Cost-smart LLM workbooks are shifting from “max model” to “max workflow.” Embedding-based classification can cut costs, but it isn’t universal. In one project, embedding-based tagging used all-MiniLM-L6-v2 and bge-m3 with OpenAI embeddings and cosine similarity, and it still got out-labeled by simply prompting GPT-4.1 to label. The embed-vs-labeling verdict isn’t one-size-fits-all [1].
Embedding-vs-Labeling Tradeoffs — If your categories stay static, embeddings can shine. But if you add a new category later, you’ll have to recompute everything, which hurts agility [1]. So the choice depends on how fluid your taxonomy is and how you value upfront vs ongoing costs.
Caching, Latency, and Prompts — Long system prompts quietly eat context window, slow throughput, and raise spend [2]. Prompt caching helps cost and sometimes latency, but it doesn’t fix noisy instructions. A sensible target: keep the system prompt to roughly 5–10% of the total window [2].
On-Device and Local-LLM Options — There’s vibrant talk about running LLMs on hardware. People discuss Gemini 2.5 Pro, Deep Think, Qwen, Deep Seek, and tooling like LM Studio on devices such as a MacBook Pro M3 Max, plus models like GLM 4.6 and pathways via Kagi Assistant [3]. The consensus ranges from trying cheaper cloud plans first to supplementing with local LLMs for heavy tasks [3].
Takeaway: cost and latency hinge on workflow design—embed for stability when taxonomy is fixed, cache aggressively, and weigh on-device options when latency and data locality matter [2][3].
References
My trick for getting consistent classification from LLMs
Explores embedding-based classification with LLMs, compares embedding models vs direct LLM labeling, discusses caching, open-set labeling, cost, and practicality insights.
View sourceI wrote a 2025 deep dive on why long system prompts quietly hurt context windows, speed, and cost
Explores how lengthy system prompts shrink context windows, raise latency and cost; discusses KV cache, prefill, caching, and guardrails practices
View sourceGemini 2.5 pro / Deep Think VS local LLM
User compares Gemini 2.5 Pro/Deep Think to local LLMs on MacBook, reviews models like GLM, Qwen, GPT OSS, and costs.
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