Post by Xinference

We calculated the real cost of LLM inference at 10M tokens per day. The result surprised us. Not because one option was clearly cheapest, but because the cheapest option changes completely depending on your scale. Here's what most teams get wrong: they pick an inference approach based on today's volume, then get locked in as usage grows 10x. 📌 Three paths, very different cost curves: Path 1: Managed API (OpenAI, Anthropic, etc.) └ Zero setup. Pay per token. └ Great at low volume (< 1M tokens/day). └ At 10M+ tokens/day, costs compound fast. └ A single GPT-4-class model at scale can cost $50K-100K/month in API fees alone. Path 2: Fully Self-Hosted └ High upfront investment: GPUs, infra team, maintenance. └ Cost per token drops dramatically at scale. └ But you carry the operational burden: updates, scaling, monitoring, fault recovery. └ Break-even vs API typically happens around 2-5M tokens/day. Path 3: Hybrid (the underrated option) └ Run your high-volume, latency-sensitive workloads on self-hosted infra. └ Use managed APIs for low-volume, experimental, or bursty tasks. └ One unified API layer across both. No code changes when you shift workloads. └ This is where most production teams land after 6 months of iteration. The crossover math (simplified): → At 500K tokens/day: API wins. Don't overcomplicate it. → At 2-5M tokens/day: self-hosted starts breaking even. Evaluate seriously. → At 10M+ tokens/day: self-hosted saves 40-70% vs API pricing. → At any scale with mixed workloads: hybrid gives you cost control without operational lock-in. Key takeaways: – The "cheapest" option depends entirely on your token volume and growth trajectory – Most teams underestimate how fast API costs scale with usage growth – A hybrid approach with a unified API layer gives you flexibility to shift workloads as economics change – The real cost isn't just per-token price; it's also engineering time, vendor lock-in, and migration pain Don't optimize for today's bill. Optimize for next year's. #OpenSource #LLM #AIInfrastructure #ModelDeployment #Xinference #CloudCost #MLOps