Reading
A task-type-aware LLM inference routing system that automatically routes requests to local Ollama/VLLM or cloud backup services, achieving optimal balance between cost and performance.
Section 01
K-9 LLM Router is a task-type-aware LLM inference routing system designed to solve the cost-performance balance challenge faced by developers and enterprises in LLM inference. It automatically routes requests to local deployments like Ollama/VLLM or cloud backup services, achieving optimal balance between cost and performance.
Section 02
With the popularization of large language model applications, developers and enterprises face the challenge of balancing cost and performance:
Section 03
K-9 LLM Router is an inference routing middleware compliant with the Swarm API contract specification, located between the application layer and model providers. Its core features include:
Section 04
K-9 LLM Router supports multiple configurable routing strategies:
| Task Type | Recommended Routing | Reason |
|---|---|---|
| Simple Q&A | Local small model | Low cost, fast response |
| Code generation | Local/cloud hybrid | Medium complexity, try local first |
| Complex reasoning | Cloud large model | Requires strong reasoning ability |
| Creative writing | Cloud model | High quality requirements |
| Embedding generation | Local embedding model | Batch processing friendly, low cost |
Prioritize local inference, switch to cloud only when local cannot handle, load is too high, or user specifies cloud.
Prioritize cloud large models, use local only when network is unavailable, API is rate-limited, or data is sensitive.
Dynamically select based on current response time, automatically adapt to network fluctuations.
Section 05
Section 06
K-9 LLM Router represents the direction of LLM application architecture from single model dependency to intelligent routing hybrid architecture, enabling developers to:
Section 07
K-9 LLM Router supports three deployment modes:
Run as an independent process, receive request routing via HTTP API, suitable for microservice architecture.
Deployed on the same host/container as the application, acting as a local proxy, suitable for edge scenarios.
Integrated directly into the application as a Python/Node.js library, suitable for fine-grained control scenarios.
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