How are you managing multi-LLM workflows without blowing your API budget?
Managing multi-LLM workflows is getting painful.
Most developers we talk to run into two main walls:
Skyrocketing API bills from unoptimized prompts and repetitive context payloads.
Latency delays from context-heavy calls across OpenAI, Claude, and DeepSeek.
We recently launched @mwoosh_router to tackle this head-on. Here is what builders actually get out of it:
Up to 43% API Cost Reduction: Automated semantic and partial caching cuts redundant calls before they hit the model provider. • +3.7% Output Quality Boost: Prompt engineering logic optimizes context depth automatically, keeping context loss under 0.03%. • Flat-Rate "Per-Request" Pricing: Predictable, fixed micro-cent pricing per call on supported models—no token math required.
I’d love to learn from the community:
How are you currently routing requests between frontier models?
Are you using custom caching layers or relying purely on provider defaults?
What’s your single biggest headache when orchestrating multiple models today?
Drop your setup below—let’s talk stack efficiency! 👇
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