LLM Router and Model Selection

In late March, someone quietly posted to Reddit about building a unified LLM router — a single OpenAI-compatible endpoint sitting in front of more than thirty models, normalising their APIs into one clean interface. The idea wasn't new in concept, but the execution was: one address, many models, zero friction for the application layer. Then, three weeks later in mid-April, a more developed version of the same instinct appeared on Dev.to. This time the author went further, describing a gateway that doesn't just route traffic to different models — it learns which model to use. Usage patterns, task types, cost signals, latency data: the system watches, infers, and redirects. The gap between those two posts tells you something about the pace of iteration happening in this space. What started as a plumbing problem became, within weeks, a machine learning problem.

If confirmed as a real pattern — builders independently converging on adaptive LLM routing infrastructure — here is what this means. The assumption that a single model handles all tasks is quietly dying at the engineering level, even if the marketing hasn't caught up. Organisations running AI workloads will increasingly treat model selection as a runtime decision, not a configuration choice. That shifts value away from any single frontier model toward the orchestration layer that sits above them — the thing that knows when to use the expensive model and when the cheaper one is genuinely good enough. This is quietly significant for API pricing strategies, for model incumbents, and for the small companies now building the routing layer itself. Second-order effect: as routing becomes commoditised infrastructure, switching costs between model providers collapse further, which is good for buyers and uncomfortable for anyone selling model access on lock-in assumptions.

Watch for enterprise tooling or well-funded developer platforms announcing native routing or model-selection features — that's the signal that this pattern has moved from clever side projects into the production mainstream.

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