Why AI Workloads Need Different Infrastructure
Traditional web applications have predictable resource patterns: CPU-bound request handling, steady memory usage, linear scaling with traffic. AI workloads break these assumptions.
GPU compute is expensive and bursty. Model training jobs consume massive GPU resources for hours or days, then nothing. Inference workloads spike unpredictably. Paying for peak GPU capacity 24/7 is wasteful; not having capacity when you need it blocks your entire ML pipeline.
Storage patterns are unique. Model artifacts are large (gigabytes to terabytes). Training datasets need high-throughput access during training but can sit in cold storage otherwise. Vector databases require low-latency SSD storage with specific IO patterns. General-purpose storage tiers waste money on AI workloads.
Networking matters more than you think. Distributed training requires high-bandwidth, low-latency interconnects between GPU nodes. Model serving needs fast response times to end users. Data pipelines move large volumes between storage, compute, and serving layers. Poor networking architecture creates bottlenecks that no amount of compute can fix.
We design infrastructure that accounts for these patterns from day one, rather than retrofitting general-purpose architecture for AI workloads.
Our Architecture Approach
Assessment & Design
Every engagement starts with understanding your specific workloads, scale requirements, compliance constraints, and budget targets. We don’t apply templates — we design architectures that match your reality.
We evaluate:
- Current infrastructure and spending patterns
- Workload profiles (training, inference, data processing)
- Scale projections and growth patterns
- Compliance and data sovereignty requirements
- Team capabilities and operational maturity
Compute Strategy
Training workloads get spot/preemptible instances with checkpointing — save 60-80% versus on-demand pricing with fault-tolerant job management. Training jobs automatically resume from checkpoints if instances are reclaimed.
Inference workloads get right-sized GPU instances with auto-scaling policies tuned to your latency SLAs. We implement model batching, quantization, and caching to maximize throughput per GPU dollar.
Non-GPU workloads (data preprocessing, API servers, orchestration) run on cost-optimized CPU instances with separate scaling policies. No GPU waste on tasks that don’t need it.
Infrastructure as Code
Every component — compute, networking, storage, monitoring, security — is defined in Terraform or Pulumi and version-controlled in Git. This means:
- Reproducibility — spin up identical environments for dev, staging, and production
- Auditability — every infrastructure change is tracked, reviewed, and reversible
- Disaster recovery — rebuild entire environments from code in minutes, not days
- No drift — manual changes are detected and flagged
We don’t do “ClickOps.” If it’s not in code, it doesn’t exist.
Cost Optimization
Cloud cost management for AI workloads requires more than turning off unused instances. We implement:
- Right-sizing analysis — match instance types to actual resource utilization
- Reserved capacity planning — commit to steady-state workloads for 30-40% savings
- Spot/preemptible strategy — use interruptible instances for fault-tolerant workloads
- Storage tiering — automatically move data between hot, warm, and cold storage
- Scaling policies — scale down aggressively during off-peak hours
- Cost dashboards — real-time visibility into spend by team, project, and workload type
Most clients see 30-50% cost reduction within the first quarter after optimization, with continued savings as scaling policies mature.
Monitoring & Observability
You can’t optimize what you can’t see. We deploy comprehensive monitoring from day one:
- Infrastructure metrics — CPU, memory, GPU utilization, disk IO, network throughput
- Application metrics — request latency, throughput, error rates, model inference time
- Cost metrics — real-time spend tracking with budget alerts
- Custom dashboards — Grafana dashboards tailored to your team’s needs
- Alerting — PagerDuty/Slack integration with escalation policies and runbooks
Security & Compliance
Security is an architecture decision, not a bolt-on. We build in:
- Network segmentation — VPCs, subnets, and security groups that enforce least-privilege access
- Encryption — at rest and in transit, with customer-managed keys where required
- IAM — role-based access control with temporary credentials and audit logging
- Secrets management — HashiCorp Vault or cloud-native secrets managers
- Compliance frameworks — GDPR, SOC 2, HIPAA-ready architectures with documentation
Cloud-Agnostic, Opinionated Design
We have deep expertise across AWS, GCP, and Azure, and we recommend the platform that best fits your specific requirements:
- AWS — broadest service catalog, strongest GPU availability (P4d, P5 instances), mature ML ecosystem (SageMaker, Bedrock)
- GCP — strongest in AI/ML tooling (Vertex AI, TPUs), competitive pricing, excellent Kubernetes support (GKE)
- Azure — best for Microsoft-centric organizations, strong compliance certifications, OpenAI partnership
Where possible, we design for portability — containerized workloads, cloud-agnostic infrastructure-as-code, and abstraction layers that reduce switching costs. True vendor lock-in is rarely in your interest.
Migration & Modernization
If you have existing infrastructure that needs optimization or migration, we plan and execute transitions with zero downtime:
- Assessment — map current architecture, costs, and pain points
- Design — target architecture optimized for your workloads
- Phased migration — blue-green or canary cutover with rollback capability
- Validation — performance testing under production load before decommissioning old infrastructure
- Optimization — continuous tuning of scaling policies and cost structure post-migration
We don’t rip and replace. We transition incrementally, validate at each phase, and only decommission old infrastructure once the new setup is proven stable.