The LLM Router Craze: Why Everyone Built One
If you've been building AI features in 2026, you've probably seen the same pattern: every SaaS team suddenly has an LLM router. The pitch is seductive — route each prompt to the cheapest or fastest model that can handle it, and you'll cut costs by 30-50% while keeping quality high. On paper, it's a no-brainer. In practice, it's a trap.
We fell for it too. At Devs & Logics, we built a custom LLM router for our own product and for client projects. We spent weeks designing heuristics, evaluating models, and tuning thresholds. Then we deprecated the whole thing. In this post, I'll explain why we walked away, when routing might still make sense, and what we use instead — a simpler approach that saved us money, time, and a lot of headaches.
The router trend exploded because model choice became overwhelming. By 2025, there were dozens of capable models from OpenAI, Anthropic, Google, and open-source providers. Each had different pricing, latency, and strengths. Startups saw an opportunity to build a middleware layer that would intelligently pick the right model for each request. Venture capital flowed into routing startups. Every engineering blog had a post about how to build one.
But here's the thing: most of those routers were solving a problem that didn't exist for the majority of use cases. If your app is doing summarization, extraction, or chat, you don't need a router. You need a reliable model that works well enough. The complexity of routing often outweighs the savings.
Our Original LLM Router: What We Built and Why
Let me give you a concrete picture. We were building an AI-powered document analysis feature for a client's SaaS product. The client wanted to process PDFs, extract key data points, and generate summaries. We had two main models in mind: a powerful flagship model for complex reasoning and a cheaper, faster model for straightforward tasks. We thought we could save money by sending simple requests to the cheap model and only escalating to the expensive one when needed.
So we built a router. It was a TypeScript service that sat between our Next.js API routes and the model providers. It evaluated each request based on prompt length, complexity score, and a few other signals. It used a small classification model to predict which model would perform best. We added a fallback mechanism: if the cheap model's confidence was low, we'd re-run the request on the flagship.
At first, it worked. We saw a 25% cost reduction in testing. But then we deployed it to production, and the real world hit.
The router added ~200ms of latency on every request. That doesn't sound like much, but for a chat interface, it's noticeable. More importantly, it made debugging a nightmare. When a user complained about a bad response, we had to figure out which model was used, why the router chose it, and whether the fallback triggered. We added logging, but the logs were massive and hard to parse. We spent more time debugging the router than improving the actual AI features.
And the cost savings? They shrank. The cheap model made more mistakes than we expected, so the fallback rate was high. We ended up running the flagship model for a significant portion of requests anyway. The net savings dropped to maybe 10%, which was not worth the complexity.
The Hidden Costs of Routing: Latency, Complexity, and Debugging Nightmares
Let's break down the hidden costs we encountered. First, latency. Every router adds at least one extra network call to evaluate the request. If you're using a small classifier, that's an extra API call. If you're doing heuristic checks, it's just CPU time, but still overhead. In our case, we used a classification model, so we added a full round-trip to our stack. That's 100-200ms per request, which is significant when you're aiming for sub-second responses.
Second, complexity. A router is a new component in your architecture. It needs to be deployed, monitored, and maintained. It has its own failure modes. What if the router service goes down? Then your entire AI feature is down. We had to add retries and circuit breakers, which added even more code.
Third, debugging. When something goes wrong, you have to trace through the router's logic. Did it pick the right model? Was the prompt modified? Did the fallback trigger? This is especially painful in a serverless environment like Vercel, where logs are ephemeral and you can't easily attach a debugger.
We also realized that the router was making our team slower. Every time we wanted to try a new model, we had to update the router's evaluation logic. We couldn't just swap out the model in one place; we had to re-tune the thresholds. This slowed down our iteration cycle, which is critical in AI development where models change frequently.
Finally, there's the cost of the router itself. If you're using a third-party routing service, you're paying per request. If you're self-hosting, you're paying for compute. Either way, that cost eats into your savings.
When LLM Routing Actually Makes Sense (Spoiler: Rarely)
Now, I'm not saying routing is never useful. There are specific scenarios where it's justified. For example, if you're building a platform that serves thousands of different use cases with wildly varying requirements — like a multi-tenant API where one customer needs long-form code generation and another needs short sentiment analysis — routing can help you optimize costs across the board.
Another case is when you have strict latency or cost SLAs for different tiers of users. If you have a free tier that uses a cheap model and a premium tier that uses a flagship model, that's not really routing — it's just tier-based model selection, which you can implement with a simple conditional.
Routing also makes sense if you're dealing with very long prompts or very high volume, where even a 10% cost saving translates to thousands of dollars per month. But for most SaaS products, the volume isn't that high, and the savings don't justify the complexity.
