Use Cases
Every inference workload has a different scaling story
Bursty recommenders, steady LLMs, periodic batch scorers — each behaves differently at 3am vs. 3pm. MLSrvyn gives each workload type a policy that fits its traffic shape.
Recommendation Systems
Handle bursty traffic spikes without cold-start gaps that break engagement loops. MLSrvyn pre-warms replicas based on historical diurnal patterns, so morning surges absorb cleanly.
See how it worksLLM Serving
Long-running sessions need replica persistence, not scale-to-zero. MLSrvyn classifies LLM endpoints as Steady archetype and holds minimum replicas alive across low-traffic windows.
See how it worksReal-Time Scoring
Sub-100ms SLO on fraud and risk models — even during traffic surges that hit at irregular intervals. MLSrvyn enforces p99 SLO at the scaling layer, not the load balancer.
See how it worksWhich workload fits your fleet?
Tell us your fleet composition and we'll map each model to a scaling archetype — free fleet analysis with every access request.