You can start with Site-to-Site VPN for control-plane traffic and low-volume data sync — it's encrypted and usually ready in days. But if you're moving model artifacts larger than ~10GB regularly, or your inference latency SLA is under 100ms end-to-end, you'll hit VPN bandwidth ceilings quickly. Plan for Direct Connect in your architecture from day one, even if you don't provision it immediately. The provisioning lead time alone (4–12 weeks with your ISP) means you should order it before you need it.

EKS Anywhere runs Kubernetes on your own hardware using AWS-compatible APIs — you manage the underlying infrastructure. AWS Outposts is AWS-managed hardware physically installed in your data center; AWS owns the rack, patches the firmware, and maintains the hardware. Outposts gives you native AWS services (S3 on Outposts, RDS, EKS) with zero data egress to the cloud. EKS Anywhere is lower cost and more flexible; Outposts is the right choice when you need the full AWS management plane locally with full data sovereignty.

For new deployments, NVIDIA Blackwell (B100/B200) is the current benchmark for sub-50ms inference on 70B+ parameter models. H100 SXM5 remains strong for 7B–34B models at lower cost. For brownfield environments, A100 80GB clusters are still highly capable for most enterprise inference workloads. iFactory's on-prem demo at Sapphire runs a Blackwell cluster serving SAP-context inference — we can show you the benchmark numbers against your specific model and throughput requirements. Hardware selection should always start with your target latency SLA and TCO model, not the GPU spec sheet.

Drift detection in hybrid environments requires capturing inference telemetry on-prem (input distributions, confidence scores, output patterns) and shipping that metadata — not the raw data — to AWS CloudWatch or SageMaker Model Monitor. You compare live distributions against your training baseline and trigger retraining alerts when statistical distance (KL divergence or PSI) exceeds your threshold. The key is that you only move metadata, not the regulated data itself, so data residency rules remain intact. iFactory deploys this monitoring pattern as a standard component in our hybrid architectures.

For steady-state inference workloads above ~50,000 requests/day, on-prem GPU CapEx typically breaks even with cloud GPU OpEx within 18–24 months. Below that threshold, pure cloud is usually cheaper when you factor in hardware depreciation, power, and data center costs. The hybrid model is most cost-effective when you separate the workloads correctly: burst training to AWS (elastic, pay-per-use) while running production inference on-prem (fixed CapEx, no egress costs). The egress cost alone — typically $0.09/GB out of AWS — makes large-scale inference economics strongly favor on-prem for data-heavy workloads.

Yes — this is one of our core specializations. iFactory's connector library includes native integrations for SAP S/4HANA (both Public and Private Cloud), ECC, SAP BTP, and Joule. We've deployed hybrid architectures where SAP transactional data stays on-prem, feeds fine-tuned models via secure API, and the inference output writes back to SAP processes in real time. Our Sapphire demo on May 13 shows exactly this pattern running live. If you're evaluating how AI fits into your S/4HANA migration or RISE/GROW decision, that session is built for your situation.