AI Health Orchestrator
Production-grade model reliability for teams that cannot afford downtime
Continuously monitors the health of every AI model and service endpoint in your stack. When a model degrades, latency spikes, or a provider goes dark, the orchestrator detects the event within seconds and routes traffic to a healthy fallback — automatically, without human intervention.
Everything you need to keep models healthy
Built for engineering teams running AI in production — not a dashboard toy, but an active control plane with teeth.
Continuous probing of every registered model endpoint. Tracks liveness, readiness, and saturation at configurable intervals. Alerts fire in under 30 seconds of a detected anomaly.
Policy-driven failover to secondary or tertiary model providers with no code change required. Failed endpoints are quarantined and re-evaluated on a configurable cool-down schedule before being restored.
Per-model p50/p95/p99 latency histograms, tokens-per-second throughput, and error-rate time series. All signals are exportable to Prometheus and Grafana out of the box.
Define routing rules based on latency thresholds, cost budgets, or capability requirements. Chains can span multiple providers — primary, secondary, and last-resort — with weighted traffic splitting during gradual migrations.
Statistical deviation detection on rolling windows flags sudden quality regressions, token-count drift, and response-time outliers. Alerts are routed via webhook, PagerDuty, or Slack with full context attached.
Define service-level objectives per model or per application tier. The orchestrator tracks burn rate against each error budget in real time and surfaces burn-rate alerts before your SLO window closes.
Four stages from signal to resolution
The orchestrator runs a closed-loop control cycle. Each stage is observable and auditable.
Register model endpoints via API or config file. The orchestrator begins probing immediately — no SDK required in the model service itself.
Health checks, telemetry ingestion, and anomaly scoring run continuously. Every signal is correlated against the model's baseline to separate noise from real degradation.
When a threshold is breached, the orchestrator executes the configured recovery action: reroute traffic, restart the service, escalate to an on-call channel, or all three in sequence.
Every event — detection, action taken, time to recovery — is written to an append-only audit log. Incident reports are generated automatically for post-mortems.
Proven in real production scenarios
These are the situations where teams reach for AI Health Orchestrator first.
When a hosted LLM provider experiences a regional outage or rate-limit storm, the orchestrator detects the elevated error rate within seconds and shifts traffic to a configured secondary provider. End users see no interruption; engineers see a timestamped incident record.
Route latency-sensitive requests to the fastest available model at any given moment. As p95 latency rises on the primary endpoint, traffic shifts automatically to a lower-latency option — then shifts back once the primary recovers, with no configuration change.
GPU memory saturation, queue depth, and concurrency limits are tracked against configurable high-water marks. Alerts fire while headroom still exists, giving operations teams time to provision additional capacity before users experience degraded quality or timeouts.
Ready to put your AI infrastructure on autopilot?
LyDian AI, INC. works directly with engineering and platform teams to deploy AI Health Orchestrator against your specific model stack and SLO requirements.
Questions before a call? Reach us at +1 813 458 5004
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