Keep Your AI Reliable, Secure & Cost-Efficient

Monitoring-only plans start at $5K/month. Full managed operations (monitoring + retraining + FinOps + incident response) range from $10K–$30K/month depending on the number of models and complexity. This is typically 50–70% cheaper than building an internal MLOps team ($200K+ per ML engineer).

Inference latency, throughput, error rates, data drift, concept drift, prediction accuracy, and custom business KPIs. Alerts go to PagerDuty/Slack/email with configurable escalation chains. You get a weekly report summarizing performance, anomalies, and recommendations.

It depends on how fast your domain changes. E-commerce recommendation models may need weekly retraining. Fraud detection models drift monthly. Stable models (document classification) may go 3–6 months. We set up automated evaluation schedules and retrain only when performance drops below your thresholds — not on arbitrary schedules.

Yes. We onboard existing AI systems regardless of who built them. We audit the architecture, set up monitoring, create runbooks, and integrate with your existing infrastructure. Onboarding typically takes 2–4 weeks depending on complexity and documentation quality.

AWS (SageMaker, ECS, Lambda), Azure (ML Studio, AKS, Functions), and GCP (Vertex AI, GKE, Cloud Functions). We also manage hybrid environments with on-premise GPU clusters. Our monitoring tools are platform-agnostic — we use the same dashboards regardless of where your models run.

We maintain a registry of all AI systems with risk classifications per the EU AI Act. High-risk systems get automated bias testing, explainability reports, and human oversight protocols. We generate compliance documentation on demand for regulatory audits. Full enforcement begins August 2, 2026.

MLOps is the infrastructure and practices. Managed AI services means we run that infrastructure for you — 24/7 monitoring, incident response, retraining, cost optimization, and compliance. You get the outcome (reliable, cost-efficient AI) without hiring a 3–5 person MLOps team.

Four levers: GPU right-sizing (most endpoints are over-provisioned), auto-scaling (scale to zero when idle), spot/preemptible instances for batch jobs, and inference optimization (quantization, distillation, batching). We review costs monthly and implement optimizations continuously. Most clients see 30–40% savings within 90 days.

Standard SLA: 99.9% uptime, <15 min critical incident response, <4 hour resolution for P1 issues. Premium SLA: 99.95% uptime, <5 min critical response, dedicated on-call engineer. Both include monthly performance reviews and quarterly strategy sessions.

Absolutely. Most clients start with monitoring-only ($5K/month) and add retraining, FinOps, or full incident response as their AI footprint grows. There's no lock-in — you can adjust your plan monthly based on what you need.