AI workloads demand infrastructure that traditional cloud setups can't deliver. QuantPi architects GPU-optimized, auto-scaling cloud environments that train models faster, serve predictions cheaper, and comply with enterprise security requirements — across AWS, GCP, and Azure.
Running AI workloads on generic cloud infrastructure is like racing a Formula 1 car on dirt roads. You need GPU clusters optimized for your training jobs, inference endpoints that scale with traffic, cost controls that prevent runaway bills, and security that satisfies your compliance team. QuantPi delivers all of this — designed, built, and managed by engineers who live in the intersection of AI and cloud infrastructure.
We deploy on AWS, GCP, Azure, and hybrid environments. Our infrastructure-as-code approach means every environment is reproducible, auditable, and version-controlled. We use Terraform, Kubernetes, and modern GitOps practices to ensure your AI infrastructure is as reliable and scalable as your production applications.
Schedule a DemoProvisioning, scheduling, and optimization of GPU training clusters. Spot instance strategies that cut training costs by 60-80%.
Production-grade K8s clusters with GPU node pools, autoscaling, resource quotas, and monitoring — optimized for ML training and inference workloads.
Terraform and Pulumi modules for reproducible, version-controlled AI infrastructure. One-click environment provisioning.
Continuous cost monitoring, rightsizing, reserved instance strategy, and spot instance optimization. Typical savings: 40-70% on compute costs.
Deploy across AWS, GCP, and Azure based on pricing, GPU availability, and data locality. Avoid vendor lock-in with portable architectures.
VPC isolation, encryption at rest and in transit, IAM policies, audit logging, and compliance frameworks for SOC2, HIPAA, GDPR, and ISO 27001.
Audit current cloud setup, identify inefficiencies, and define target architecture for AI workloads.
1 weekDesign and implement infrastructure-as-code with GPU scheduling, networking, and security.
3-4 weeksMigrate existing workloads, deploy monitoring, and validate performance benchmarks.
2-3 weeksContinuous cost optimization, documentation, team training, and operational runbooks.
1-2 weeksProvision and manage large-scale GPU clusters for distributed model training with automatic spot instance fallback and checkpoint recovery.
Learn moreAuto-scaling inference endpoints with model caching, request batching, and edge deployment for sub-20ms latency.
Learn moreHIPAA and SOC2-compliant AI infrastructure with data encryption, access controls, audit logging, and network isolation.
Learn moreDynamic workload placement across cloud providers based on real-time GPU pricing, availability, and SLA requirements.
Learn moreIt depends on your workload. AWS has the broadest GPU selection, GCP offers TPUs and competitive pricing, Azure integrates well with Microsoft ecosystems. We design for your specific needs.
Typically 40-70% through spot instances, rightsizing, reserved capacity, and workload scheduling. We provide detailed cost projections before starting.
We offer both project-based and managed services. Many clients start with a build engagement and transition to managed operations.
Absolutely. We deploy within your existing AWS, GCP, or Azure accounts using infrastructure-as-code. Full visibility and control remain with your team.
Start with a technical conversation. No pitch decks, no pressure — just a discussion about what’s possible.