The LLM Cost Problem
Frontier models like GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are extraordinarily capable — and extraordinarily expensive when used for every single inference. Yet most production systems default to a single model for all tasks. This is the root cause of inflated AI bills.
Strategy 1: Classify Before You Route
The first step is understanding what your prompts actually need. Most workloads break down as:
- Simple tasks (60-70%): summarization, classification, extraction, short Q&A — Llama 3.1 8B handles these perfectly at $0.06/M tokens
- Medium tasks (20-25%): multi-step reasoning, code generation, analysis — GPT-4o Mini at $0.15/M tokens
- Complex tasks (5-15%): legal/medical analysis, complex coding, nuanced generation — GPT-4o at $5/M tokens
Routing intelligently across these tiers yields 70-90% cost reduction on typical workloads.
Strategy 2: Semantic Caching
Many LLM applications process similar or identical prompts repeatedly. Customer support bots, search assistants, and FAQ systems all see high query repetition. Semantic caching stores embeddings of previous prompts and returns cached responses when similarity exceeds a threshold (typically cosine similarity > 0.92).
The economics are compelling: a cached response costs ~$0.0001 to serve vs $0.002–0.05 for a live inference call.
Strategy 3: Request Batching & Prompt Compression
For non-latency-sensitive workloads, batch multiple small requests into a single API call. Combine this with prompt compression techniques — removing redundant instructions and verbose context — to reduce token count by 20-40% before the request even hits the model.
How NeuralRouting Implements All Three
NeuralRouting is a drop-in proxy that sits between your application and any LLM provider. On every request it runs a 5ms classification pass, checks the semantic cache, and routes to the optimal model. All in a single API call that's fully compatible with the OpenAI SDK.