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AI Cost Optimization in Production
An intensive technical training for developers and technical decision-makers who want to take control of their AI costs in production. From auditing API usage to building a real-time cost monitoring stack, optimizing prompts, and building a hybrid routing layer with Ollama, you'll leave with concrete strategies to cut your costs in half within a week of returning to work.
Duration
2 days
Level
Intermediate
Price
9.99 EUR/month (all courses included)
Max group
12 participants
What you will learn
+Understand LLM API pricing structure (tokens, models, hidden costs)
+Set up cost monitoring with LangFuse (open-source, self-hostable)
+Reduce token consumption through prompt optimization techniques
+Implement Claude prompt caching to save up to 90% on repeated context
+Build a hybrid local/cloud router with Ollama for simple queries
+Configure budget alerts and automatic cost-control guardrails
Course program
Module 1: AI Cost Anatomy: Understanding What You're Paying For
3h30- LLM pricing models: Claude (Haiku $0.80/M, Sonnet $3/M, Opus $15/M), OpenAI, Mistral
- Build a Python cost calculator: intercept every API call and log input_tokens, output_tokens, model, cost_usd
- Hidden costs: embeddings, function calls, vision tokens (1024×1024 image ≈ 1,700 tokens)
- API usage audit: extract your 10 most expensive endpoints from Anthropic Console logs
- Workshop: identify which 20% of your calls generate 80% of your monthly bill
Module 2: Cost Monitoring Stack with LangFuse
3h30- LangFuse open-source: Docker setup in 10 minutes, Python SDK integration in 20 lines
- Granular tracing: @observe decorator to capture model, tokens, cost, latency, user_id, feature
- Cost dashboard: Cost per feature, Cost per user, Top 10 most expensive requests, daily trend
- Budget alerts: Anthropic Console spend limits + LangFuse webhooks + Slack/PagerDuty notifications
- Workshop: instrument the reference app and trigger a Slack alert on cost threshold breach
Module 3: Prompt Optimization & Claude Prompt Caching
3h30- Token reduction: remove redundant instructions, compress few-shot examples, use documented abbreviations
- Context compression: sliding window, progressive summarization with Haiku, key entity extraction
- Claude prompt caching (Beta): mark static context blocks with cache_control — 90% savings on cached tokens ($0.30/M read vs $3/M fresh for Sonnet)
- Model selection decision tree: Haiku for classification, Sonnet for reasoning, Opus for complex analysis
- Workshop: reduce a reference application's daily cost by 40% using three techniques in combination
Module 4: Hybrid Routing with Ollama & ROI Calculation
3h30- Ollama in production: Llama 3.2 3B (fast classification), Mistral 7B (simple generation), Phi-3 mini (JSON extraction)
- Complexity classifier: lightweight model decides if a request needs a cloud LLM or can run locally (>92% accuracy target)
- Python HybridRouter class: route(), fallback() — auto-escalate to Claude Sonnet on low-confidence local output
- Break-even calculation: GPU cost (AWS A10G $1.006/h) vs API cost — threshold typically at 5,000+ requests/day
- Workshop: build a complete hybrid router that saves 60% on simple queries with <2% quality regression
Ready to get started?
9.99 EUR/month — All courses included, cancel anytime