FastRouter

You don't always want a panel of models weighing in. Sometimes one model is enough, and sometimes the question is hard enough that it isn't. FastRouter Blend can run every time, as a model you call directly, or it can sit as a tool an agent reaches for only when it decides the question actually warrants a second opinion. Same underlying panel and judge, different amount of ceremony. Most questions don't need five models. The ones that do are exactly where this pays for itself.

Still asking a chat model to answer "ONLY YES OR NO" and parsing the result? It works until a model update changes the phrasing. Then tickets quietly misroute, and nothing crashes to tell you. TypeSafe Jev on FastRouter returns typed answers with probabilities instead of text. Yes/no, choice-and-score questions, several decisions in one call, no parsing. The post covers the failure modes, thresholds with an abstain path, and a checklist for replacing one parsed decision today. Read more:fastrouter.ai/blog/posts/sto…

It depends a lot on the job, which is why a single ranking rarely settles it. Running the same prompts against two or three of them on your own tasks takes a few minutes and tells you more than any list.

The hiring analogy fits. The part that trips teams up is confirming the cheaper model really holds up on the routine seats, and an eval on your own tasks answers that before any traffic moves.

Good reminder that judge defaults are a guess until measured on your own data. Running the same eval set across several judge models and comparing cost per eval is the useful exercise, thanks for sharing the numbers.

That is a painful way to learn it. Per key spend limits and alerts will not stop a bad loop from existing, but they cap how far one can run before someone notices.

Model choice per agent is the biggest cost lever in a swarm, so it is worth making it a config setting instead of code. Checking quality on your own tasks before downgrading is the step people skip.

First thing I would check is whether the traffic mix changed, longer prompts or more retries, since model and price stayed the same. Per request traces with token counts and retries usually point at it quickly.

The monthly total tells you nothing actionable. Tagging requests by feature, workflow or tenant at call time is what lets you answer which one actually moved the bill.

The fourth is not knowing which of those three is hitting you. Per request cost visibility turns the guess into a fix, and caching a static system prompt is usually the quickest win.

Slowdowns are hard to plan around when you are tied to one provider. A few models behind one endpoint, with latency visible per model, at least shows where the slowness actually sits.

Two of the three ways FastRouter Insights finds savings cost nothing to generate. The third one costs something on purpose. Model Switch replays your real requests against alternative models and scores both outputs with an LLM judge before it ever recommends a change. That's real inference, so it's opt-in, not on by default like the other two. The tradeoff is worth it. You're not guessing whether a cheaper model holds up on your traffic; you're looking at evidence from your own requests before you touch anything in production.

Honest answer from what we see: most teams only build the failover after the first bad day. Keeping the fallback chain at the gateway instead of inside each agent stops it becoming one more thing to maintain.

Silent upstream changes are the ones that hurt most. Running your own eval set on a schedule and keeping a fallback ready is the right pairing, and it helps when swapping the model never touches application code.

Agreed, the real test is the degraded path. Keeping the backup model at the routing layer means app code does not need its own fallback branch for every provider.

Library defaults nobody chose are a sneaky one. Pointing apps at an alias you control instead of a hardcoded model ID makes a retirement date a config change rather than an outage.

The account and balance juggling is the unglamorous part of testing models, agreed. One endpoint and one balance across providers turns comparing two APIs into changing a model name.

Those two numbers are the whole case for not guessing the model per task. Running the same prompt across a few models and checking cost against output before committing tends to pay for itself.

That sounds like a rough day. A lot of teams only learn how much one provider matters on days like this, and a backup route set up in advance is what turns an outage into a non event.

LLM gateway pricing looks simple at evaluation time and gets re-litigated by finance six months later. There are four pricing models hiding behind that one pitch, and only one of them doesn’t compound as you grow. Here’s the math to run before you sign anything. Read more in our newest article: fastrouter.ai/blog/posts/llm…