
      scaleDown:
        stabilizationWindowSeconds: 0

So even picking the "right" queue-depth number doesn't save you.
Is there a different metric we can use instead??

Now you're asking the right questions.

---

Attempt 3 — the stabilized design (stable)

Two requirements to stabilize the system:

1. Scale on a signal that moves before trouble, and
2. Damp scale-down so a dip can't tear down capacity you just paid for.

First, let's look at some metrics and rank vLLM's metrics by what they tell you and when:

Signal	Shape	Tells you
num_requests_waiting	0 until full, then climbs	you're already overloaded (lagging)
num_requests_running	proportional, flat once at max_num_seqs	how busy, up to the cap
kv_cache_usage_perc	smooth 0–1 (fraction)	how close to full — before the queue forms (leading)

KV-cache utilization is the right signal for vLLM because it rises before requests queue, and it is the binding resource the capacity math is about.
Scaling up at ~80% buys you lead time — you add capacity while the current pod still has headroom, INSTEAD OF AFTER it's drowning.

triggers:
  - type: prometheus
    metadata:
      serverAddress: http://kps-kube-prometheus-stack-prometheus.monitoring.svc:9090
      query: sum(vllm:kv_cache_usage_perc)   # V1 engine name; older builds use gpu_cache_usage_perc
      threshold: "0.80"

IMPORTANT: Use sum, not avg — this is the part that actually kills the flap. sum(kv_cache_usage_perc) measures total demand across pods (in "pod-fulls"), which doesn't change when you add a replica — so ceil(sum / 0.80) has a stable fixed point. An avg falls as you add pods, drops back under the threshold, and reintroduces the exact oscillation you're trying to kill. That's the deeper rule behind this whole article: a good autoscaling signal is one whose total is invariant to replica count. num_requests_waiting fails it (collapses to 0 once you have enough capacity); sum(kv_cache_usage_perc) passes it.

OK, so how do we dampen the scale-down?

Pair the signal with scale-down damping ≥ your measured lead time L, so a momentary dip never tears down capacity you just paid minutes to bring up:

advanced:
  horizontalPodAutoscalerConfig:
    behavior:
      scaleDown:
        stabilizationWindowSeconds: 120   # >= L (node + engine init + readiness)

Stable ScaledObject scaledobject-attempt3-stabilized.yaml:

Apply the load again (load_test.py):

We now have a stable scale-up for processing.
It works without trying to find the exact right threshold from the request queue!
We don't even need to walk through the different points — the autoscaler handles it exactly like we want it to because we chose the right signal!

Oh, big deal... "the docs already say to add a cache trigger"

They do — and that's the trap. Some (e.g. this guide) suggest keeping the queue-depth trigger and adding a second cache trigger, assuming the two combine into "scale only if both agree."

They don't, by default. KEDA evaluates each trigger independently and takes the max of their desired replica counts — that's OR logic, not AND. So the lagging, bistable queue-depth trigger can still drive scale-up and scale-down on its own, and the flap comes right back. (KEDA docs)

If you want the leading signal to govern scaling, use it alone — drop the queue-depth trigger. If you genuinely need both conditions, don't just list two triggers and hope: that silently keeps the OR/max default. AND-style logic is possible, but only if you explicitly use the opt-in scalingModifiers.formula to combine the named triggers yourself. Adding a plain second trigger feels safer and quietly isn't.

Summary

Flapping is a signal-choice bug wearing a threshold costume. In order of impact:

1. Scale on a leading, proportional signal (kv_cache_usage_perc), not a lagging saturation one (num_requests_waiting).
2. Query it as a sum, not an average — sum(vllm:kv_cache_usage_perc) measures total demand across pods, which doesn't change when you add a replica. Additional Suggestions:
3. Use scalingModifiers if you need to combine metrics. Example here: scaledobject-scalingmodifiers.yaml.
4. Size scale-down damping to your actual lead time.

The final working scaledObject used for Attempt 3 can be seen here — trigger, threshold, and damping in one place.

You will likely still want to calculate your threshold, but — it's the last decision, not the first.
Get the signal right and most of the flapping is gone before you tune anything.

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Alternatives

Keep pods warm

You may not need the perfect signal. The cheapest lever against the timescale mismatch is over-provisioning — set minReplicaCount > 1 so warm replicas absorb bursts without waiting minutes for a cold node.

What it fixes: the lead-time problem directly, and it makes any residual flapping cheaper (no cold starts) and rarer (you scale from a higher floor). What it doesn't fix: the marginal 2→3 decision still rides whatever signal you chose. And it costs money — idle GPUs are the expensive kind.

For a small deployment, warm headroom + a reasonable threshold is often good enough — the leading-signal work is where it pays off at scale or when you're cost-sensitive.

More pods per node

If one-pod-per-GPU-node causes node churn, why not pack several pods per node? A couple of reasons this doesn't help:

1. Flapping is a control-loop stability problem — pack three pods per GPU and the replica count still oscillates, just faster.
2. Each pod gets a fraction of the 24 GB, so a fraction of the KV cache and a lower concurrent-request capacity (C).

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What changes at real scale

Everything above is one GPU — basically a teaching rig. Once you're running a real fleet, the strategy actually flips.

Instead of reacting fast to every queue blip, you want to over-provision warm headroom and scale infrequently. If possible, use predictive scaling — forecasting on daily/weekly patterns, not waiting on any live metric — for the traffic you can actually see coming, and let reactive scaling handle only the residual. Even that reactive layer should be the leading-signal kind from Attempt 3, not lagging queue-depth.

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What's next: KV-cache-aware routing

That's a topic for another article, but worth knowing it exists: at fleet scale, how you route requests across pods (e.g. KV-cache-aware routing) becomes another lever alongside scaling. Not a replacement for getting the scaling signal right — just the next layer once you have more than a few pods.

• vLLM Production Stack — prefix-aware routing
• Red Hat / llm-d — KV cache aware routing