Let me give you a rule of thumb: if you can enumerate your use cases in a single document, you don't need a router. You need a simple mapping. If you have more than, say, ten distinct use cases with different model requirements, then maybe routing is worth considering. But even then, I'd start with a simple switch statement and only move to a router when you have data proving it's necessary.
The Simpler Alternative: Direct Calls with Smart Fallbacks
So what did we replace our router with? A much simpler pattern: direct model calls with smart fallbacks. Here's how it works.
First, we pick one primary model for each use case. For most tasks, that's a mid-tier model that offers a good balance of quality, speed, and cost. For example, in 2026, we might use a model like GPT-4.1-mini or Claude Haiku for most tasks. These models are cheap and fast enough for production.
Second, we implement a fallback chain. If the primary model returns an error, times out, or produces a response that fails our validation checks, we retry with a more powerful model. The validation check is simple: we look for expected output structure, like valid JSON or a minimum length. If it fails, we escalate.
Third, we use structured outputs and tool calling to make the models more reliable. With TypeScript and Zod, we can define schemas for the expected output, and the model will conform to them. This reduces the need for routing because the model is less likely to fail.
This pattern is easy to implement in Next.js API routes. Here's a pseudo-code example:
export async function POST(req: Request) {
const body = await req.json();
try {
const result = await callModel('primary', body.prompt);
if (isValid(result)) return Response.json(result);
// fallback
const fallbackResult = await callModel('fallback', body.prompt);
return Response.json(fallbackResult);
} catch (error) {
// final fallback
const finalResult = await callModel('flagship', body.prompt);
return Response.json(finalResult);
}
}That's it. No router, no extra network calls. The fallback only happens when something goes wrong, which is rare if you've set up your schemas correctly.
We also use caching aggressively. For common prompts or deterministic tasks, we cache the response at the edge with Vercel's data cache. This reduces the number of model calls, which is the biggest cost lever. We saw a 40% reduction in API costs just from caching, which was more than our router ever saved.
How We Refactored Our AI Stack in 2026
When we decided to deprecate the router, we did a full refactor of our AI stack. Here's the process we followed, which you can apply to your own project.
First, we audited our use cases. We listed every AI feature in our product and categorized them by complexity. We found that 80% of our requests were simple — extract data, summarize, or classify. Only 20% required deep reasoning. So we chose a primary model that handled the 80% well, and a fallback for the 20%.
Second, we standardized on a single provider. We were using multiple providers, which made the router necessary to compare them. By consolidating on one provider, we eliminated the need for cross-provider routing. We kept a backup provider for disaster recovery, but we don't route between them dynamically.
Third, we implemented observability. We added logging for every model call, including the model used, latency, cost, and whether a fallback occurred. This gave us visibility into our AI costs and helped us identify issues quickly. We used a simple logging library and sent logs to our existing observability tool.
Fourth, we optimized our prompts. We spent time engineering prompts that work well with our primary model, reducing the need for fallbacks. We also used prompt caching where available, which cuts costs for repeated prefixes.
Finally, we built a small internal tool that lets us test different models against a set of golden examples. This helps us evaluate new models without deploying a router. If a new model comes out that's better and cheaper, we can switch our primary model in a day.
This refactor took us about a week. In contrast, we spent a month building the router. The result was a simpler, more maintainable system that costs less and performs better.
Key Takeaways for SaaS Founders Building AI Features
If you're building AI features for your SaaS product, here's my advice, based on our experience.
Start simple. Don't build a router until you have a proven need. Start with direct calls to a single model. You can always add complexity later.
Measure before you optimize. Track your actual costs and latency. You might find that your biggest cost driver is prompt length or cache misses, not model choice. Use tools like Langfuse or Helicone to get visibility.
Use fallbacks, not routers. A simple fallback chain handles edge cases without adding complexity. It's easier to reason about and debug.
Cache aggressively. Caching is often the most effective cost optimization. If you have repeated requests, cache the responses at the edge.
Invest in prompt engineering. A well-engineered prompt can make a cheaper model perform as well as a flagship. Spend time on this before considering a router.
If you're building a SaaS MVP and want to avoid these pitfalls, our SaaS MVP development service can help you architect your AI stack the right way from the start. We've been through the router hype and came out the other side with a simpler, more cost-effective approach.
For more on integrating AI without over-engineering, check out our AI integration best practices guide.
The LLM router craze is a classic example of over-engineering. In 2026, the winners are building lean AI stacks that focus on user value, not architectural elegance. We deprecated ours, and we're glad we did. You might want to reconsider yours too.