How much can I save by optimizing my AI API costs after migration?

Most developers save an additional 30-50% on top of their initial migration savings by optimizing token usage, implementing model routing, and caching common requests. A typical $500/month Claude 4 bill that dropped to $150 after migration can often be reduced further to $75-100 with proper optimization.

What is model routing and how does it reduce AI costs?

Model routing means using cheaper models for simple tasks (data extraction, summarization, simple chat) and reserving expensive models for complex reasoning. Instead of sending every request to GPT-5 at $30/1M output tokens, route simple tasks to DeepSeek V4 Pro at $0.87/1M — a 97% cost reduction with no quality loss for those tasks.

Why is my DeepSeek bill higher than expected after migrating from Claude 4?

Common reasons: 1) Rate limit retries adding hidden costs (budget 3-5% extra), 2) Token counting differences between providers inflating usage, 3) Not optimizing prompt lengths — shorter prompts = fewer input tokens, 4) Sending full conversation history instead of summarizing context. Fixing these typically reduces costs 15-25%.

Should I use GPT-5 or DeepSeek for my application after Claude 4 shutdown?

For most applications, DeepSeek V4 Pro ($0.44/$0.87 per 1M tokens) offers 90%+ of GPT-5 quality at 3% of the cost. Use GPT-5 ($10/$30 per 1M tokens) only for complex reasoning, code generation, or tasks requiring maximum accuracy. A hybrid approach saves 80-95% compared to Claude 4 Opus pricing.

How do I implement response caching to reduce AI API costs?

Cache responses for identical or semantically similar inputs. For chatbots, cache common questions (FAQ-style). For code generation, cache frequent patterns. For RAG, cache document embeddings. Effective caching can reduce API calls by 20-40% depending on your traffic patterns. Redis or CDN-level caching works for most use cases.