1 min read
LLM Cost and Latency Notes: using caching where it actually pays off
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
Cost
15 articles
1 min read
Practical AI Performance Tuning: setting latency budgets per user journey
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
LLM Cost and Latency Notes: using caching where it actually pays off
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
Keeping AI Workloads Economical: setting latency budgets per user journey
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
LLM Cost and Latency Notes: reducing token waste without hurting answer quality
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Cost Discipline for LLM Apps: reducing token waste without hurting answer quality
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Fabric Capacity Planning: Lessons from Production
Started with F64 because "enterprise." Spent money we didn't need to. Lesson: Start smaller. F32 was enough. Can always scale up.
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Optimizing AI Cost Per Query
Started at $0.08 per query. Too high for our user volume. Switched simple queries from GPT-4o to GPT-4o-mini.
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Azure OpenAI: The Hidden Costs Nobody Talks About
Let me show you where the costs hide. Simple math, right? Wrong. Every chat conversation includes the entire history. That "helpful" feature where the AI…
1 min read
AI Cost Optimization: Managing LLM Expenses
Strategic cost optimization can reduce AI expenses by 50-80% while maintaining quality.
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Model Routing Patterns: Smart Query Distribution
Smart routing can reduce AI costs by 50-70% while maintaining quality.
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Prompt Caching Strategies: Reducing Latency and Cost
Smart caching can reduce AI costs by 30-50% for applications with repetitive queries.
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Inference Optimization: Making AI Fast and Cost-Effective
Inference optimization is a continuous process. Start with caching and routing for quick wins, then progressively implement more sophisticated techniques.
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Token Optimization Strategies for GPT-4
Output tokens cost 2x input tokens. This changes optimization strategy. With systematic token optimization, you can reduce GPT-4 costs by 50-70% while…
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The Serverless Benefits Realized in 2022
The promise of serverless has largely been fulfilled: Pay only for what you use No server management Automatic scaling Faster time to market Most…