Evaluation Guide / Infrastructure & Deployment
How to Evaluate AI Infrastructure and Deployment Platforms
Evaluate AI infrastructure platforms across GPU management, model serving, auto-scaling, MLOps, cost optimization, and multi-cloud deployment.
Infrastructure Is the Bottleneck — Not Models
The gap between training a high-performing model and serving it reliably at enterprise scale is vast. AI infrastructure platforms must handle GPU orchestration, model packaging, auto-scaling, monitoring, and cost management across diverse deployment targets — from cloud clusters to edge devices. Infrastructure decisions made today lock in performance ceilings and cost floors for years.
Infrastructure Evaluation Timeline
Workload Profiling
1–2 weeks
Catalog all model types, inference patterns (batch/real-time/streaming), latency SLAs, and throughput requirements.
Architecture Design
2–3 weeks
Define target architecture across cloud providers, on-premise clusters, and edge locations with networking and storage requirements.
Platform Benchmarking
3–4 weeks
Run representative workloads on 3–4 platforms; measure latency, throughput, scaling speed, and cost per inference.
Production Migration
4–8 weeks
Migrate 2–3 production models, validate SLAs, set up monitoring and alerting, and train operations teams.
Core Evaluation Dimensions
GPU & Compute Management
GPU cluster orchestration, multi-tenancy, fractional GPU sharing, spot/preemptible instance support, and hardware abstraction across NVIDIA, AMD, and custom silicon.
Model Serving
Multi-framework support (PyTorch, TensorFlow, ONNX), model versioning, A/B testing, canary deployments, batching strategies, and streaming inference.
Auto-Scaling
Horizontal and vertical scaling policies, scale-to-zero for cost savings, custom metric-based scaling, cold start latency, and burst capacity handling.
MLOps Lifecycle
Experiment tracking, model registry, CI/CD for ML, feature stores, data versioning, and automated retraining pipelines.
Cost Optimization
Inference cost tracking per model/team, right-sizing recommendations, spot instance orchestration, model compression tooling, and chargeback reporting.
Multi-Cloud & Edge
Deployment across AWS, Azure, GCP without vendor lock-in, edge runtime support, offline inference capability, and federated model management.
Infrastructure Platform Comparison
| Capability | Cloud-Native MLaaS | Open-Source Stack | Managed ML Platform |
|---|---|---|---|
| GPU Orchestration | Integrated (provider GPUs) | Kubernetes + custom operators | Abstracted (multi-cloud) |
| Model Serving | Proprietary endpoints | Triton / vLLM / TGI | Managed endpoints + custom |
| Scale-to-Zero | Available (cold start 10–60s) | Custom (KNative/KEDA) | Available (cold start 5–30s) |
| Multi-Cloud | Single provider lock-in | Portable by design | Multi-cloud native |
| Edge Deployment | IoT services (limited ML) | Custom compilation | Edge runtime included |
| Cost Visibility | Cloud billing integration | Manual tracking | Built-in cost dashboards |
| Operational Complexity | Low (managed) | High (self-managed) | Medium (semi-managed) |
The True Cost of AI Inference
Cost per Inference
Cost/Inference = (GPU Hours × Hourly Rate + Networking + Storage + Orchestration Overhead) / Total Inferences Served
Auto-Scaling: The Make-or-Break Capability
Auto-scaling for AI workloads is fundamentally different from web application scaling. Model loading times of 30 seconds to 5 minutes (for large models) mean that reactive scaling alone is insufficient. Evaluate platforms on their support for predictive scaling, warm pool management, and the granularity of their scaling metrics.
Infrastructure Platform Due Diligence
- Supports your primary ML frameworks (PyTorch, TensorFlow, JAX) without format conversion
- GPU fractional sharing enables cost-effective serving of smaller models
- Scale-to-zero available with cold start latency under 30 seconds for your model sizes
- Built-in model versioning with instant rollback capability
- Cost tracking at per-model and per-team granularity with chargeback support
- Multi-cloud deployment from a single control plane without repackaging models
- Canary and shadow deployment patterns for safe production updates
- Integration with existing CI/CD systems (GitHub Actions, GitLab CI, Jenkins)
- Edge deployment runtime for models under 500MB with offline inference support
- SLA guarantees with financial backing (99.9%+ uptime for serving endpoints)
Model Compression and Optimization
The most cost-effective inference improvement is often model optimization rather than more hardware. Evaluate whether platforms provide built-in tools for quantization (INT8, INT4), distillation, pruning, and format conversion (ONNX, TensorRT) that can cut serving costs substantially with limited accuracy impact — measure the trade-off on your own model rather than accepting a headline figure.
Hidden Cost Warning
Beware of platforms that quote inference costs based on optimized model benchmarks but deploy unoptimized models by default. Always benchmark with your actual models in your actual deployment configuration. Ask vendors to demonstrate optimization tooling on your specific model architectures during evaluation.
Selection Process
- Profile your workload mix — The optimal platform for 80% batch inference differs dramatically from one optimized for 80% real-time serving.
- Benchmark at your target scale — Performance at 10 QPS tells you nothing about behavior at 10,000 QPS. Test at 2× your projected peak load.
- Measure cold start realistically — Load your largest production model from a cold state 50 times. The P99 cold start time is your real scaling bottleneck.
- Calculate 18-month TCO — Include GPU costs, networking, storage, operations staffing, and the opportunity cost of models stuck in development due to deployment friction.
- Test failure modes — Kill nodes, exhaust GPU memory, and spike traffic during evaluation. How the platform fails is more important than how it performs under ideal conditions.
The best AI infrastructure is invisible — your data scientists should think about models, not about Kubernetes manifests and GPU driver versions.
Reference Architectures
Real-Time Serving
GPU-backed endpoints with auto-scaling, model caching, request batching, and sub-100ms P99 latency targets.
Batch Processing
Spot instance orchestration, distributed inference, checkpointing, and cost-optimized scheduling for throughput workloads.
Edge + Cloud Hybrid
Lightweight edge runtimes with cloud fallback, model synchronization, and centralized monitoring across deployment tiers.
Researched and reviewed under Xither's editorial standards — AI-assisted, adversarially reviewed, and primary-sourced. Spot an error? Tell us.
Procurement
Shortlisted? Take it to RFP.
Enterprise AI RFI & RFP Template — every question ships with what a strong answer looks like and the red flags to watch for, so you score vendors side by side instead of comparing sales decks. One-time purchase, exports to XLSX.