Rivac AI LabsAI Infrastructure Provisioning & Scaling
AI infrastructure tends to land on one of two expensive extremes, GPUs provisioned for peak load sitting idle most of the time, or capacity sized for the average case that throttles training runs and times out inference during real demand. This service sizes and provisions cloud, GPU, and edge infrastructure for training and inference workloads, then builds autoscaling that responds to actual load signals instead of static headcount guesses. It's for teams running ML or LLM workloads on infrastructure that's either burning cash sitting idle or falling over under real traffic. The result is a capacity plan and autoscaling setup that tracks demand instead of guessing at it.
This is a capability we deliver for you as a service, not a role we’re hiring for.
How We’d Approach This
A clear, staged plan, not a black box
These are the service-specific steps inside our six-step process.
- 1
Diagnose current infrastructure utilization across training and inference workloads to find idle spend and capacity gaps.
- 2
Pilot right-sized provisioning and autoscaling rules on one workload to validate cost and performance before wider rollout.
- 3
Review capacity plans and autoscaling thresholds with infrastructure and finance stakeholders.
- 4
Roll out provisioning and autoscaling across remaining workloads, with utilization monitoring to catch drift from plan.
What You Get
Deliverables from this engagement
- Infrastructure utilization audit for training and inference workloads
- Right-sized provisioning plan across cloud, GPU, and edge resources
- Configured autoscaling policies tied to real load signals
- Utilization and cost-monitoring dashboard
Six Ways We Could Architect This
Different engagement, different build, pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Read all 6 architectures as text
Lean Fast-Start Architecture
Provisioning
- Workload Requirements Assessed
- Single GPU Instance Provisioned
- Basic Environment Configured
Basic Scaling Script
- Cron Based Scale Up Script
- Cron Based Scale Down Script
Manual Monitoring
- Utilization Checked Manually
- Cost Reviewed Weekly
Adjustment
- Under or Over Provisioned
- Instance Size Adjusted Manually
- Left as Is
- Change Logged
Cloud-Native / Managed Services Architecture
Managed Cluster Layer
- Managed Kubernetes GPU Cluster
- Managed Load Balancer
- Training Workload Pool
- Inference Workload Pool
Autoscale Operations
- Managed Autoscaler
- Horizontal Pod Scaling Rules
- Node Pool Scaling Rules
Cost Operations
- Cloud Cost Management Dashboard
- Idle Resource Detection
Delivery Layer
- Utilization Report per Workload
- Right Sizing Recommendations
- Capacity Plan Updated Quarterly
Event-Driven & Automation-First Architecture
Triggers
- Queue Depth Metric Rises
- Autoscaler Event Fired
- GPU Utilization Threshold Crossed
- Scheduled Idle Window Reached
- Scale Down Event Fired
Automation Layer
- Automated Scale Out Action
- Automated Scale In Action
- New Capacity Registered
Alert Path
- Capacity Threshold Still Breached
- Auto Alert to Infra Team
- Logged as Resolved
Delivery
- On Call Infra Engineer Notified
Enterprise-Grade & Compliance-Ready Architecture
Governance Layer
- Infrastructure Governance Policy
- Cost Governance Board
- Access Control for Infra Changes
Resilient Infrastructure
- Multi Region Redundant Deployment
- Disaster Recovery Failover Plan
- Role Based Provisioning Access
Auditable Operations
- Audit Log of Provisioning Changes
- Capacity Review Checkpoint
- Compliance Cost Reporting
Compliance Delivery
- Signed Off Capacity Plan
- Audit Trail Export
- Retention Schedule for Infra Logs
AI-Augmented, Human-in-the-Loop Architecture
Demand Signals
- Historical Training and Inference Load
- Capacity Forecasting Model
- Live Utilization Telemetry
AI Forecasting Layer
- Predicted Demand Curve
- Recommended Scaling Policy Changes
Human in the Loop
- Infra Engineer Reviews Forecast
- Safe and Cost Sound
- Adjust Forecast Assumptions
- Human Approved Scaling Policy
Delivery
- Policy Deployed to Production Cluster
- Utilization Tracked Against Forecast
Integration-Heavy / Ecosystem-Connected Architecture
Client Systems
- Client Cloud Account
- Infrastructure Integration Layer
- Client FinOps Tooling
- Client Observability Stack
Infrastructure Core
- Provisioning and Autoscaling Engine
- Capacity Plan Generator
Two Way Sync Layer
- Client CICD Deployment Pipeline Sync
- Client Ticketing System Sync
- Automated Deployment Triggers
- Capacity Request Workflow
Shared Governance
- Joint Review with Client Infra Team
- Published Capacity and Cost Plan
More in MLOps & LLMOps
- AI Production Incident Response & On-Call
Dedicated on-call coverage and incident-response playbooks for machine-learning and LLM systems running in production.
- AIOps & Automated Anomaly Detection
Automated anomaly detection and self-healing operations that catch and resolve AI-system issues before they become incidents.
- ML/LLM CI/CD Pipeline Engineering
Automated build, test, and deployment pipelines for machine-learning and LLM-powered releases, including staged rollouts and safe rollback.
- Model Monitoring & Drift Detection
Continuous observability into deployed model behavior that catches accuracy decay, data drift, and silent failures before users notice.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.
Talk to us about AI Infrastructure Provisioning & ScalingMost engagements like this start as a $500–$2,500 pilot, see full pricing.