Multi-region spot GPU orchestration for cost-optimized batch ML inference

Multi-region spot GPU orchestration for cost-optimized batch ML inference targets a $18.0B = 180K companies running production ML inference x $100K annual GPU/inference spend. Scope: startups, mid-market SaaS, and enterprises with in-house ML teams across NA/EU/Asia. Assumes ~20 percent of the 900K software companies globally (Gartner estimate) deploy models beyond experimentation. total addressable market with low saturation and a year-over-year growth rate of 42 percent (2023-2028 CAGR for AI infrastructure spend, per IDC and investor reports; driven by LLM inference scaling and margin pressure).

Key trends driving demand: LLM inference cost crisis -- Foundation model inference can consume 40-60 percent of AI product COGS, forcing startups to optimize GPU spend or risk negative unit economics.; Spot instance supply expansion -- AWS, GCP, Azure expanding spot GPU availability in secondary regions (eu-west-2, ap-southeast-2) as hyperscale data center buildouts continue through 2024-2025.; Shift to async/batch inference patterns -- Products like email summarization, video transcoding, and RAG pipelines increasingly decouple user request from inference execution, making latency-tolerant architectures viable.; Open-source inference tooling maturity -- Projects like vLLM, TGI, and Ray Serve have proven production-grade, lowering the barrier for teams to self-host vs. relying on managed API providers..