Frontier Inference Architecture
A decision tool that helps solutions architects and sales engineers pick the right open-source language model for a specific workload. Instead of asking "which model is best overall," it asks the harder question: given your constraint (latency, cost, token throughput, retries), which model actually wins on your infrastructure. You configure a workload (support copilot, voice agent, agentic code), tune constraints on the left panel, and get a rule-based recommendation. Then click Run Benchmark to test the recommendation live against real models via OpenAI-compatible APIs. You see TTFT (time-to-first-token), tokens per second, total latency, cost per prompt, and the actual request ID from Baseten. No LLM in the loop. Every recommendation is defensible line by line. Built for product engineers and sales engineers shipping AI features inside real products where infrastructure tradeoffs matter more than benchmark scores.
Open-source models converge on capability so fast that the real decision now lives in serving architecture, not raw performance. This tool moves the recommendation from "Claude is better" to "DeepSeek costs less on your cached-input pattern; gpt-oss-120b wins on TTFT for voice." Sales engineers and product leads can now back recommendations with live data and run it in customer calls.
Model picker for sales engineers choosing open LLMs
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