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sathishssj3
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cost-latency-optimizer

Reduces LLM costs and latency through caching, model selection, batching, and prompt optimization.

2|Updated Jun 5, 2026
Search Tags:#caching#redis#llm#latency#cost optimization#batching#model selection
One-click install
npx skills add https://github.com/sathishssj3/NexVR-Engine --skill cost-latency-optimizer-sathishssj3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cost-latency-optimizer
Source: https://github.com/sathishssj3/NexVR-Engine/tree/main/.agents/skills/cost-latency-optimizer
Command: npx skills add https://github.com/sathishssj3/NexVR-Engine --skill cost-latency-optimizer-sathishssj3

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? LLM applications often suffer from unpredictable API costs and slow response times, especially at scale. This Skill provides concrete patterns to track spending, identify latency hotspots, and apply proven optimizations like caching and cheaper model routing. ## Core Features & Use Cases - Cost Breakdown Analysis: Track per-call token costs across LLM calls, embeddings, and tool calls with a CostAnalyzer class. - Caching & Model Selection: Cache repeated queries in Redis and route simple queries to cheaper models like gpt-3.5-turbo for up to 60x savings. - Latency Optimization: Batch parallel calls, stream responses for faster time-to-first-byte, and profile hotspots with p95 latency tracking. - Use Case: A chatbot spending $500/month on GPT-4 can implement query caching and complexity-based model routing to cut costs by 50-80% while halving average response latency. ## Quick Start Analyze my LLM application's costs and latency, then recommend caching, model selection, and batching optimizations.

Frequently Asked Questions about cost-latency-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce LLM API costs in production?▼

Reduce LLM API costs by caching repeated queries in Redis, routing simple queries to cheaper models like gpt-3.5-turbo, and trimming unnecessary tokens from prompts. These techniques combined can cut spending by 50-80%.

How to choose between GPT-4 and GPT-3.5-turbo for queries?▼

Choose models based on query complexity: use gpt-3.5-turbo for simple short questions since it is roughly 60x cheaper, and reserve GPT-4 for complex analytical tasks. Classify complexity with heuristics like query length and keywords.

How do I cache LLM responses with Redis?▼

Cache LLM responses by hashing the model name and prompt into a SHA256 key, then storing the response in Redis with a TTL such as one hour. Check the cache before each API call to avoid paying for duplicate queries.

Why is my LLM application response time slow?▼

Slow LLM responses usually come from sequential API calls, large prompts, or waiting for full completions. Batch calls in parallel with asyncio, stream tokens for faster time-to-first-byte, and track p95 latency to find hotspots.

What are the limitations of prompt optimization for cost reduction?▼

Prompt optimization reduces tokens by removing whitespace, examples, and verbose phrasing, but aggressive trimming can degrade output quality. Typical savings are around 30% of tokens, so combine it with caching and model selection for larger gains.

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