Use Case

Real-Time Scoring — sub-100ms SLO for fraud and risk models

Fraud detection and credit risk scoring run on per-transaction decision paths. Any latency spike above your SLO means either failing the transaction or accepting it blind — both are expensive outcomes.

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The problem: load balancers don't scale fast enough to protect SLOs

Real-time scoring workloads are often event-driven — a payment processor, a checkout flow, an onboarding step. They're not bursty in a diurnal sense; they spike when your upstream product does. That could be a marketing email that goes live at 10am, or a batch of ACH settlement events hitting at 3am.

By the time a load balancer or RPS-based auto-scaler detects the spike and adds replicas, 60–90 seconds of requests have already queued behind an under-provisioned model pool. For a sub-100ms SLO, 90 seconds of queue depth means thousands of SLO breaches before the first new replica is warm.

fraud-scorer.log — 03:17:08

ALERT  model=fraud-v7  p99_ms=247  slo_budget_ms=80  SLO_BREACH
WARN   queue_depth=1840  active_replicas=3  scaling_eta=75s
INFO   event_source=ach_batch  arrival_rate=2300rps
ALERT  decision_timeout_rate=12%  (03:17–03:18)

How MLSrvyn protects scoring SLOs at the scaling layer

MLSrvyn classifies real-time scoring endpoints as Event-driven archetype. The profiler identifies the irregular-interval, high-amplitude burst pattern — distinct from diurnal or steady workloads — and sets a proactive scale budget.

Rather than waiting for RPS thresholds, MLSrvyn monitors the p99 latency histogram in real time and begins scaling when the p99 crosses a configurable fraction of your SLO budget — well before queue depth grows. Replicas are added in parallel batches to cut the warm-up window from 90s to under 30s.

mlsrvyn.yaml — fraud-v7 policy

model: fraud-v7
archetype: event_driven
slo:
  p99_ms: 80
  p50_ms: 22
scaling:
  min_replicas: 3
  max_replicas: 20
  scale_up_slo_pct: 60  # scale at 60% of p99 budget
  scale_batch_size: 3    # add 3 replicas at once

247ms → 34ms

p99 during ACH batch events (SLO: 80ms)

Decision timeout rate during irregular traffic spikes

29s

Scale-up response time (down from 75s)

Protect your scoring SLOs under any traffic pattern

Request access and we'll profile your fraud and risk models against their historical traffic events to determine the right scaling archetype and SLO guard.

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