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KEDA change simulator
Know what your autoscaler will do before production does.
Replay real traffic against new KEDA targets, replica limits, and cooldowns. See the cost and reliability impact before changing a single workload.
✓ Read-only✓ No cluster agent✓ Evidence-first AI
Average replicas11.2 → 4.6
−59%Recommendation
Target 300 · min 4 · max 12 covers every observed peak.
Estimated opportunity$434/mo
if node capacity is released
Historical replayCapacity modelingReliability guardrailsExplainable recommendations
Interactive workspace
Replay your scaling policy
Planning model · no changes applied
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at $0.09 per pod-hour
Replica timeline
Current policy compared with proposed policy
Peak traffic—
CurrentProposed
Reliability checks
Guardrails derived from this replay
Recommended changeMedium-high confidence
Target300req/min/pod
→Replica range4–12min · max
This policy reduces steady-state capacity while retaining room for observed peaks.
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Planning model: replay approximates KEDA/HPA decisions from the supplied metric, limits, and cooldown. Validate application capacity and latency with a load test before production rollout.
Three inputs. One defensible decision.
ScaleReplay keeps calculation and explanation separate, so recommendations can be reviewed and challenged.
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Import the policy
Paste a KEDA ScaledObject or enter the target, minimum, maximum, and cooldown.
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Replay real demand
Upload timestamped metrics. The simulator applies both policies to every interval.
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Explain the tradeoff
The assistant converts deterministic results into risks, recommendations, and rollout language.
Simple pricing
Simulate free. Ship with confidence on Pro.
Use the deterministic simulator without an account. Upgrade when you need a review-ready engineering report and billing controls.
Free
$0 forever
- KEDA and HPA replay
- CSV and YAML import
- Reliability guardrails
- Basic Markdown summary
Pro
$49 / month
- Everything in Free
- Executive engineering report
- Rollout and rollback plan
- Recommended KEDA YAML
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Ship safer scaling changes
Stop choosing replica limits by instinct.
Replay the policy, challenge the assumptions, and take an evidence-backed change to review.