Hey everyone, Like a lot of you, I got tired of trying to guess whether it actually makes financial sense to migrate certain workloads off commercial APIs and onto self-hosted open-weight models (or vice versa), especially when factoring in prompt caching discounts and fluctuating cloud GPU spot prices. Most comparison sites just throw a static pricing table at you or rank models by raw ELO without letting you plug in your actual monthly volume. So, I ended up putting together a lightweight tool to solve this for my own workflow: ModelAnalysis.si A few things it does that might be useful if you’re building agentic pipelines or managing API spend: API vs. Self-Hosting Breakeven Matrix: You can adjust volume sliders for input/output tokens and cache hit rates, and it automatically calculates the exact point where renting a RunPod spot instance (like an RTX 4090, L40S, or A100) beats paying per-token API fees. VRAM Sizing & Deployment Specs: For open-weight models, it maps out memory footprints across quantizations (Q4, FP8, FP16) and generates ready-to-use execution parameters. Live Telemetry & Pareto Frontiers: Pulls in live pricing, LMSYS Arena ELO rankings, coding/agentic indices, and latency stats across nearly 500 models so you can spot pricing anomalies or value sweet spots instantly. No paywalls, logins, or bloated UI—just pure telemetry and calculators. Would love to hear what you think or if there are any other specific hardware configs or benchmarks you’d want to see added to it! submitted by /u/zackku
Originally posted by u/zackku on r/ArtificialInteligence