One Control Plane. Three Outcomes.
SLOpilot tracks service behavior, simulates infrastructure changes, and selects the lowest-cost action that maintains your SLOs. The same capabilities serve different priorities depending on your role.
Keep Every Service on Its SLO — at Minimal Cloud Cost
SLOpilot delivers cost optimization in two stages: a 14-day observation that produces rightsizing recommendations, compliance baselines, and predictive models — then continuous SLO-governed control of HPA, VPA, and Karpenter at minimal cost.
The Problem
Autoscalers optimize in isolation. VPA right-sizes pods. HPA scales replicas. Karpenter provisions nodes. None of them see the others' impact on your SLOs. The result: over-provisioning 'just in case' or under-provisioning that triggers SLO violations.
Observation Mode
SLOpilot connects read-only and analyzes workload behavior for 14 days — producing rightsizing recommendations, compliance baselines, and the predictive models that power the What-If Engine. Zero production changes.
Infrastructure Rightsizing
Confidence-rated CPU and memory recommendations appear as usage data accumulates — typically within the first one to two weeks. Each recommendation passes a data coverage gate and an estimate stability check.
SLO-Governed Automation
Continuously steers CPU, memory, and replicas to maintain YOUR declared SLOs. Cost-efficiency determines which alignment-achieving action is selected.
Test Every Infrastructure Change Before It Hits Production
SLOpilot's What-If Engine simulates resource changes, traffic surges, and SLO boundary conditions — so you know the impact before you commit. Every scenario logged with confidence intervals that quantify prediction uncertainty.
The Problem
Infrastructure changes are a leap of faith. Will this resource reduction violate SLOs? Will this traffic spike breach our latency target? Today you find out in production. With What-If, you find out first.
Resource Scenarios
"What if we reduce CPU limits by 20% on namespace X?" — see projected SLO impact before committing.
Traffic Scenarios
"What if traffic doubles during Black Friday?" — validate capacity under stress.
SLO Scenarios
"What if we tighten our P99 latency target from 200ms to 150ms?" — see cost implications before changing the target.
Every Decision Already Documented
The same control loop that tracks SLOs, simulates impact, and selects actions produces a continuous governance record — audit-native evidence as an inherent output, not an afterthought. Decision records include both infrastructure rightsizing justifications — what usage data supported the recommendation and which quality gates it passed — and scenario planning validations that log what simulation confirmed the safety of the change.
The Inherent Property
Traditional compliance requires separate tooling, manual evidence collection, and periodic audits. Teams spend weeks preparing documentation that is already outdated by the time it is reviewed.
Continuous Evidence
Compliance against your declared SLOs tracked continuously, not sampled quarterly. Trajectory toward degradation detected before it becomes a reportable incident.
Scenario Validation
The What-If Engine produces audit-ready resilience test results — logged with full reasoning chains. Not just annual tests.
Decision Reasoning Chains
Every optimization action documented: what was considered, what was projected, why this option was selected. Audit-native records exportable as structured compliance evidence.
SLO maintained under injected latency. No degradation detected.
Resource allocation reduced 30%. SLO margin preserved at 18%.
Start with Observation Mode
14 days, zero changes, zero risk. See your SLO-cost tradeoffs and compliance evidence potential.