Cost-aware LLM scheduler for multi-provider inference routing

Cost-aware LLM scheduler for multi-provider inference routing targets a $4.8B = 120K companies running production LLM workloads x $40K annual LLM spend (assuming 15-20% addressable via routing/scheduling optimization). Basis: OpenAI reported 2M+ API customers in 2024; estimate 10% are production workloads above $2K/mo, skewed toward startups and mid-market SaaS. Conservative penetration assumes only companies spending $5K+/mo on inference see ROI. total addressable market with low saturation and a year-over-year growth rate of 65% (inference API spend growing as models move from prototype to production; time-based pricing adoption accelerating).

Key trends driving demand: Time-of-day pricing adoption -- DeepSeek V4 introduced peak windows in early 2025; expect OpenAI and Anthropic to follow as capacity constraints emerge, making cost-aware routing a table-stakes capability for production pipelines.; Model performance parity -- Llama 3.1, Mistral Large, DeepSeek V3 approaching GPT-4 on benchmarks, giving teams viable fallback options and increasing willingness to route across providers based on cost/latency.; LLM cost as COGS line item -- AI-native startups now report inference spend in board decks; CFOs demand cost predictability and optimization as gross margins compress below 60% for some copilot/agent products.; Inference workload segmentation -- Engineering teams distinguish latency-critical (chatbot, inline suggestions) from batch-tolerant (embeddings, summarization, data enrichment) workloads, creating natural routing policy boundaries..