ClawRouter cut LLM API costs from $47 to $1.80 in a week: a look at the smart router

Developers who run LLM APIs in production often make an unpleasant discovery: a large share of the requests going to expensive flagship models are elementary tasks that a budget model could have handled. That is what one Habr author found. In a single working week he spent $47 on LLM APIs, even though, by his own estimate, half of his prompts were trivial. After installing ClawRouter, an open source router for LLM requests, he replayed the same week for $1.80. The saving was more than 96%.

The logic behind that figure is simple: not all tasks are equally hard, but without routing every request is billed at the rate of the chosen model, regardless of its real complexity. If you default to Claude Sonnet or GPT-4o for everything, you pay a premium price for answers to trivial questions and simple text transformations.

The cost of LLM APIs becomes more pressing as developers move from experiments to production workloads. At the prototype stage the spending is negligible, but in production it scales in proportion to user activity. A request to GPT-4o costs 20–30 times more than a comparable one to GPT-4o Mini, and for most tasks the difference in answer quality is barely noticeable. The "one prompt, one expensive model" architecture is the most common, even though it is the least optimal.

ClawRouter is an open source proxy server that sits between your application and LLM providers. Every incoming prompt is analyzed on 15 parameters: task complexity, length and structure of the context, need for step-by-step reasoning, code work, output formatting requirements, sensitivity to errors and other signals. Based on this classification, the request is automatically routed to the cheapest model able to handle the task at an acceptable level of quality.

A simple question goes to GPT-4o Mini or Claude Haiku. A complex multi-step request goes to GPT-4o or Claude Sonnet. Tasks that demand highly precise reasoning or delicate code work go to top-tier models.

Integration is minimal: ClawRouter is compatible with the OpenAI API format, so you only need to change the base URL in your application code. No logic changes are needed. OpenAI, Anthropic, Google and several other providers are supported.

On the plus side, routing behaves predictably, and detailed logs explain why a given request went to a particular model, which helps you understand and improve the classification. Rules can be flexibly tuned to a specific project and type of task.

On the limitations side, edge cases are sometimes classified inaccurately: the router underestimates a task's complexity and sends it to a cheaper model, which lowers answer quality. Such cases require manual tuning of the thresholds.

There are several mature alternatives. LiteLLM offers rich multi-provider management, load balancing, fallback logic and detailed analytics, but has a higher barrier to entry. RouteLLM from Lmarena uses a trained classifier built on real data. OpenRouter is a cloud option that requires no self-hosted infrastructure. Each solution makes its own trade-offs between setup complexity, level of control and the cost of the routing layer.

The key takeaway: real workloads are heterogeneous. The request "design the architecture of a distributed system" and the request "fix a typo in this text" need fundamentally different resources, yet without routing both are handled by the same expensive model. Smart routing removes this imbalance automatically, without changes to application logic and without a loss in quality on complex tasks.

For individual developers and small teams spending $50 a month or more on LLM APIs, tools like ClawRouter pay for themselves within the first week. For larger workloads the savings can be even greater.