There's a term for the anxiety of picking a good option when a better one might exist: FOBO, Fear of Better Options. It used to be about restaurant menus and flight bookings. Now it's how a lot of engineering teams pick their LLM.
Every few months another "best model yet" ships. A point release here, a new pricing tier there, an open-weight model that suddenly closes the gap on your specific task. Each launch reopens a question your team just finished answering: is what we shipped on still the right decision?
The result is confusion and fatigue. Teams either re-benchmark from scratch on gut feel every time a headline drops, or they stop re-benchmarking at all and quietly drift on cost or quality without anyone noticing, because nobody has a process to check.
Engineering leads: when did you last re-evaluate your default model against what's available since, and validate it using your own traffic instead of someone else's leaderboard?
This is the exact gap Cortega's Model Recommender closes. It's free and self-serve: it ranks a curated catalog of current models against your actual workload and stated priorities, and shows cost, quality, and performance versus whatever you run today, with public benchmark evidence behind every result. You get an answer grounded in your own traffic, so switching models becomes a decision you make on purpose instead of a rumor you're chasing every quarter.
The winning enterprise strategy isn't chasing every shiny model release. It's building an infrastructure layer agile enough to adopt the best model for the job, automatically.