The Platform

Every model gets its own scaling policy

The MLSrvyn platform is four interconnected systems: Traffic Profiler, SLO Engine, GPU Allocator, and Fleet Dashboard.

01 — Traffic Shape Profiler

Traffic Shape Profiler

Continuous analysis of each endpoint's request distribution. Detects burst coefficient, diurnal periodicity, inter-arrival variance. Classifies into scaling archetypes: Bursty, Steady, Batch, Hybrid.

Bursty Steady Batch Hybrid

02 — SLO-Driven Scaling Engine

SLO-Driven Scaling Engine

Define latency SLOs per model or per SLO tier. The engine monitors p50/p90/p99 in real-time and adjusts replica counts to maintain the SLO envelope — scaling up before a breach, not after.

Proactive scaling before p99 breach

Per-model p50 / p90 / p99 tiers

Real-time latency histogram integration

Latency chart showing p99 values staying within green SLO threshold envelope, with scaling event markers when replicas increase to prevent SLO breach

03 — GPU Bin-Packing Allocator

GPU Bin-Packing Allocator

When the scaling engine decides to reduce replicas, the allocator rebalances workloads across fewer physical nodes before releasing GPU capacity. This prevents the classic waste pattern of 3 replicas on 3 partially-utilized nodes.

mlsrvyn allocate --fleet prod

Before rebalance:
  node-gpu-01:  31% util  [rec-v3, llm-chat]
  node-gpu-02:  28% util  [rec-v3, scorer-fraud]
  node-gpu-03:  34% util  [llm-chat, scorer-risk]

After bin-pack:
  node-gpu-01:  91% util  [rec-v3, llm-chat, scorer-fraud, scorer-risk]
  node-gpu-02:  released ✓
  node-gpu-03:  released ✓

04 — Fleet Dashboard

Fleet Dashboard

Unified view of all models in your fleet. Per-model SLO compliance, GPU utilization, replica count, traffic rate, and scaling event log. No more 100-tab Grafana hunting.

Per-model SLO status at a glance

GPU utilization mini-bars per node

Scaling event timeline and reason log

Live p99 latency in monospace digits

Fleet dashboard table showing ML model list with colored SLO status chips, GPU utilization bars, latency values, and replica counts

See it on your own fleet

Our team will run a 48-hour traffic analysis on your existing endpoints and show you the projected bin-packing savings before you commit.

Request Access View Benchmarks

MLSrvyn does not train models, host model weights, or replace your GPU cloud provider. It sits between your orchestration layer and your GPU fleet, managing how replicas are allocated and scaled. Your infrastructure stays yours.