Scienaptic AI

Does that alternate data help me understand my customers' ability to pay? Or the intent to pay?

Most teams build AI SaaS platforms with shared RAG pipelines, GPUs, vector DBs, and models. It works… until enterprise scale hits. Then come the real problems: • Noisy neighbors in OpenSearch • GPU starvation • Retrieval quality degradation • Cross-tenant security concerns • Unpredictable latency & SLA failures The biggest lesson: Multi-tenant AI systems are NOT just a software problem. They are also: • Retrieval isolation problems • GPU scheduling problems • Embedding-space contamination problems • Resource governance problems The production-grade solution is tiered multi-tenancy: → Shared infra for smaller tenants → Dedicated retrieval + inference paths for large enterprise customers Key patterns that actually work in production: • Tenant-aware vector search filters • Dedicated indexes for large tenants • Token-aware GPU scheduling • Tenant-scoped caching • Per-tenant observability • IAM + KMS + VPC isolation • Reserved GPU pools for enterprise SLAs Senior engineers optimize for: fairness, isolation, latency predictability, and compliance. Not just model quality. That’s where real AI platform engineering starts. #AIEngineering #RAG #GenerativeAI #AWS #MLOps #LLMOps #VectorDatabases #SystemDesign #EKS #OpenSearch

I’m excited to be in Portland! If you’re at the Inclusiv conference, please let me know!