11 Tips to Reduce AI Costs in Software Development

AI budgets hit faster than expected. Token costs compound quietly in the background — until you’re mid-sprint and suddenly hitting limits. This video walks through 11 practical techniques developers can use to reduce AI costs without reducing what AI does for them.

What’s covered:

• The mindset shift: when NOT to use AI — and why that’s good engineering, not a step backward
• How to generate reusable tools instead of re-prompting the same things repeatedly
• Matching model size to task complexity, including sub-agent model configuration in Claude Code
• Why output tokens cost 3–5× more than input tokens, and how to exploit that
• Working incrementally: splitting tasks and clearing context to control token accumulation
• RAG for large codebases: stop loading the whole project, query only what’s relevant
• Agentic workflows: setting stop conditions to prevent token-burning infinite loops
• Caching with CLAUDE.md and static path imports — up to 10× cheaper for repeated content
• Batch APIs for non-time-sensitive processing
• Observability with Langfuse and Phoenix: measure before you optimize

Every token saved is a token you can spend on harder problems.