Post by Xinference

Most teams can get a model running in a weekend. Getting it production-ready? That's where 80% of projects stall. Here's the checklist we use internally (and with enterprise customers) before any model goes live: ☐ 1. Latency SLA defined Not "as fast as possible." A hard number. P50, P95, P99. If you can't measure it, you can't hold it. ☐ 2. Throughput ceiling tested Load test at 3x your expected peak. Not 1x. Not "we think it'll be fine." Saturate the GPU and know exactly where it breaks. ☐ 3. Failover path exists What happens when your primary model goes down? Cold restart? Fallback to a smaller model? Route to API? If the answer is "page someone at 2am," you're not production-ready. ☐ 4. GPU utilization above 40% Below that, you're burning money. Most teams we audit sit at 15-25% utilization because they over-provision "just in case." Batching, model co-location, and smart scheduling can push this to 55%+ without touching latency. ☐ 5. Model update path defined Can you swap a model version without downtime? How long does it take from "new weights available" to "live in production"? If the answer is days, your iteration cycle is too slow. ☐ 6. Observability beyond "it's running" Token throughput, queue depth, time-to-first-token, error rates by model, cost per request. Not just uptime. You need to know why it's slow before users tell you. Most inference platforms pass checks 1-2. The ones that survive production pass all six. The gap between "demo-ready" and "production-ready" is where engineering credibility lives. Screenshot this before your next deployment review.